understanding the health of operational personnel … · 2019-11-27 · richard galeano asm master...

UNDERSTANDING THE HEALTH OF

OPERATIONAL PERSONNEL IN AN

AMBULANCE SERVICE: A MIXED

METHODS STUDY

Richard Galeano ASM Master of Public Health

Bachelor of Business (Distinction)

Submitted in fulfilment of the requirements for the degree of

Doctor of Philosophy

School of Public Health and Social Work

Faculty of Health

Queensland University of Technology

2019

3

Keywords

Keywords

Ambulance Absenteeism

Benefit Finding Culture

Diet Emergency Medical Services

Emergency Medical Dispatcher Emergency Medical Technician

Fatalities Fatigue

Health Health Awareness

Health Status Help Seeking Behaviour

Injury Ill Health

Occupational Health and Safety Mental Health

Nutrition Paramedic

Physical Fitness Physical Health

Presenteeism Pre-hospital

Resilience Risk Factors

Safety Satisfaction

Sleep Stress

Spirituality Violence

Workplace Wellness Occupational Health and Safety

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Abstract

Abstract

Prehospital care is an important element of the health care system in Australia.

Those ambulance operational personnel (AOP) who provide frontline services

including paramedics, emergency medical dispatchers (EMDs) and operational

supervisors, are principally shift workers and experience relatively high occupational

injury rates; primarily musculoskeletal (MSK) but also psychological injury. A

systematic review of the literature showed evidence that paramedics are susceptible to

higher rates of injuries, mental health issues, low health status, musculoskeletal

disorders, poor sleep and high rates of fatigue as a result of general and ambulance

specific stressors. EMDs were also shown to have high rates of psychological distress,

obesity and alcohol consumption. Occupational violence was shown to be increasing

whilst shift work and extended work hours were implicated in fatigue, sleep and

depression. Health improvement strategies have previously focused on mental health

issues. The more complex relationships between the nature of the work, organisational

and work environment and the evidence base supporting interventions are not well

understood. Subsequently, these shortcomings have been a catalyst for this research

program. It was hypothesised that the health of AOP is worse than that of the general

population, which is the result of the interplay between the nature of ambulance work,

the working and the organisational environment.

The overarching aim of this research was to explore the health status of AOP

and to develop a conceptual understanding that may inform policy development,

management and future research. The research objectives were:

1. To describe the health status of ambulance operational personnel.

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Understanding the health of operational personnel in an ambulance service: A mixed methods study

2. To identify the individual, organisational and environmental factors that

impact on health status.

3. To develop a conceptual framework for understanding the relationships

between causative factors and health outcomes.

4. To identify strategies that may lead to the improvement of health.

The research question seeks to add to the body of knowledge on AOP health

status, risk factors and how they may be mitigated in the future. This explanatory

mixed methods research included a systematic literature review, a qualitative analysis

of data taken from a survey of 663 AOP and a qualitative analysis of data gathered

from semi-structured interviews of ambulance personnel with a variety of working

backgrounds, to determine those strategies that may lead to improvement in the health

of AOP.

This research developed a more comprehensive understanding of the health of

AOP. In addition, it provided measurable knowledge of the negative influence,

associations and impacts of risk factors, work-related health culture, stressors and

personal interests on work and health status. Additionally, the literature review has

related poor health status and risk factors associated with AOP, organisation of the

work and work culture to the safety of operational personnel. Consequently, the health

status of operational personnel in an ambulance service has been shown to be worse

than the Australian population. This contributes to higher levels of workplace related

injury and illness, chronic disease, obesity, fatigue, ill-health and absenteeism. This is

further evidenced by the outcomes of this research that found, asthma and

cardiovascular disease to be 2.5 times that of the Australian population. In addition,

central adiposity rates were 10% higher than the Australian population and those with

three or more chronic diseases were twice that of the Australian population. This

research has the potential to impact on AOP health and organisational performance

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and subsequently, patient safety and outcomes, if not addressed. Findings from this

research are useful in increasing understanding about the complex link between the

AOP health and work-life balance in a far-reaching context.

Significant differences were found in the thematic analysis of interviews and

showed understanding of the health of ambulance operational personnel (AOP) was

limited to mental health. The associations and modelling presented to interviewees

were regarded with uncertainty. There was a lack of an evidence base associated with

actions to improve that health.

This is the first research that has raised the spectre in such a broad manner, about

the inadequate health of AOP. It has provided further evidence that AOP need an

extended health improvement approach that focuses on all aspects of health, not just

those that are related to mental well-being. Whose responsibility this is remains

unclear, however this research suggests ambulance services need to take a lead role in

improving the organisation of the work and the working and organisational

environment that leads to health improvement of AOP. Careful consideration needs to

be given to how these work-related elements can be mitigated to improve the health of

ambulance operational personnel. Whilst mental health has been shown to be affected

by the work of AOP, this research creates an understanding of the implications for

AOP and ambulance organisations if a holistic approach to health status is not

implemented. As an example, the chain of predictors in Figure 5.3, shows a

complexity in relation to mental health disorders that has not previously been

understood.

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Table of Contents

Table of Contents

Keywords ............................................................................................................................. 3

Abstract ............................................................................................................................... 4

Table of Contents ................................................................................................................ 7

Table of Figures ................................................................................................................. 12

Table of Tables .................................................................................................................. 14

Abbreviations .................................................................................................................... 19

Acknowledgements ........................................................................................................... 21

1 Introduction .......................................................................................................... 22

1.1 Background .............................................................................................................. 22

1.2 Context .............................................................................................................. 23

1.3 Significance, Scope and Definitions ......................................................................... 24

1.4 Aim and Purpose ...................................................................................................... 25

1.5 Hypotheses .............................................................................................................. 26

1.6 Phases of the Research Program ............................................................................. 27

2 Literature Review .................................................................................................. 30

2.1 Introduction ............................................................................................................. 30

2.2 Health Status ........................................................................................................... 34

2.2.1 Occupational Health and Safety ..................................................................... 34

2.2.2 Physical and Mental Health............................................................................ 45

2.2.3 Psychological Stress ....................................................................................... 49

2.2.4 Resilience and Benefit Finding ....................................................................... 61

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Table of Contents

2.3 Measures of Health Status ....................................................................................... 67

2.4 Factors Influencing Health and Wellbeing of AOP .................................................... 73

2.4.1 Individual Factors ............................................................................................ 74

2.4.2 Pre‐Employment testing ................................................................................. 77

2.4.3 Education and Training ................................................................................... 79

2.4.4 The Nature of Ambulance Work ..................................................................... 80

2.4.5 Occupational Violence .................................................................................... 81

2.4.6 The Nature of the Working Environment ....................................................... 84

2.4.7 The Organisational Environment .................................................................... 92

2.5 Health Improvement Strategies ................................................................................ 94

2.6 Conclusion................................................................................................................. 98

3 Research Design .................................................................................................. 101

3.1 Quantitative Methods ............................................................................................ 104

3.1.1 Determining Sample Size .............................................................................. 107

3.1.2 Quantitative Data Input ................................................................................ 108

3.1.3 Quantitative Data Analysis ............................................................................ 110

3.2 Qualitative Methods ............................................................................................... 113

3.2.1 Qualitative Data Collection ........................................................................... 116

3.2.2 Qualitative Data Analysis .............................................................................. 117

3.3 Reliability and Validity of the Survey Instrument ............................................... 120

3.3.1 Validity .......................................................................................................... 120

3.3.2 Reliability ...................................................................................................... 122

3.3.3 Administration of the Survey .................................................................. 124

3.4 Ethical Considerations ............................................................................................ 125

3.5 Conclusion............................................................................................................... 127

4 Results ................................................................................................................ 128

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4.1 Introduction ........................................................................................................... 128

4.2 Stage One – Demographic and Descriptive Analysis ............................................. 130

4.2.1 Health Status of Individual Workers ............................................................ 135

4.2.2 Work Status .................................................................................................. 146

4.2.3 Risk Factors .................................................................................................. 149

4.2.4 Organisational Symptomology ..................................................................... 163

4.2.5 Personal Interests / Caring for Self .............................................................. 174

4.3 Stage Two ‐ Regression Modelling ............................................................... 179

4.3.1 Health Status ................................................................................................ 181

4.3.2 Chronic Disease ............................................................................................ 185

4.3.3 Organisational Symptomology ..................................................................... 190

4.3.4 Caring for Self ............................................................................................... 195

4.3.5 Risk Factors .................................................................................................. 200

4.3.6 Conclusion .................................................................................................... 212

4.4 Stage Three ‐ Thematic Analysis ............................................................................ 213

4.4.1 Step One: Becoming familiar with the data ................................................. 213

4.4.2 Step 2: Generating Codes ............................................................................. 214

4.4.3 Step three: Searching for themes ................................................................ 216

4.4.4 Step 4: Review themes ................................................................................. 217

4.4.5 Step 5: Defining themes ............................................................................... 227

4.5 Conclusion .............................................................................................................. 233

5 Discussion ........................................................................................................... 234

5.1 Introduction ........................................................................................................... 234

5.2 The Health of AOP ................................................................................................. 235

5.2.1 Occupational Health and Safety ................................................................... 237

5.2.2 Chronic Diseases .......................................................................................... 240

5.2.3 Factors Influencing the Health of AOP ......................................................... 252

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Table of Contents

5.2.4 The Working Environment ............................................................................ 265

5.3 Strategies to Improve AOP Health Status ............................................................... 281

5.3.1 Improving the Health and Safety of AOP ...................................................... 284

5.3.2 Improving Shift Work .................................................................................... 288

5.3.3 Worksite Wellness Programs ........................................................................ 290

5.4 A Conceptual Framework of Understanding .......................................................... 296

6 Conclusions ......................................................................................................... 305

6.1 Implications for Policy Makers ................................................................................ 306

6.2 Strengths and Limitations ....................................................................................... 320

6.2.1 Strengths ....................................................................................................... 320

6.2.2 Limitations .................................................................................................... 322

6.3 Recommendations for Future Research .................................................................. 325

6.3.1 Recommendation One .................................................................................. 326

6.3.2 Recommendation Two .................................................................................. 327

6.3.3 Recommendation Three ............................................................................... 327

6.3.4 Recommendation Four ................................................................................. 327

6.3.5 Recommendation Five .................................................................................. 328

6.3.6 Summary ....................................................................................................... 328

7 Bibliography ........................................................................................................ 331

8 Appendices ......................................................................................................... 368

8.1 Appendix A: Ethics Approval, Recruitment Information and Survey ....................... 368

8.1.1 Information Sheet for Participants ............................................................... 372

8.1.2 Ambulance Health Survey (AHS) 2015 .......................................................... 378

8.1.3 Consent Form for the AHS 2015 ................................................................... 380

8.2 Appendix B: Literature Review Synopsis ................................................................. 412

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8.3 Appendix C Associations, Effects and Odds Ratio Tables ....................................... 431

8.4 Appendix D: Regression Plan ................................................................................. 443

8.5 Appendix E: Semi‐Structured Interview ................................................................. 453

8.6 Appendix F: Category of Variables ......................................................................... 458

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Table of Figures

Table of Figures

Figure 2-1. Systematic Review of the Literature .................................................................. 32

Figure 2-2. Overall and LTI Rates (QAS, 2016b) ................................................................ 38

Figure 2-3. Ratio of Near Misses to Injuries from Three Models ........................................ 43

Figure 2-4. Prevalence of Mental Health Disorders in Paramedics ..................................... 52

Figure 2-5. Conceptual Model – Obesity Relationships with AOP ..................................... 59

Figure 2-6. Influencers on Effective Worksite Wellness Programs ..................................... 66

Figure 2-7. Whole of Working Life Approach to Health Surveillance. ............................... 78

Figure 2-8. Occupational Violence Rates in the Ambulance Service. ................................. 82

Figure 3-1. Sequential Explanatory Strategy ...................................................................... 102

Figure 4-1. Age: Respondents vs. Ambulance Service. ..................................................... 130

Figure 4-2. Employee Categories: Respondents vs. Ambulance Service. .......................... 135

Figure 4-3. Asthma: Age and Gender - Ambulance Service vs. Australian Population

..................................................................................................................... 142

Figure 4-4. Respondents who do other Work ..................................................................... 148

Figure 4-5. Sleep Hours: 2003 vs. 2015 ............................................................................. 161

Figure 4-6. Sleep Hours vs. Shift Pattern ........................................................................... 162

Figure 4-7. Reasons for Considering Leaving the Ambulance Service ............................. 164

Figure 4-8. Male Job Satisfaction vs. Age Grouped .......................................................... 167

Figure 4-9. Female Job Satisfaction vs. Age Grouped ....................................................... 167

Figure 4-10. Fatigue Changes Throughout the Shift Cycle. ............................................... 172

Figure 4-11. Diagrammatic Approach to a Structural Connectedness Model .................... 179

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Figure 4-12. Proportion of Categories ............................................................................... 216

Figure 4-13. Initial Thematic Map - Four Draft Themes ................................................... 217

Figure 4-14. Final Thematic Map – Five Themes.............................................................. 232

Figure 5-1. The AOP Fatigue Quandary. ........................................................................... 271

Figure 5-2. The Modified AOP Fatigue Quandary. ........................................................... 272

Figure 5-3. An Interconnectedness Model for Mental Health Disorders ........................... 287

Figure 5-4. Improving Health Related Risk Factors and Safety ........................................ 295

Figure 5-5. A Conceptual Model of Understanding the Health of AOP. ........................... 302

Figure 5-6. A Conceptual Framework for Health Improvement of AOP .......................... 303

Figure 6-1. A six point policy map .................................................................................... 306

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Table of Tables

Table of Tables

Table 1.4-1 Objectives ......................................................................................................... 26

Table 2.2-1 EMS Injury Rates .............................................................................................. 39

Table 2.2-2 Effects of Physical Activity on Depression and Dementia ............................... 48

Table 2.2-3 General and Ambulance Specific Stressors ...................................................... 53

Table 2.2-4 Acute and Chronic Stressors in Ambulance ..................................................... 54

Table 2.4-1 Potential Negative and Positive Effects of Extended Shift Hours .................... 87

Table 2.5-1 Elements and Effectiveness of Workplace Physical Activity Programs

in Relation to Cardio Respiratory Fitness ...................................................... 98

Table 3.1-1 Examples of Different Data/Variable Types Collected .................................. 110

Table 3.1-2 Regression Analysis – Dependent Variables .................................................. 112

Table 3.2-1 Stages of the Thematic Analysis ..................................................................... 118

Table 3.2-2 Strategies used for Enhancing the Credibility of the Qualitative Research

..................................................................................................................... 119

Table 3.3-1 Face Validity Questions .................................................................................. 121

Table 3.3-2 Cronbach’s Alpha and Mean Inter-Item Correlations - AHS 2015 ................ 123

Table 4.2-1 Highest Qualification by Age ........................................................................ 132

Table 4.2-2 Highest Qualification by Employment Type .................................................. 132

Table 4.2-3 Qualifications .................................................................................................. 132

Table 4.2-4 Respondent Location by Station Classification .............................................. 133

Table 4.2-5 Respondent Employment Categories .............................................................. 134

Table 4.2-6 Self-Reported Health: Respondents vs. Other Sources ................................... 136

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Table 4.2-7 Kessler Psychological Distress Score: Respondents vs. Australian

Population .................................................................................................... 137

Table 4.2-8 Leading causes of Ill Health: Australian vs. Respondent Population ............. 140

Table 4.2-9 Arthritis ........................................................................................................... 141

Table 4.2-10 CVD Diagnosis: Age - Respondents vs. Australian Population ................... 144

Table 4.2-11 Diabetes: Age & Gender, Respondents vs. Australian Population ............... 145

Table 4.2-12 Other Diabetes Associations ......................................................................... 145

Table 4.2-13 Experience -Years of Service ....................................................................... 147

Table 4.2-14 Overweightness: Respondents & Other Sources .......................................... 150

Table 4.2-15 Self-Measured Overweightness & Health Status Indicators ......................... 151

Table 4.2-16 Central Adiposity of Respondents ................................................................ 152

Table 4.2-17 Hypertension & Health Status Indicators ..................................................... 154

Table 4.2-18 Exercise Hours - Respondents ...................................................................... 155

Table 4.2-19 Mean Sitting Hours - Respondents ............................................................... 156

Table 4.2-20 Male Respondents with One or More Stressors ........................................... 160

Table 4.2-21 Work Related Health Culture ....................................................................... 165

Table 4.2-22 Job Satisfaction in an Ambulance Service .................................................... 166

Table 4.2-23 Fatigue Experience in the Ambulance Service ............................................. 171

Table 4.2-24 Shift Cycle Fatigue and a Positive Job Satisfaction Score ........................... 173

Table 4.2-25 Why Respondents Don’t Take Rest Breaks? ................................................ 175

Table 4.2-26 Major Barriers to Exercise ............................................................................ 176

Table 4.2-27 Hours Worked .............................................................................................. 178

Table 4.2-28 Hours Worked vs. Age ................................................................................. 178

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Table of Tables

Table 4.3-1 Regression Modelling – Dependant Variables by Category ........................... 180

Table 4.3-2 Predictors of Self-Reported Health ................................................................. 181

Table 4.3-3 Predictors of MHD .......................................................................................... 182

Table 4.3-4 Predictors of Psychological Distress ............................................................... 183

Table 4.3-5 Predictors of Disability ................................................................................... 184

Table 4.3-6 Predictors of Long Term Conditions .............................................................. 185

Table 4.3-7 Predictors of Asthma ....................................................................................... 186

Table 4.3-8 Predictors of Cardiovascular Disease ............................................................. 186

Table 4.3-9 Predictors of Cancer ........................................................................................ 187

Table 4.3-10 Predictors of Diabetes ................................................................................... 188

Table 4.3-11 Predictors of Arthritis ................................................................................... 188

Table 4.3-12 Predictors of Three or more Chronic Diseases ............................................. 189

Table 4.3-13 Predictors of Job Satisfaction ........................................................................ 191

Table 4.3-14 Predictors of Work-Related Health Culture .................................................. 192

Table 4.3-15 Predictors of Thoughts of Leaving ................................................................ 193

Table 4.3-16 Predictors of Rest Breaks .............................................................................. 194

Table 4.3-17 Predictors of Fatigue ..................................................................................... 195

Table 4.3-18 Predictors of Sleep Hours ............................................................................. 196

Table 4.3-19 Predictors of Lack of Time ........................................................................... 198

Table 4.3-20 Predictors of Lack of Energy ........................................................................ 199

Table 4.3-21 Hours Worked ............................................................................................... 199

Table 4.3-22 Predictors of Anxiety .................................................................................... 200

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Table 4.3-23 Predictors of Back Injury .............................................................................. 201

Table 4.3-24 Predictors of Obesity - BMI ......................................................................... 202

Table 4.3-25 Predictors of Central Adiposity – Waist-Hip ................................................ 204

Table 4.3-26 Predictors of Systolic Blood Pressure .......................................................... 204

Table 4.3-27 Predictors of Diastolic Blood Pressure ......................................................... 206

Table 4.3-28 Predictors of Blood Pressure (SBP/DBP) ..................................................... 207

Table 4.3-29 Predictors of Exercise Hours ........................................................................ 208

Table 4.3-30 Predictors of Sedentary Behaviour ............................................................... 209

Table 4.3-31 Predictors of Vegetable Consumption .......................................................... 210

Table 4.3-32 Predictors of Fruit Consumption .................................................................. 211

Table 4.3-33 Age as a Predictor Variable for Dependent Variables .................................. 212

Table 4.4-1 Frequency and Proportion of Categories ........................................................ 214

Table 4.4-2 Coding Tree - Words, Phrases, Categories and Codes ................................... 215

Table 5.2-1 Characteristics of Respondents by Employment Category............................. 236

Table 8.3-1 Station Classification ...................................................................................... 431

Table 8.3-2 Other Associations with Decreasing Self-Reported Health ............................ 431

Table 8.3-3 Mental Health Conditions vs. Job Satisfaction ............................................... 432

Table 8.3-4 Personal and Family Stressors ................................................................... 432

Table 8.3-5 Disability vs. Employment Type .................................................................... 433

Table 8.3-6 Types of Cardiovascular Disease - Respondents ............................................ 433

Table 8.3-7 Self-Reported Overweightness & Health Status Indicators ............................ 434

Table 8.3-8 Fruit & Vegetable Consumption - Respondents ............................................. 434

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Table of Tables

Table 8.3-9 Fruit & Vegetables vs. Health & Works Status Indicators ............................. 435

Table 8.3-10 Alcohol Consumption – Respondents ........................................................... 436

Table 8.3-11 Sleep & Other Statistically Significant Associations .................................... 436

Table 8.3-12 Work-Related Health Culture Responses ..................................................... 437

Table 8.3-13 Job Satisfaction Scale & Responses .............................................................. 438

Table 8.3-14 Job Satisfaction vs. Increased Alcohol Consumption ................................... 439

Table 8.3-15 Job Satisfaction vs. Bodily Pain .................................................................... 439

Table 8.3-16 Job Satisfaction vs. Disability ....................................................................... 440

Table 8.3-17 Regular Rest Breaks and Positive Job Satisfaction Associations ................. 441

Table 8.3-18 Irregular Rest Breaks & Negative Associations............................................ 441

Table 8.3-19 Lack of Time or Energy – Associations ........................................................ 442

Table 8.3-20 Workplace Wellness Programs Associations ................................................ 442

Table 8.6-1 Variables Categorised ..................................................................................... 458

19

Abbreviations

Abbreviations

Amb = Ambulance AP = Australian population AOP = Ambulance operational personnel APSC = Australian Public Service Commission BFRSS = Behavioural Risk Factor Surveillance System BORRTI = The Bell Object Relations & Reality Testing Scale c/s = Cross sectional Č = With CCP = Critical care paramedic CIS = Critical incident stress EHMP = Employee health management programs EMDs = Emergency medical dispatchers EMS = Emergency medical system EMT = Emergency medical technician Env = Environmental FCE = Functional capacity evaluation GP = General population GWP = General working population HADS = Hospital Anxiety and Depression Scale IES = Impact of Events Scale IES-R = Impact of Events Scale-Revised JSQ = Job Strain Questionnaire JSS = Job Stress Survey LEADS = Longitudinal EMT Attributes and Demographics Scale MSK = Musculoskeletal NASS = Norwegian Ambulance Stress Survey NGSE = New General Self-Efficacy Scale PDHS = Police Daily Hassles Scale PDS = Post-Traumatic Diagnostic Scale Pop = Population PTG = Post-traumatic growth PTSD = Post-traumatic stress disorder PTSS = Post-traumatic stress symptomology PWBS = Well-Being Psychological Support Scale QAS = Queensland Ambulance Service QAO = Queensland Audit Office QEAW = Questionnaire on the Experience & Assessment of Work QoL = Quality of life Sig = Significant SIMP = Single Item Measure of Personality SPS = Perceptions of Social Support Scale SSS = Social Support Scale SWY = Shift work years T2DM = Type 2 Diabetes Mellitus WHP = Workplace-based health promotion WMSD = Workplace musculoskeletal disorder YIA = Years in ambulance

20

Acknowledgement

Statement of Original Authorship

The work contained in this thesis has not been previously submitted to meet

requirements for an award at this or any other higher education institution. To the best

of my knowledge and belief, the thesis contains no material previously published or

written by another person except where due reference is made.

Signature: QUT Verified Signature

Date: 29 October 2019

21


Acknowledgements

I would like to extend my appreciation to the following people who have helped

with this demanding journey. Foremost, I would like to express my sincere gratitude

to my Principal Supervisor Professor Gerard FitzGerald, Associate Supervisors,

Emeritus Professor Tony Parker and Doctor Adem Sav for supporting and tolerating

my frequent absences from academia. Their patience, commitment, advice and

enthusiasm have been a constant source of support throughout this long process. This

research would not have been possible without their help and that of 663 ambulance

operational personnel who unselfishly provided me with their health data. The

Queensland Ambulance Service supported this research from the start and continued

to support it with access to ambulance operational personnel data, without which there

would have been no research.

I would also like to acknowledge the support of the KJ McPherson (KJM)

Education and Research Foundation, without which this research would have been

significantly more difficult to conduct. They provided me with a funding grant that

supported all the incidental components of the research.

Finally, I’d like to thank my wife Barbara who has patiently watched, supported

and encouraged me through a lifelong journey of learning, change and continuing

challenge.

Thank you all.

22

Introduction

1 Introduction

1.1 Background

Prehospital care is an important adjunct of the health care system in Australia,

and ambulance services are a subset of prehospital care. Ambulance services include

a large range of professional and support staff. Those operational personnel who

provide this service can be classified in terms of paramedics, emergency medical

dispatchers (EMDs) and supervisors and are primarily shift workers. Paramedics

experience high levels of work related psychological (Bentley, Crawford, Wilkins,

Fernandez, & Studnek, 2013; Pyper & Paterson, 2016; Petrie et al., 2018) and physical

injury (primarily musculoskeletal) (Maguire, B.J., O'Meara, Brightwell, O'Neill, &

Fitzgerald, 2014). These problems are exacerbated by high occupational violence rates

(Maguire, 2018a; Maguire, 2018b), poor sleep patterns (Sofianopoulos, Williams, &

Archer, 2012) and working in a high demand and low control work environment

(Regehr, C. M., D, 2007). EMDs have also been reported to have high levels of obesity

and physical health complaints that may be a result of psychopathology (Lilly, London,

& Mercer, 2016) and post-traumatic stress disorder (Pierce & Lilly, 2012).

Organisational and environmental factors associated with ambulance work have

the potential to adversely affect the health and wellbeing of ambulance operational

personnel (AOP) as a consequence of the combined influence of the nature of the work

involving:

Exposure to risk factors such as assault, infectious diseases and psychologically

distressing human suffering.

Sedentary activities, high speed travel and unpredictable locations.

23


Work scheduling with long 24/7 periods of shift work.

1.2 Context

To meet high ambulance service demand, paramedics often work twenty-four-

hour shift patterns, have an intermittent pattern of work and function in an

unsupervised and highly stressful environment (Asbury et al., 2018). They may need

to deal with patients who are critically ill as well as those with less severe illnesses or

injuries. The circumstances in which they operate include medical, surgical and

traumatic incidents and situations in which individuals are in a high state of distress

and increasingly affected by drugs or alcohol (Maguire, Brian J, O'meara, O'neill, &

Brightwell, 2018). They also work under the intense scrutiny of distressed relatives

and bystanders often in inconvenient and unfamiliar environments. Paramedics may

be subjected to complaints, verbal and physical violence, irregular eating, long shifts

without rest breaks and with little autonomy in the work that they are assigned to

(Crowe et al., 2018).

Increasingly they operate in an environment of growing workload, injuries,

fatigue and high demand for clinical accuracy and an uncontrolled safety environment

(Weaver, Wang, Fairbanks, & Patterson, 2012; Courtney, James A., Francis, & Paxton,

2013; Roberts, Sim, Black, & Smith, 2015). Although paramedic employment is

regarded as physically active, they may also be exposed to periods of sedentary activity

(Coffey, Macphee, Socha, & Fischer, 2016).

EMDs are shift workers who receive calls for assistance, make critical decisions

about the level of care required and despatch appropriate resources. They can be

overweight, experience alcohol abuse, post-traumatic stress disorder (PTSD) and

depression (Pierce & Lilly, 2012; Lilly et al., 2016). Additionally, they experience

24

Introduction

high call volumes and can be exposed to verbal abuse and distressed callers (Coxon et

al., 2016).

1.3 Significance, Scope and Definitions

The research that focuses on paramedicine has been developing exponentially

over the last 12 years in conjunction with the development and professionalism of

AOP. In addition, paramedics now require university clinical qualifications (Brooks,

I. A., Cooke, Spencer, & Archer, 2016; Brooks, I. A., Grantham, Spencer, & Archer,

2018). However, research that focuses on the health-related issues for operational and

support staff within ambulance services has remained minimal and, in some instances,

non-existent. For example, supervisors and managers in ambulance services were little

discussed within the literature, even though they are a critical element in providing

ambulance services. Supervisors and managers can have multiple functions that

encompass clinical, operational, personnel support and managerial work.

This research is critically important for the health of AOP and the performance

of ambulance organisations. It is the only known research that is a substantial review

of AOP (paramedics, EMDs, and supervisor/managers) and combines issues of health

status, the relationship to the organisational and working environment, work and

personal factors and presenting results to ambulance service representatives seeking

opinion and advice on improving that health. This research will link evidence from the

literature, AOP and ambulance representatives (through semi-structured interviews

and a thematic analysis) to develop a conceptual framework of understanding and a

theoretical framework for health improvement and future research on this subject. The

health of operational personnel must be considered in the context of mental, physical

and psychosocial health and lead to ambulance services in Australia acting to improve

25


the health of AOP. It will show that the well-being of AOP is no longer about mental

health, which has for so long been related to the work of AOP, with little understanding

of the actual associations and impacts. Whilst causes will not been determined by this

research, the mental health of AOP will be related to their physical health, the

organisation of the work and the working and organisational environment. This

research will show there is little evidence for current policies that have the health of

AOP as their priority.

Definitions

LTIFR - is the number of lost time injuries in a given period per million hours

worked. A lost-time injury is an incident that results in a fatality, permanent

disability or time lost from work which may be as little as one day or shift.

Overweightness - BMI ≥ 25 ≤ 30.

Obesity - BMI > 30.

Central adiposity - Waist-Hip ♂ > .91, ♀ > .81.

1.4 Aim and Purpose

The goal of this research was to investigate the health status of AOP and the

influence of demographic, organisational and environmental factors on the health

outcomes, across the three operational groups. These include paramedics, EMDs and

supervisor/managers. The objectives of this research are described in Table 1.4-1.

26

Introduction

Table 1.4-1 Objectives

Detailed Objectives

To describe the health status of ambulance operational personnel.

To identify the individual, organisational and environmental factors that impact

on health status.

To develop a conceptual framework for understanding the relationships between

causative factors and health outcomes.

To identify strategies that may lead to the improvement of health.

This research also seeks to add to the body of knowledge on AOP health status,

risk factors and how that may be mitigated in the future. Within this aim, the research

seeks to answer the following questions:

What are the characteristics of the physical and mental health of ambulance

operational personnel?

What are the individual and work-related factors that influence this health?

What strategies may lead to the improvement of health?

1.5 Hypotheses

Primary: The health of operational personnel in an ambulance service is worse than

that of the general population.

Secondary: This poor health is the result of the interplay between the nature of

ambulance work, the working and the organisational environment.

27


1.6 Phases of the Research Program

These will be addressed in the following chapters of the thesis:

2. Literature review: This was a systematic review of the literature which informed

the current physical and mental health status of paramedics and to a lesser extent,

(as there was limited literature) EMDs. Apart from the impact leadership style

(Ghorbanian, Bahadori, & Nejati, 2012) has on the health of paramedics, there was

no evidence in relation to supervisor/managers. In addition, the literature review

provided the basis for understanding the individual and work-related factors that

influence health, and variable evidence on strategies that may improve the health

of this sub-population.

3. Research design: Chapter three described the development of a mixed methods

study, including a quantitative/qualitative design and the development of a survey

tool (The Ambulance Health Survey 2015 [AHS 2015]) designed to provide

information on the health status of different categories of AOP. The AHS 2015

was also designed with recognition of the multiple factors influencing health status

and their relationship and impact on short and longer-term health outcomes to

inform the qualitative component of the research. The research design included a

methodology for statistical analysis, determining face and content validity of the

AHS 2015, ethical considerations, a semi-structured interview approach to sharing

these results and the thematic analysis of these conversations.

4. Results: This was described in three stages.

Stage One: The AHS 2015 was released in April 2015 and returns closed in

August 2015. Returns totalled 663 and provided an overview of the demographics,

health, work and risk factors, personal interests and caring for self. Multiple

28

Introduction

important associations were discovered between organisational, individual and

environment factors and how they impacted on the health of operational personnel

in the three categories.

Stage Two: Effect size and contextual inclusion of independent variables using

ordinal and binary logistic regression analysis was used to develop multiple

predictive models.

Stage Three: Nine semi-structured interviews were conducted and transcribed

in 2017. The semi-structured interviews were difficult to schedule based on the

availability of the participants and the work commitments of the researcher who

was working in remote locations for seven months of 2017. These interviews were

analysed using a thematic approach to assist in developing health and wellbeing

proposals that would inform policy development. The thematic analysis was

conducted in early 2018.

5. Discussion: This chapter integrates the findings of AHS 2015 and the outcomes

of key stakeholder interviews with the current evidence. The context explores how

the findings of this research may extend the grasp of these issues and inform the

development of a conceptual framework of understanding and a model for health

improvement. It seeks to identify similarities between these findings and what was

previously known, identify new knowledge, theoretical models and explore

options for practical application.

6. Conclusions: This chapter discussed the implications of the research for policy

makers in relation to the health improvement of AOP in an ambulance service.

Strengths and limitations were outlined, and five recommendations were made in

consideration of future research.

29


7. Bibliography: This is an alphabetical list of all references.

8. Appendices: These include:

a. Appendix A: Ethical Approval and AHS 2015 Survey Documentation.

b. Appendix B: Overview of the Literature Review.

c. Appendix C: Associations, Effects and Odds Ratio Tables.

d. Appendix D: Regression Plan.

e. Appendix E: Ethical Approval and Semi-Structured Interview

Documents.

f. Appendix F: Variable Categorisation.

30

Literature review

2 Literature Review

2.1 Introduction

Chapter two explores what is known about the health status of AOP and the

influence of organisational, environmental, personal, lifestyle factors, education and

training in developing and sustaining a healthy workforce able to match the physical

and mental demands of the work. This research was conducted at a time of significant

change in each of these domains and increasing demand for emergency services

globally and in Australia, along with changes in population demographics with an

ageing population and longer life expectancy. These changes, together with the

introduction of new technologies and therapeutic practices contribute to new and

emerging challenges in providing cost effective ambulance services. This will require

a new and comprehensive approach to identification of potential health hazards and

risks and systems for organisations and individuals to ensure effective risk control

solutions.

Efficacious prehospital care systems provide immediate medical care as well as

responsive and effective access to ongoing care and are thus a critical component of

the continuum of modern health care systems. Effective ambulance services require

paramedics, EMDs and operational leaders to communicate effectively with each other

within regulatory and organisational systems and subject to quality improvement

strategies. The unpredictable nature of the work, increasing demand for services and a

challenging clinical environment presents a need for those delivering ambulance

services to have the physiological and psychological capacity to match these demands

(Pyper & Paterson, 2016; Varker et al., 2017; Petrie, Milligan-Saville, et al., 2018).

31


Therefore, an organisational structure which recognises the need for effective health

and safety systems and procedures to develop and sustain a healthy workforce is

essential in achieving this goal.

The aim of this review was to examine the literature and provide evidence-based

information necessary to evaluate the health status of AOP and identify the

organisational, environmental, personal and educational factors that influence AOP

health and wellbeing. The specific objectives of this review were:

To identify AOP health priorities and their impact on ambulance services.

To identify the current knowledge in relation to the health status of AOP and

prioritise the health issues.

To identify the health risk factors, opportunities and strategies for effective

organisational and individual risk control solutions in the ambulance service

context.

A broad range of electronic databases were searched including: EBSCOhost,

Library Press, ProQuest dissertations, Theses Global, Science Direct, Web of Science,

PubMed, Google Scholar, Cochrane Library, Informit, Safe Work Australia, Australian

Bureau of Statistics, Australian Institute of Health, National Electronic Injury Surveillance

System (NEISS) in the United States (U.S.), Trove and QUT eprints using key word

criteria such as ‘paramedic’. Each of the key words were cross referenced against ‘other

search words’ such as ‘fatigue’ and secondary key words, such as ‘nurses’, were used as

necessary. Key words are described on page three of this thesis.

A modified ‘Preferred Reporting Items for Systematic Reviews and Meta-Analysis’

(PRISMA) (Moher et al., 2009) methodology was used to identify a total of 1078

references. All papers, reports and records were examined, and in some instances, new

32

Literature review

terms were discovered that required the search to be re-run. For instance, in the Australian

context the term ‘Paramedic’ was used, but in the United States (U.S.) context the term

‘Emergency Medical Technician’ or ‘Paramedic’ can be used. Older papers that had

limitations in the currency and quality of the evidence were considered and, in some

instances, included. For instance, (Boreham, Gamble, Wallace, Cran, & Stevens, 1994)

was one of five papers, and the oldest, that were found on the health status of paramedics.

This process is described in Figure 2.1.

Figure 2-1. Systematic Review of the Literature

33


An evidence map of the major papers was developed to identify gaps in the

knowledge base for AOP health and was categorised relative to research design,

demographics, methods and key outcomes (Appendix B). Only two of the studies were

randomised control trials and these had no relationship with prehospital care. The

remainder were cross sectional (n = 22), longitudinal (n = 59) and case studies,

prospective surveys, retrospective reviews of data sets, reports, data sets, theses, meta-

analyses and systematic reviews. When an area of interest had limited relevant

literature on ambulance, this discussion was drawn from other like professions or those

who work in similar environments (e.g. fire fighters and police). The primary likeness

for this inclusion emanates from being a first responder in an emergency. Additionally,

those who do have a direct clinical care responsibility and work shift work in a patient

care environment were also included (e.g. nurses). General literature on an issue (e.g.

shift work), was also considered to bring context to the review.

Three dominant themes emerge from the analysis of the literature: 1)

descriptions of the health status of AOP, 2) identification and analysis of the factors

that influence the health status and 3) identification of remedial strategies. The

following is structured around these three themes.

34

Literature review

2.2 Health Status

2.2.1 Occupational Health and Safety

The Occupational Health and Safety (OHS) risks associated with the conduct of

ambulance services are likely to exceed those of most other professions (Maguire, B.J.

et al., 2014). Paramedics are exposed to some of the most traumatic experiences and

work in uncontrollable and unpredictable environments, involving public places and

people’s homes (Roberts et al., 2015). They provide services on a 24-hour basis,

leading to disrupted patterns of sleep, eating and limited positive social interactions.

They are also reported to have worse health than the general population (Studnek, J.

R., Bentley, et al., 2010).

Fatalities

In the U.S., fatalities occur regularly, and ambulance personnel have a higher

rate of fatal accidents and a higher standardised mortality than the general population

and other health occupations (Sterud et al., 2006; Maguire, Brian J. & Smith, 2013).

For example, between 1992 and 1997, 114 fatalities occurred in the US representing a

fatality rate of 12.7 per 100,000 EMS workers. This was 2.5 times the national average

for all workers (Maguire, B. J., Hunting, Smith, & Levick, 2002; Reichard, Marsh, &

Moore, 2011). An examination of the U.S. National Electronic Injury Surveillance

System from 2003 through to 2007 identified 65 ambulance employee fatalities, most

of which were transport related accidents (45% motor vehicle, 31% aircraft) (Reichard

et al, 2011). This was a fatality rate of 7.2 fatalities per 100,000 paramedics which was

1.4 times the national average (Reichard et al., 2011).

In contrast to the U.S. experience, a review of paramedic fatalities in Australia

during the period 2000 - 2010 (Maguire, B.J. et al., 2014), indicated a fatality rate of

35


approximately six times the national average; with an average of one paramedic killed

every two years in a transport related accident.

Injuries

In addition to fatalities, paramedics also have higher injury rates than the general

population or other health professions. A retrospective examination of occupational

health records from 1998 through to July 2002, of two urban EMS agencies in the U.S.

(Maguire, B. J., Hunting, Guidotti, & Smith, 2005) identified the relative risk for EMS

workers of a work-related injury as 1.5 compared to the general population. The injury

and lost time injury (LTI) rates were 34.6/100 and 19.6/100 full time equivalent (FTE)

workers respectively, with 57% of injuries resulting in lost workdays. These injury

rates were the highest reported by the Department of Labour for any industry in the

U.S.

An analysis of lost time injury data from the Longitudinal Emergency Medical

Technician Attributes and Demographics (LEADS) study in the U.S. (Studnek, J. R.,

Ferketich, & Crawford, 2007), indicated a lost time injury rate of 8.1/100 FTE for EMS

workers. These results may be underreported due to different data collection methods

(incident records versus self-reported data), small sample sizes, non-representative

samples and lack of comparison with matched samples (Sterud et al., 2006). In an

analysis of 1,295 workers compensation claims from public safety providers in one

urban population in the U.S. over 29 months, 36% were from EMS providers who had

the highest rates of lost time and medical evaluations in comparison to police and

firefighters (Suyama, Rittenberger, Patterson, & Hostler, 2009).

Despite these findings, the source of the data provides confusing and

occasionally contradictory findings. A review of data from the U.S. National

Electronic Injury Surveillance System – Occupational Supplement (NEISS-Work), a

36

Literature review

work based data collection, found injury rates amongst EMS workers of 4.9/100;

significantly lower than the lost time injury rate of 34.6/100 described by Reichard &

Jackson, (2010) and Maguire, Brian J. & Smith, (2013). This difference reflects that

only 34% of all worker injuries were treated in emergency departments (ED) where

the NEISS-Work data was collected (Reichard & Jackson, 2010). Thus, reliance on

routine work based injury data may underestimate the actual injury rate, as injured

paramedics do not necessarily go to an emergency department (Reichard, Marsh,

Tonozzi, Konda, & Gormley, 2017). Non-reporting is a recognised problem. Rates

from an on-line survey of EMS personnel in the U.S. (Heick, 2009) were very different

from those derived from analysis of U.S. national data (Cutter & Jordan, 2004). Cutter

and Jordan (2004) reported that of all respondents, 29% experienced an injury in the

previous year and 64% reported multiple injuries; with only 32.4% of the 145

respondents not reporting an injury.

Injury rates

Injury rates also vary by gender. In a review of work-related musculoskeletal

disorders in Swedish ambulance personnel (Aasa, Barnekow-Bergkvist, Angquist, &

Brulin, 2005), females reported a higher incidence of lower back disorders (30%) than

males (10%). It was proposed that this higher incidence of musculoskeletal injuries in

females was related to ambulance vehicles being designed for males, and insufficient

arm muscle strength in females to match the physical demands of work tasks (Aasa,

Barnekow-Bergkvist, et al., 2005; Fairbanks, Caplan, Bishop, Marks, & Shah, 2007).

These differences in injury rates by gender may also relate to underreporting by men,

as masculinity has been shown to negatively impact mental health and acts as a barrier

to health seeking behaviour (Hoy, 2012).

37


In an analysis of workers compensation claims from nurses and ambulance officers

in all states of Australia (Gray & Collie, 2016) from 2008 through to 2014, it was reported

that ambulance officers have an accepted claim rate of 17.2/100 workers. This claim rate

was seven times that of all other workers, while the nurse accepted claim rate (2.5/100

workers) was the same as all other workers. Data obtained from the ambulance service

shows a similar pattern. Overall injury rates were 27.1/100 workers with females recording

higher injury rates in all but one age group (16-24) (QAS, 2016b). The overall lost time

injury rate was 9.7/100 workers with females scoring higher rates of lost time injuries in

all age groups and overall (10.8 for females vs. 8.8 for males). Higher reporting rates

amongst females were disproportionate to the overall male/female employee ratio (63.7

vs 32.3%) (QAS, 2016). For paramedics, the most common body regions for injuries are

the back 37% followed by the shoulder 9%, abdomen and pelvic area 4%, wrist 5% each,

knees 6%, and ankles 3%. Overall injury rates and lost time injury rates for the ambulance

service (QAS, 2016b) are shown in Figure 2.2. The peak for female injury rates occurs in

the 45-54 age group and may be related to a number of factors such as body composition

(obesity and muscle mass) and bone mineral density (Tirosh et al., 2015; Lloyd et al.,

2016). This increased rate for females compared to males needs to be investigated further,

as male injury rates decline as ambulance workers age.

38

Literature review

Abbreviations: IR = Injury rate, LTI = Lost time injury rate

Figure 2-2. Overall and LTI Rates (QAS, 2016b)

Reported injury rates for EMS workers from several studies and public documents

are summarised in Table 4.2-1 (Aasa, Barnekow-Bergkvist, Angquist, & Brulin, 2005;

Maguire, B. J., Hunting, Guidotti, & Smith, 2005; Reichard & Jackson, 2010; Studnek,

Jonathan R., Crawford, Wilkins, & Pennell, 2010; Reichard, Marsh, Tonozzi, Konda, &

Gormley, 2017). The types of injury reported by AOP were also variable and the self-

reported and derived data discussion focuses more on physical injuries as these were more

overt and measurable. However, there was also considerable concern for psychological

injury derived from the nature of ambulance work. Additionally, there was growing

evidence to indicate an association between physical and mental health (Correll et al.,

2017; McMahon et al., 2017; Stubbs et al., 2017).

0

10

20

30

40

50

60

16‐24 25‐34 35‐44 45‐54 55‐64 Total

Rate/100FTE

Age in years grouped

Male IR

Females IR

Overall IR

Male LTI

Female LTI

Overall LTI

39


Table 2.2-1 EMS Injury Rates

Outcome variable Injury rate/100

FTE

LTIFR Location Potential Causation

Factors

Overall1 34 97.8 U.S. Not reported

Overall2 253 39.8 Aus. Not reported

Average4 N/R 7 Aus. Not reported

Back5 30 Fem/10 Male U.S. Psychological demands

Worry about work

Awkward postures

Heavy lifting

Neck/Shoulder6 33 Fem/7 Male U.S.

Lower back or leg

pain7

Overall 50

31 Fem/59 Male

U.S. JS

Prior back problems

Poor/fair health

Back8 18 U.S. JS

Poor/fair health

≥ 30 years

Overall9 4.9 U.S. Physical demands

LTI rate10 8.1 U.S. call volume

Urban

Hx. of back

ED treated IR11 8.6 U.S. Body motion

Harmful substances

Injury rate12 8.0 Aus. Muscular stress

Abbreviations: JS = Job satisfaction, Hx = history, N/R = not recorded, Inj. = injury, Fem = female, ↑= increase, U.S. = United States, Aus = Australia, ≥ = greater than & equal to, IR = injury rate, LTI = lost time injury.

1 (Maguire, B. J. et al., 2005) 2 Work Cover Qld 2013 3 This injury rate is calculated using the QAS Injury and Incident surveillance system (LTI & non-LTI). This includes all personnel in the QAS as at April 2013. It is estimated that if the Injury rate was calculated for operational personnel only it would be approximately 29/100. 4 SWA 5 (Aasa, Barnekow-Bergkvist, et al., 2005) 6 (Aasa, Barnekow-Bergkvist, et al., 2005) 7 (Aasa, Barnekow-Bergkvist, et al., 2005) 8 (Studnek, J. R., Bentley, et al., 2010) 9 (Reichard & Jackson, 2010) 10 (Studnek, J. R. et al., 2007) 11 (Reichard et al., 2017) 12 (Maguire, B.J. et al., 2014)

40

Literature review

Work related musculoskeletal disorders

In a retrospective analysis of patients with chronic lower back pain (CLBP)

matched by age, gender and location compared to others without CLPB; those with

CLPB had a higher co-morbidity including depression (13.0% vs. 6.1%), anxiety

(8.0% vs. 3.4%) and sleep disorders (10% vs.3.4%) (Gore, Sadosky, Stacey, Tai, &

Leslie, 2012). Findings from an analysis of both male and female ambulance officers

in Sweden indicated that psychological issues were associated with neck/shoulder and

lower back complaints of females (Aasa, Brulin, Angquist, & Barnekow-Bergkvist,

2005). In males, all musculoskeletal disorders were significantly associated with

psychological issues and lack of social support (Aasa, Barnekow-Bergkvist, Angquist,

& Brulin, 2005). This is a very important issue and data was sought in the Ambulance

Health Survey 2015 (AHS 2015) in relation to back injuries, psychological issues,

mental health disorders and sleep that could substantiate these results. It adds

momentum to the theory that a reactive OHS system is inadequate for AOP.

Work-related musculoskeletal disorders for paramedics have been identified in

Melbourne, Australia as the costliest OHS problem. In a study of six organisations

including one ambulance service and two hospitals, using a Physical Hazards Scale

and the Work Assessment Organisation Questionnaire, psychosocial hazards were

identified as a significant contributor to work-related musculoskeletal injuries

(WRMI) (Oakman & Chan, 2015). This was supported by another study in Victoria,

Australia of workers’ compensation claims from 2003 to 2012 amongst paramedics

and nurses. Paramedics were identified as having an incidence of lower back injuries

and mental health disorders 13 times greater than nurses. Although this research did

not link musculoskeletal injuries to mental health, it did describe the incidence of

musculoskeletal injuries and mental health disorders as higher than any other health

care workers (Roberts et al., 2015).

41


Injuries and the working environment

It is well established that the work environment influences the rate and pattern

of injuries (Dembe, Erickson, Delbos, & Banks, 2005; Crowe et al., 2018). However,

the type of injury and the causes of those injuries differ in a national context.

According to Cutter and Jordan (2004), physical assaults were the most common

reported injury in health care professions in the U.S. at 12.9%. However, Reichard and

Jackson (2010) described the physical demands of emergency responders (police, fire

and ambulance) in the U.S. as the leading cause of injuries in this group of workers. A

telephone survey of previously injured EMS workers in the U.S. identified lifting,

carrying or transferring as the most common causes of an injury (Reichard et al., 2017).

In an examination of work-related injuries of EMS workers in Turkey, motor vehicle

accidents (31.9%), needle stick injuries (16.0%), ocular exposure to bodily fluids

(15.4%) and other sharp injuries (9.8%) were identified as the common sources of

injury (Yilmaz, Serinken, Dal, Yaylacı, and Karcioglu, 2016).

Transportation related injuries remain common and were often associated with

emergency driving conditions. In a retrospective study of ambulance crashes over 11

years, using data from the U.S. National Health and Transport Safety System (NHTSS)

(1987 to 1997), Kahn, Pirrallo, and Kuhn (2001) described the rate of injuries for every

100,000 miles driven as nearly 15 times higher for ambulances driving under lights

and sirens than those without lights and sirens. Despite no benefit to patient outcomes

being shown, there is continued use of a lights and sirens protocol in ambulance

services throughout the world, causing risks to workers and patients (Murray, B. &

Kue, 2017). In two Australian studies, almost half of paramedic respondents admitted

to falling asleep whilst driving (Sofianopoulos et al., 2012) and 10% feeling

dangerously sleepy (Archer, 2012). It was also reported by paramedics that seat belts

42

Literature review

were used approximately 50% of the time in the rear of ambulance vehicles in the U.S.

(Johnson, Lindholm, & Dowd, 2006).

The patterns and rates of fatalities and injuries underestimate the extent of the

problem and subsequently, analysis of incidents that may not result in injury could

provide a more comprehensive overview of the risk profile. Heinrich, Petersen, Roos,

Brown, and Hazlett (1980) described the accident ratio triangle as a relationship

between near misses and injury accidents. In addition, a survey of 6,700 safety

professionals by Bird and Germain (2004), produced a more detailed version of this

relationship triangle. Data was retrieved from the ambulance service OHS database

from 1 July 2012 until 16 April 2013. The relationships between (Heinrich, Petersen,

Roos, Brown, & Hazlett, 1980; Bird, F. & Germain, 2004) and ambulance service data

are described in Figure 2.3. The data has been adjusted so that each hierarchy has a

common denominator of 300 near misses. However, a problem in comparing these

relationships is the definition of what is a minor and major injury. Consequently, there

may be some variances because of the researcher’s understanding of this issue. There

were zero fatalities in 2012-13 in the ambulance service. Figure 2.3 describes an

increased risk of equipment damage and injuries in an ambulance service compared to

previous modelling (Heinrich et al., 1980; Bird, F. & Germain, 2004).

43


Figure 2-3. Ratio of Near Misses to Injuries from Three Models

The causes of fatalities, injuries and near-miss incidents are complex and no one

model can be universally applicable (Reason, J, Hollnagel, & Paries, 2006). Whilst

never intended as a model for accident analysis, the Swiss Cheese Model (SCM)

proposes five potential causes of workplace accidents (Reason, James, 1990). These

include poor line management defences and decisions, psychological precursors of

unsafe acts, unsafe acts and inadequate defence (Reason, James, 1990). The causes of

injury-accidents may be different from those that involve fatalities. Similarly, the

‘injury-accidents’ may differ from ‘near misses’. A comprehensive approach to

analysis of causation should also include analysis of near miss incidents as it is not

known which incidents have the potential to become injury or even fatality accidents

(Anderson, M. & Denkl, 2010; Reason, James, 2016). This is a critical issue, as the

collection of near miss incidents is haphazard in an ambulance service especially in a

busy urban environment. However, it remains to be seen how any organisation can

encourage more reporting of near miss incidents whilst the influence of safety culture

is not understood (Goh, Y. M., 2017; Layne, Nemeth, Mueller, & Martin, 2019).

44

Literature review

Influence of safety culture

This section shows a more complex interaction of safety culture, assistance, near

miss reporting and organisational support. Focussing on near miss incidents allows an

organisation to adopt a more proactive approach to identifying potential weaknesses

in the safety management systems before an injury occurs (Patterson, Huang, et al.,

2010; Gnoni & Saleh, 2017). However, an analysis of causation in all incidents

including ‘near misses’ depends on a culture of safety within the organisation.

Safety culture includes beliefs, values and attitudes to the importance of safety

within a workplace (Queensland, 2013). It is sometimes known as ‘the way we do

things around here’ and is implicit in all aspects of safety, including health (Kirwan,

Reader, & Parand, 2018). In a cross-sectional survey to determine the association

between EMS workplace safety culture and provider or patient safety outcomes,

Weaver et al. (2012) showed 11 to 32% of occupational injuries and accidents in the

Emergency Medical Services were not reported. The Emergency Medical Services

Safety Attitudes Questionnaire (EMS-SAQ) was administered across 61 agencies in

North America and found the OHS culture varied from service to service, from

position to position and with the levels of demand (Patterson, Huang, et al., 2010).

Additionally, Guldenmund (2007) reported that the value of OHS in the

organisation was largely determined at the organisational level. That is, if the leaders

exhibit safe and healthy behaviours and attitudes at an organisational and personal

level, this behaviour will encourage common boundaries between workers and

management. However, safety does depend on the daily experiences of the worker as

was shown by Weaver et al. (2012), who demonstrated EMS workers experience fewer

injuries when organisations take health and safety seriously. In addition, when the

vision of safety was unreliable or not sustained by leaders, the OHS climate was

45


regarded as being ineffective (AbuAlRub, Gharaibeh, & Bashayreh, 2012; Chew

Abdullah, 2013).

OHS in the ambulance service has always been considered of improving safety

culture and in a reactive sense of assisting AOP after they were injured. This creates

doubt about the efficacy of the reactive approach to OHS and suggests a more complex

interaction that may predispose AOP to injuries and is shown in the next section to

have a relationship with mental and physical health. In addition, it suggests a more

proactive approach is needed, that includes preemployment testing, psychological

interventions, worker wellbeing and leadership improvements (Leitao Alexandre,

2015; Bayram, Ünğan, & Ardıç, 2017)

2.2.2 Physical and Mental Health

Despite considerable anecdotal speculation as to the likely impact of the nature

and context of ambulance work on mental and physical health and wellbeing, there

was limited evidence to support this perception. Any such impacts were likely to be

socially, culturally and organisationally determined and thus, difficult to generalise.

For example, a cross sectional study of Danish ambulance personnel and fire-fighters

(Hansen, Rasmussen, Kyed, Nielsen, & Andersen, 2012), identified paramedics as

having half the prevalence of poor self-rated health of the general population (5% vs.

10%) but a higher proportion (42% vs. 29%) of musculoskeletal pain. These results

are consistent with a Norwegian study on ambulance personnel where depression and

anxiety were described as less than the general population (Sterud, Hem, Ekeberg, &

Lau, 2008a).

46

Literature review

Other occupations

There is evidence of an association between physical and mental health drawn

from other occupational environments (Goh, J., Pfeffer, Zenios, & Rajpal, 2015;

Pohling, Buruck, Jungbauer, & Leiter, 2016). In a review of the association between

mental disorders and subsequent chronic health conditions across 17 countries, mental

disorders were associated with an increased risk of a wide range of physical and

chronic conditions including heart disease, stroke, cancer, diabetes, hypertension,

asthma, chronic obstructive pulmonary disease (COPD), peptic ulcers, arthritis and

chronic neck or back pain (Scott, Lim, Al-Hamzawi, & et al., 2016). Early onset of

CVD (Scott et al., 2013), metabolic syndrome (Mitchell et al., 2011) and reduced life

expectancy (Wahlbeck, Westman, Nordentoft, Gissler, & Laursen, 2011) have also

been associated with mental health disorders. Whilst there are no known studies on the

relationship between mental and physical health in AOP, a cross sectional survey of

Lithuanian nurses (n = 748) provided some insight into the health of other health care

professionals. Low self-reported health was associated with mental distress, low

physical activity and a low sense of coherence (it may not work out as well as

expected) (Malinauskiene, Leisyte, Romualdas, & Kirtiklyte, 2011). These outcomes

have relevance for AOP, given the reported high rates of psychological distress and

mental health disorders in other emergency service workers (Petrie et al., 2018).

Factors that contribute to poor physical and mental health

In a systematic review of factors that contribute to depression in the general

population, two protective factors (physical activity and quality of social interactions)

were reported (Pemberton & Tyszkiewicz, 2016). In addition, increased social

networks and relationships have been hypothesised as improving physical health

(Cohen, S. & Janicki-Deverts, 2009). Conversely, negative associations with social

47


relationships, including social loss and loneliness, have been shown to have

detrimental effects on physical health (Stroebe, Schut, & Stroebe, 2007). Some of the

key pathways to improving social relationships included achieving life goals, feelings

of control, optimism, purpose, trust and self-esteem (Cohen, S., 2004; Cohen, S. &

Lemay, 2007; Cohen, S. & Janicki-Deverts, 2009). Exercise and social relationships

have relevance for the nature of ambulance work, due to the reported low rates of

physical activity in AOP (Courtney, J. A., 2010; Courtney, J. A. et al., 2013) and the

known affects on social interactions in shift workers. Shariat, Bahri Mohd Tamrin,

Daneshjoo, and Sadeghi (2015) in a systematic review of the literature on the effect of

physical activity in the general population (Dinas, Koutedakis, and Flouris, 2011)

reported beneficial outcomes as effective as that of anti-depressant medications.

Analysis of data from the National Comorbidity Study in the U.S. (Goodwin, 2003),

showed an inverse relationship between the level of physical exercise and depression

(OR = 0.75) and anxiety disorders (OR = 0.64 – 0.78). Additionally, a longitudinal

study of British public servants showed that regular physical exercise was associated

with a reduced likelihood of depressive symptoms (Silva et al., 2012). There was also

evidence indicating that low to moderate and high levels of physical activity were

significantly associated with a protective effect for cognitive decline in patients with

dementia (Sofi et al., 2011). Consequently, it is not unreasonable to theorise that

increased levels of physical exercise in AOP would improve cognitive, physical and

mental health.

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Literature review

Mood adjustment has also been linked to physical activity which can enhance

positive moods with higher levels of interest, excitement, enthusiasm, and alertness

when compared to people with low levels of physical activity (Pasco et al., 2011).

Regular low intensity aerobic exercise, 30 - 35 minutes, 3 - 5 days per week for 10 -

12 weeks, resulted in moderate increases of positive activated affect, which is

substantially energisation and pleasant moods (Reed, J. & Buck, 2009). Whilst

physical activity can improve cognitive functioning throughout life, being sedentary

(sitting whilst eating, working, during transport, watching television) has implications

for health outcomes such as all cause cardiovascular mortality, obesity, metabolic

disorders, cancer and psychosocial problems (Spruit, Assink, van Vugt, van der Put,

& Stams, 2016). The associations described above, between exercise and mental

health, are shown in Table 2.2-2.

Table 2.2-2 Effects of Physical Activity on Depression and Dementia

Disease Mechanism Exercise Type Depression ↑ self-efficacy Aerobic Improved locus of control Resistance ↓Anxiety ↑ self-esteem ↑ social engagement ↓ medication ↓ obesity & ↑ image Dementia ↑ cerebral blood flow Aerobic ↑ neurotropic factors ↑ neuron generation

Adapted from Fiatarone Singh (2012)

Physical exercise

Physical exercise and reduced sedentary behaviour are also associated with

improved quality of life (Kolt et al., 2017). In a randomised controlled trial of 430

postmenopausal women randomised to a non-exercise group (n = 92) and three

exercise groups, Martin, Church, Thompson, Earnest, and Blair (2009) concluded that

49


higher doses of exercise were associated with greater improvements in all aspects of

mental and physical quality of life (QoL). As research continues into the benefits of

exercise prescription for those with different chronic diseases e.g. heart failure, cancer

and multiple sclerosis, it has also been recognised that the ‘one size fits all approach’

may be deficient (Tsiga, Panagopoulou, & Niakas, 2015). Subsequently, this

prescription should vary with the level of comorbidity and exercise capacity, which

needs to be assessed and delivered by an accredited exercise physiologist (Gillam,

2015) via a referral from a general or specialist medical practitioner, who may not be

sufficiently trained to prescribe the right level of exercise. Support and encouragement

to exercise may be a useful approach to improve AOP health. However, this focus on

one area of an issue (such as physical exercise), may improving functioning, but

potentially ignores the factors (e.g. mental health, regular rest breaks, fatigue and

sleep) that lead to a reduction in physical exercise by AOP. The issue of physical

exercise for paramedics is a complex issue that requires consideration of location,

individual needs and the availability of appropriate support (Hunter, MacQuarrie,

Sheridan, High, & Waite, 2019).

2.2.3 Psychological Stress

Psychological stress is often cited as a significant cause of poor health and

wellbeing amongst ambulance personnel (Asbury et al., 2018; Varker et al., 2018).

However, despite the nature of ambulance work, there was variable evidence related

to the rate and nature of psychological stress and its impact on health status. Whilst

depression and anxiety are often linked when there was an associated physical event,

such as acute coronary syndrome (Huffman, Celano, & Januzzi, 2010), there was no

evidence that this relationship exists in AOP. On the other hand, Courtney, J. A. (2010)

showed that fatigue had the greatest link with mental health and sleep in both

50

Literature review

metropolitan and rural paramedics in Victoria, Australia. This difficult and complex

relationship between stressors, health and wellbeing is explored further in the

following sections.

Mental health and psychological distress

An online survey of paramedics in a United Kingdom (U.K.) ambulance service

using the Post-Traumatic Diagnostic Scale showed that 22% of paramedics were

assessed as having PTSD, and 10 and 22% had clinical levels of depression and anxiety

respectively. The prevalence of PTSD in males (23%) was higher than females (15%)

(Bennett, Williams, Page, Hood, & Woollard, 2004) which is considerably higher than

the prevalence 4.4% (12 months) and 7.2% (lifetime) in the Australian population

(Cooper, Metcalf, & Phelps, 2014). In Australian workers the rate of PTSD is 11%

(SWA, 2013). These rates can vary up to 50% prevalence after exposure to

interpersonal trauma (Cooper et al., 2014). A study of 86 Canadian paramedics using

demographic questions, the Social Provision Scale (levels of social support) and the

Beck Depression Inventory, demonstrated that 25.5% of workers were in the high

range of post-traumatic symptoms and 8.1% were suffering moderate to high levels of

depression (Regehr, C., Goldberg, Glancy, & Knott, 2002). In a study of Polish

paramedics using the Impact of Events Scale-Revised, the rate of PTSD was found to

be 40% (females 64.3% and males 36.1%) (Rybojad, Aftyka, Baran, & Rzońca, 2016).

A systematic review of the global literature on EMS and other related populations e.g.

police and firefighters, concerning stress related occupational risk factors such as

alcohol and drug use, PTSD, types of stressors, social support, personal resources and

social characteristics, identified rates of PTSD as high as 20% in these emergency

service occupations (Donnelly, E. & Siebert, 2009). Additionally, a systematic review

into the mental health impact of first responders in Australia (including paramedics)

51


suggested ambulance personnel may be at high risk, and estimated prevalence rates of

11% for PTSD, 15% for depression, 15% for anxiety and 27% for psychological

distress (Petrie et al., 2018). Figure 2.2-3 describes the known prevalence of mental

health disorders in paramedics from a number of sources. While the rates of mental

health disorders vary, especially with PTSD there may be some anecdotal evidence as

to why. The ambulance service in this study has one of the most comprehensive

psychological support programs in the world (Scully, 2011; Shakespeare-Finch, J. E.,

Wehr, Kaiplinger, & Daley, 2014) and the developers of this program have travelled

extensively, helping national and international ambulance services introduce these

programs. Recent changes in this ambulance service may have helped in reducing the

development of PTSD by focusing on post traumatic growth rather than a reactive

approach once AOP develop symptoms of PTSD (Shakespeare-Finch, J. E. et al.,

2014; Shakespeare-Finch, J., Rees, & Armstrong, 2015; Guerrero, 2017; Shakespeare-

Finch, J. & Daley, 2017; Varker et al., 2017). In terms of AOP, stress reactions are

often perceived as related to exposure to critical incident stress. However, the evidence

suggests a more complex causation, including organisational and contextual issues.

For instance the high rates of PTSD exhibited in Figure 2-4 may be due to the lack of

training in managing ambulance stressors, poor hazard and risk controls, selecting the

right employee who may have greater resilience or the lack of support services that are

staffed by appropriately trained and understanding peers and professional mental

health workers (Shakespeare-Finch, J. E. et al., 2014; Shakespeare-Finch, J. et al.,

2015; Shakespeare-Finch, J. & Daley, 2017; Varker et al., 2017).

52

Literature review

Abbreviations: QAS = Queensland Ambulance service; UK = United Kingdom; USA = United States of America; Aus GP = Australia general population; Neth = Netherlands; Ger = Germany; Pol = Poland. Adapted from: (Baumeister & Härter, 2007), QAS EAP database, (Bennett et al., 2004; ABS, 2009; Rybojad et al., 2016), (Kessler et al., 1994), SR = systematic review, (Petrie et al., 2018).

Figure 2-4. Prevalence of Mental Health Disorders in Paramedics

Ambulance related stressors

The results of an on-line survey using the Post Traumatic Disorder Checklist, the

EMS Chronic Stress Scales and the Critical Incident Stress Inventory for a Canadian

EMS service, identified stress being associated with the operational and organisational

elements experienced by ambulance personnel (Donnelly, E. A., Bradford, Davis,

Hedges, & Klingel, 2016). In a study of Norwegian ambulance personnel (n = 1180)

(Sterud, Hem, Lau, & Ekeberg, 2011), a broad range of survey tools were used to

identify the most common elements associated with mental stress. These stressors

included work demands, lack of control over work and poor support from managers,

(Sterud, Hem, Ekeberg, & Lau, 2008b).

0

5

10

15

20

25

30

35

40

45

Depression PTSD Anxiety

Prevalence of Mental Health Disorders

Aus GP QAS UK Ger Neth Pol U.S U.S2 U.S. SR

53


Table 2.2-3 General and Ambulance Specific Stressors

General Organisational Stressors Ambulance Specific Stressors

Inadequate salary Hide feelings towards patients and relatives

Lack of opportunity for advancement

Insufficient personnel

Not being able to express opinions to

patients or relatives

Inadequate support by supervisor High job demands

Lack of recognition for good work Low job control

Fellow workers not doing their job Medical responsibility

Lack of participation in policy decisions Constant dealing with chronically ill people

Dealing with crisis situations Uncertainty about the scene

Negative attitude towards the organisation Incidents involving children

Personal insult from customer/colleague Incident with friend or relatives

Difficulty getting along with supervisor Occupational violence

Poorly motivated co-workers Heavy and awkward lifting

Noisy work area Working under difficult conditions

Frequent interruptions Working in bent or twisted conditions

Inadequate or poor-quality equipment Driving under difficult conditions

Excessive paperwork Shift work

Assignment of disagreeable duties Involuntary overtime

Meeting deadlines Lack of down time/rest breaks

Conflict with other departments Social isolation/desychronisation

Poor or inadequate decisions

Competition for advancement

Critical on-the-spot decisions

Assignment of increased responsibility

Assignment of new or unfamiliar duties

Covering work for another employee

Frequent changes & boring/demanding

activities

Working overtime

Periods of inactivity

Performing tasks not in job description

Insufficient personnel time

Collated from (van der Ploeg & Kleber, 2003; Sterud et al., 2008b; Arial, Gonik, Wild, & Danuser, 2010; Sterud et al., 2011)

The results indicated that job satisfaction was predicted by general work-related

stressors and health complaints were predicted by both general and ambulance specific

54

Literature review

stressors. Arial, Wild, Benoit, Chouaniere, and Danuser (2011), in a survey of 333

ambulance service personnel from French speaking Switzerland related poor mental

health to over commitment and how well supervisors value their employee’s work.

These general and ambulance specific stressors are detailed in Table 2.2-3.

Table 2.2-4 Acute and Chronic Stressors in Ambulance

Acute Chronic

Dead children Operational

Medical emergencies Shift work

Severe accidents or injuries Risk of injury or illness

Acts of violence or threats Managing social life outside of work

Lack of support post a critical incident Poor nutrition

False or misleading information Fatigue

Suicide attempts Lack of understanding from family/friends

about work

Making friends outside the job

Negative comments from the public

Feeling like you were always on the job

Friends/family feel the effects of the stigma

associated with your job

Organisational

Feeling like different rules apply to different

people

Feeling like you always must prove yourself

to the organisation

Constant changes in policy/procedures

Poor communication

Staff shortages

Bureaucratic red tape

Lack of training on new equipment

Dealing with supervisors

Lack of resources

Leaders overemphasis the negatives

Collated from (van der Ploeg & Kleber, 2003; Sterud et al., 2008b; Sterud et al., 2011)

A longitudinal study of ambulance personnel in the Netherlands indicated that

acute and chronic workplace stressors and risk factors such as the social aspects of the

55


work environment, lack of support from supervisors and poor communication were

inversely related to long term employment in ambulance services (van der Ploeg &

Kleber, 2003). These stressors are described in Table 2.2-4.

Interviews with 14 spouses of paramedics on the effect of trauma on the partners

of paramedics (Regehr, C, 2005), found that chronic stressors may be exacerbated by

feelings of worth, as paramedics are highly trusted by the community at large13, but

with a low occupational status (“regarded as drivers”). A comparative qualitative study

of a United Kingdom (U.K.) ambulance service and Ambulance Service of New South

Wales (ASNSW) (Mahony, 2001), found that the intrinsic stressors paramedics

experience by working in these services were exacerbated or moderated by

organisational and contextual influences. Both the U.K. and ASNSW paramedic

participants reported ‘problems with management’ as the main stressor. The

overarching stressor was lack of control and the way in which work was organised.

Additionally, a government sponsored assessment of the ASNSW (Parker, R. et al.,

2008) consisting of an analysis of submissions from all levels of ambulance personnel

concluded that chronic stressors may have more to do with the way paramedics are

treated rather than the emergency work. Similarly, the findings of a survey of 125

Australian paramedics (Kirby, Shakespeare-Finch, & Palk, 2011), using the Post-

Traumatic Growth Inventory, the revised Impacts of Events and COPE scale to

examine the effectiveness of paramedic coping strategies, indicated that not all

strategies achieved positive outcomes and suggested the need for more effective

training and support to improve psychological well-being. This was supported by

13 Paramedics in Australia are consistently voted the most trusted profession by the Readers Digest

annual ‘Most Trusted Professions’ survey

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Literature review

Murray, J. (2016), who proposed psychological stress may be related to: 1) high

demand, 2) in the case of a lower workload, not getting enough work to become expert

in the field, 3) low levels of clinical autonomy, and 4) high organisational control in

Australian ambulance workplaces.

Stressor relationships

On the other hand, a Polish study of 100 paramedics (Rybojad et al., 2016), using

the authors own questionnaire and the revised Impact of Events Scale, found that apart

from the stressors associated with being exposed to acutely ill and injured people

(including children), there was no correlation with any workplace-based stressors to

PTSD. In addition, there was a consistent theme on stress in ambulance services and

its negative relationship with organisational support. For instance, PTSD was more

common in paramedics where there were no early and supportive interventions from

the employer (Rybojad et al., 2016). Emotional stability and perceptions of

organisational support were also shown to be inversely related to well-being in U.K.

paramedics (Soh, Zarola, Palaiou, & Furnham, 2016), and organisational and critical

incident stressors were significant predictors of PTSD (Donnelly, E. A. et al., 2016).

Additionally, poor mental health has been reported in situations where there was a

limited recognition and reward for work effort and contribution in ambulance services

(Arial et al., 2011).

This complex relationship between stressors was also influenced by the inherent

characteristics of the individual and their resilience. A study of ambulance personnel

(n = 490) in the U.K., identified job satisfaction, decreased stress and work

engagement were elements that affected the individual’s well-being and emotional

stability (Soh et al., 2016). Kukowski, King, and DeLongis (2016) used the Maslach

Burnout Inventory to describe low perceived esteem being associated with poor mental

57


health in Canadian paramedics, while self-reported PTSD was associated with lower

than average sleep quality and sense of personal accomplishment at work. In addition,

Donnelly, E. and Siebert (2009) in an earlier study of Canadian paramedics indicated

critical incident stressors, were exacerbated by shift work and linked to PTSD and that

work-related stressors (critical, operational and organisational) were highly correlated

to PTSD and high-risk alcohol and drug use. A study of Australian healthcare workers

[non–ambulance, (n = 125) and ambulance personnel, (n = 93) (84% paramedics)],

(Jimmieson, Tucker, and Walsh 2016), identified high levels of demands (emotional,

time and cognitive). These three demands were related to exacerbating stress and it

was shown that reducing one helped neutralise the effects of the remaining demands.

Emergency Medical Dispatcher’s stressors

Less well known were the relationships between EMDs and psychological stress

and whether this stress was like that of paramedics. Rates of PTSD for EMDs have not

been reported. However, the risk of PTSD in EMDs was reported as related to their

work (Pierce & Lilly, 2012; Lilly et al., 2016). EMDs are the first workers to hear of

an incident from often distressed or abusive callers and provide medical and

psychological support until paramedics arrive (Dunford, 2002). In a study using semi-

structured interviews, 25% of EMDs (n = 9) in a U.K. based emergency operations

centre felt that they were being overloaded and undervalued (Coxon et al., 2016). A

study of 60 EMDs in the Queensland Ambulance Service (QAS) reported lack of social

support and shift work were predictors of PTSD (Shakespeare-Finch, Rees, &

Armstrong, 2015). Most, when they think of social support, think of receiving support,

however, Shakespeare-Finch and Obst (2011) described the importance of social

support in terms of giving and receiving.

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Literature review

A New Zealand review and analysis of 253 AOP including EMDs (10%)

indicated higher levels of work-related psychological well-being than police (n = 229)

or fire fighters (n = 241) (Brough, 2005a). Work-family conflict, neuroticism and job

satisfaction predicted work well-being in all three services for all types of workers.

Cortisol biomarkers of psychological distress (Hellhammer, Wüst, & Kudielka, 2009)

have been shown to be significantly higher in EMDs (n = 8), during day shifts when

compared with cortisol samples taken during their leisure time (Weibel, Gabrion,

Aussedat, & Kreutz, 2003).

Overweightness and stressors

Stress is known to have a relationship with several risk factors for poor physical

and mental health. In a meta-analysis of obesity and health related quality of life,

“Class III obese” and “overweight” individuals had a reduced mental quality of life

(Ul-Haq, Mackay, Fenwick, & Pell, 2013). Whereas, a longitudinal study of 6,755

individuals in the U.K. civil service followed from 1989 to 2009 (The Whitehall II

study), showed work related stress in this group of civil servants was associated with

Type 2 Diabetes in females and obese males (Heraclides, Chandola, Witte, & Brunner,

2012).

Whilst diet and exercise has been the mainstay of treating and preventing

obesity, it is thought other factors may play a role. For instance, increased BMI may

be related to hormonal activity. It has been suggested that acute stress will inhibit

eating (Klatzkin, Baldassaro, & Rashid, 2019), however as a person starts to recover

from this stress reaction, eating sensations are promoted, leading to increased food

intake. In chronic stress, high levels of hormones are constant and for a greater period

of time resulting in increased food and high calorie food intake (Foss & Dyrstad,

2011). A laboratory study of women of normal weight through to those who were

59


obese (n = 457), reported that increased levels of chronic and perceived stress were

associated with eating non-nutrious food, a greater drive to eat, including binge eating,

increased feelings of hunger and more ineffective attempts to control eating (Groesz

et al., 2012). Evidence is mounting of increased cortisol levels being a key player in

the obesity epidemic (van Rossum, 2017). (Hegg-Deloye et al., 2013) concluded that

obesity is prevalent amongst emergency workers and given the relationship between

stress and AOP (Bentley, Crawford, Wilkins, Fernandez, & Studnek, 2013; Rice,

Glass, Ogle, & Parsian, 2014), a conceptual model of obesity in ambulance is

presented in Figure 2.5.

↑ stress

↑ appetite

Unhealthy lifestyle

↑CVD and metabolic syndrome

↑ obesity

↑Chronic cortisol levels

Incudes: shift work, fatigue,

poor sleep, acute & chronic stress, poor diet, alcohol

Adapted from (van Rossum, 2017)

Figure 2-5. Conceptual Model – Obesity Relationships with AOP

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Literature review

Critical incident stress and sleep

Sterud et al. (2008a) reported AOP with depressive or anxiety symptoms

demonstrated a prevalence of disturbed sleep 3.4 times greater than the general

population in Norway. Hilton and Whiteford (2010), in a study of 60,556 participants

in Australia (this included health workers, however it was unknown if any AOP were

included in this research), reported higher psychological distress was associated with

higher rates of workplace accidents and failures. In interviews with 27 participants

including paramedics and EMDs, critical incident stress was shown to have an impact

on the health and well-being of paramedics and EMDs through sleep difficulties, angry

outbursts, irrationality and feelings of alienation (Gallagher & McGilloway, 2007).

These impacts don’t happen to all AOP, as the majority deal with the issues in a

cognitive and technical sense whilst maintaining some emotional distance (Regehr, C.,

Goldberg, & Hughes, 2002). However, this research also raised some concern with the

true nature of this protective strategy. Biological indicators of stress (heart rate and

salivary cortisol) and subjective measures of anxiety were measured in a video

simulation of policing. It was found that the biological response revealed previous

trauma with a lack of social support led to distress. It was hypothesised this can lead

to emotional detachment which can be reflected in interpersonal relationships (Regehr,

C., LeBlanc, Jelley, Barath, & Daciuk, 2007). Whealin and Ruzek (2008), P. 101, in a

discussion on Cognitive Behavioural Therapies summarised this issue with the

following comment: “Thus, exposure to work-related trauma continues to place a

proportion of employees at risk for stress related problems that can become chronic

mental health disorders and impair performance of key life roles”.

61


2.2.4 Resilience and Benefit Finding

Impact of an individual’s personality characteristics

In a meta-analytic review of 87 cross sectional studies, Helgeson, Reynolds, &

Tomich (2006) described benefit finding (positive psychological growth) being related

to less depression and greater well-being. Seventy eight percent of respondent

paramedics reported normal levels of stress (Likert scale of 1-5, with one expressing

the respondents understanding of normal stress and five representing the respondents

view of extremely severe stress) existed in rural and regional ambulance services and

it was thought that working within smaller communities with local support may

provide some type of protective effect.

Individuals with high self-efficacy (a belief that one is able to exercise some

control over one’s situation) and who were more likely to reduce the impact of stressful

occasions on their quality of life may help ambulance personnel cope with stress (Prati,

Pietrantoni, & Cicognani, 2010; Kirby, Shakespeare-Finch, & Palk, 2011; Scully,

2011). Similarly, Prati et al. (2010) and Scully (2011) suggested specific training and

support services for AOP may reduce stress and build resilience by improving the

psychosocial skills of AOP.

Shakespeare-Finch, Gow, and Smith (2005) reviewed personality and coping

variables and found: 1) extraversion, 2) openness, 3) agreeability, 4)

conscientiousness, and 5) coping levels were all associated with perceptions of growth

and that intervention strategies may be more effective if they were tailored to differing

personalities. Similarly, LeBlanc et al. (2011) indicated anxiety and physiological

stress responses to clinical scenarios were related to coping styles of individuals and

these coping strategies were potentially modifiable. In a Ugandan study, Pietrantoni

62

Literature review

and Prati (2008) suggested resilience (defined as self and collective efficacy and a

sense of community) following critical events was common among first responders,

which included EMS personnel. It was suggested that this resilience was protective of

first responder work related mental health.

Murray, J. (2016) indicated that many paramedics in Queensland develop

psychologically (post-traumatic growth) because of their experiences and that this

happens for several reasons, such as: 1) self-awareness, 2) education about stress, and

3) supportive and psychologically safe work environments. This was supported with

feedback from the ambulance service’s employee assistance program (EAP) where

PTSD rates (< 1%), depression rates (5.5%) and anxiety rates (6.9%) were lower than

the general population in Australia14. It should be noted that these are diagnosed mental

health disorders and there may be AOP who do not disclose their mental health issues

with their employer. It does not take into account measured psychological distress nor

those who feel depressed, as an example, but continue on with no mental health

support. Lack of resilience may lead to increased burnout which affects work-life

balance and quality of life and in turn may lead to increased turnover and sickness

absence. Subsequently, workers exhibited sign of survivor bias with personal, work

related, or patient related burnout and were more likely to leave emergency medical

services (EMS) in the U.S. (Crowe, 2016). In an Iranian study of paramedics, burnout

was adversely affected by younger age, single status, tobacco smoking, lower income,

longer work experience, longer shifts and the work status of the spouse. The latter may

relate solely to the Iranian culture as it has not been reported elsewhere (Khatiban,

Hosseini, Bikmoradi, Roshanaei, & Karampourian, 2015).

14 These figures were supplied by the Manager, QAS EAP in an interview on the 13 November 2016.

63


Organisational design and individual post traumatic growth

In a systematic review of the evidence for post traumatic growth in protective

services personnel (including ambulance), Paton (2005) examined issues that can be

influenced by organisational practices such as training, personality extraversion,

traumatic events, organisational difficulties and concluded that identifying the

mechanism by which post traumatic growth can be assured, the basis for a prevention

strategy can be developed. According to Gayton and Lovell (2012), paramedics

employed by the ambulance service with one to five plus years of experience exhibit

greater levels of resilience than first and second year undergraduate paramedical

students at university, and this resilience was significantly correlated with general

health and well-being. Clompus and Albarran (2016), in a qualitative study of a U.K.

ambulance service (n = 7), found that formal methods such as management, debriefing

and referral and informal methods such as peer support, family support, friends and

use of humour are used to manage emotions. The former is the type of prevention

strategy described by Murray, J. (2016) with a program for all personnel in an

ambulance service, on stress, resilience and growth. Whilst EAPs have been

traditionally based on an intervention model, relatively recent investigations and

subsequent understanding (Shakespeare-Finch, 2007; Kirby et al., 2011; Gayton &

Lovell, 2012; Williams et al., 2012) have seen a paradigm shift in regards prevention

and resilience building. Essential to well-being is physical health (Hernandez et al.,

2018), however none of the prevention strategies in the five references in this

paragraph, include a component aimed at improving physical health or linking mental

and physical health.

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Literature review

Health seeking behaviour

There was only one study found on health seeking behaviour in ambulance

personnel. Sterud et al. (2008a) compared the results of a survey of Norwegian

ambulance officers (n = 1,180) with the results of nation-wide population survey (n =

31,987). Ambulance officers contacted their General Practitioner (GP) significantly

less than the reference population (males 62 vs. 54% and females 80 vs 65%). Whilst

disturbed sleep was significantly related to more help seeking from a GP (Sterud, Hem,

Ekeberg, & Lau, 2008), it was the only factor for ambulance officers that were related

to seeking help from a psychologist or psychiatrist. There are many issues to be

considered in voluntary health seeking behaviour15 of an AOP when visiting a GP. The

GP may not know that the individual works in a high-risk occupation for exposure to

trauma. For example, Galeano (1996) determined that paramedics visiting their local

doctor routinely did not have their blood pressure checked. This is important, as

hypertension was more common in shift workers (Kales, Tsismenakis, Zhang, &

Soteriades, 2009; Tucker, Marqui, Folkard, Ansiau, & Esquirol, 2012).

Factors that influence help seeking behaviour have been described as gender,

marital status and education, with females being much more likely to seek help

(Thompson et al., 2016). In addition, 90% of mental health disorders were described

as being treated by Primary Care Physicians (PCPs)/General Practitioners (GP’s)

(Sterud et al., 2008a; Doherty & Kartalova-O'Doherty, 2010). Scott, Kokaua, and

Baxter (2011), argued that having a chronic medical (OR = 1.58) or pain (OR = 2.03)

15 Voluntary health seeking behaviour is whether the individual seeks the help or recognises the need to

seek help but does not.

65


condition increased the likelihood of seeking help from a GP for a mental health

problem.

In contrast, a retrospective review of active duty members, post deployment, in

the U.S. Air Force (n = 200) reported that as self-reported mental health symptoms

increased, the rate of access to mental health services decreased (Visco, 2009). Barriers

to accessing mental health services were postulated as public and self-stigma (feeling

weak or cowardly), career concerns and their perceptions towards mental health

professionals. It was described in Robinson (2004) that those who needed the mental

health services the most, felt the greatest stigma. Men have a reluctance to seek help

and this reluctance can often be based on ‘traditional masculine behaviour’ (Galdas,

Cheater, & Marshall, 2005; Jarrett, Bellamy, & Adeyemi, 2007). Smith, Braunack-

Mayer, Wittert, and Warin (2008) identified four factors that shape help-seeking in

men such as: 1) time available to monitor health, 2) previous illness experiences, 3)

capacity to maintain everyday activities and tasks, and 4) perception of the severity of

health concerns. Thus, an important component of this research will be assessing the

factors that shape help-seeking behaviour in AOP.

Health risk awareness

It is hypothesised that help seeking behaviour is part of health risk awareness

and that health risk appraisals are an integral component of improving health seeking

behaviour and awareness. Rula and Hobgood (2010) demonstrated that awareness of

health risk improved when participants were exposed to multiple health risk appraisals.

Additionally, Chih-Wen, Hagen, Bender, Shoemaker, and Edington (2009) determined

that taking a health risk assessment at least once was associated with a positive change

in health status. Additionally, the “Working Well Trial” illustrated a model of

66

Literature review

organisational determinants of effective worksite health promotion programs,

(Weiner, Lewis, & Linnan, 2009) which is described in Figure 2.6.

Adapted from (Weiner et al., 2009)

Figure 2-6. Influencers on Effective Worksite Wellness Programs

Conclusion

Whilst there is mounting evidence of the impact stress has on performance and

quality of life in AOP, it remains important that these relationships be explored further,

especially relative to physical quality of life. No dedicated validated surveys that

looked at stress in EMS were found during the literature search. There is a need for

further tools to be adapted to EMS with specific emphasis on AOP stress. The

development of a theoretical knowledge base as a guide in implementing a health

improvement program will be a critical element in advancing the health of AOP.

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2.3 Measures of Health Status

Against this backdrop of variable evidence on the status of AOP’s health, it is

challenging to measure health status. Additionally, this research identified only five

studies that reviewed the health status of paramedics (Boreham, Gamble, Wallace,

Cran, & Stevens, 1994; Sterud et al., 2006; Studnek, J. R., Bentley, et al., 2010; Pék

et al., 2013; OWHC, 2014). Whilst there are other studies that review health, these

studies will often review only one component, such as mental health (Pyper &

Paterson, 2016) or back injuries (Broniecki, Monica, Esterman, & Grantham, 2012).

Mostly, health status may be implied through surrogate measures including

absenteeism, presenteeism and job satisfaction.

Absenteeism

In a dissertation presented to the University of Tennessee (Rogerson, 2005), it

was reported that Tennessee emergency responders who had poor to fair self-reported

health status had the most absenteeism. A Canadian study in a municipal ambulance

service (n = 280) determined a link, for the first time, between communication

practices, organisational commitment and job satisfaction in paramedics, of which

absenteeism was one component (Jules & Bourque, 2009). Using the Male Role Norms

Inventory-Revised, a Danish study of ambulance personnel (n = 2,426) reported an

inverse relationship between increasing traditional male masculinity ideals and

sickness absence, and a direct relationship with sickness presence (Hansen, Lund, &

Labriola, 2011).

Tobacco smoking has also been implicated in absenteeism. Halpern, Shikiar,

Rentz, and Khan (2001) concluded that smokers had significantly greater absenteeism

than ‘never smokers’ and ‘former smokers’ showed a significant decline in

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absenteeism in the years following tobacco smoking cessation. In addition, a

systematic review of tobacco smoking and absenteeism in the workplace, Weng, Ali,

and Leonardi-Bee (2013) concluded that tobacco smokers had a 33% increase in the

risk of absenteeism against never smokers and a 19% increase in this risk as against

previous smokers.

Chronic conditions have also been associated with absenteeism. In a study of

7,797 respondents from 12,397 Dow employees (63% of the complete workforce)

Collins et al. (2005) examined allergies, arthritis or joint pain, asthma, back or neck

disorders, breathing disorders, depression, anxiety or emotional disorder, diabetes,

heart and circulatory problems, migraine and chronic headaches and stomach or bowel

disorders. Absenteeism by chronic condition was between 0.9 to 5.9 hours in a four-

week period and work impairment ranged from 17.8% to 36.4% decreased ability to

perform at work. For all chronic conditions, work impairment and absence cost were

estimated to be 10.7% of total labour costs for the Dow Company (Collins et al., 2005).

Work performance

Given the type of work carried out by AOP and the relationship between physical

activity and mental health, it would be a reasonable assumption that work performance

was improved by healthy paramedics. It was reported by Pronk et al. (2004) in a survey

of non-ambulance employees in the U.S. that improved levels of physical activity were

related to improvements in the quality of the work performed and overall job

performance. Improved cardiorespiratory fitness resulted in an increase in the quantity

of work performed and a reduction in the amount of effort used to do that work.

Obesity was associated with not getting along with co-workers and severe obesity was

related to a higher number of absent days from work. This relationship between work

performance and obesity was supported by a number of other studies (Hertz, 2004;

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Schmier, 2006; Jans, van den Heuvel, Hildebrandt, & Bongers, 2007), where it was

concluded that obese employees were absent for more days.

Presenteeism

Presenteeism, which describes those who are present for work when sick, has

also been associated with poor mental and physical health (Cooper & Dewe, 2008). It

has been regarded as having a greater cost to employers than absenteeism (Gosselin,

Lemyre, & Corneil, 2013). Collins et al. (2005) projected the costs associated with

presenteeism (performance-based work loss) were 6.8% of the total labour costs and

greatly exceeded the combined costs of absenteeism and medical treatment.

In a study (n = 2,348) across 110 organisations and four European nations, time

pressure at work related to sickness presence (Claes, 2011) and reducing time

pressures on employees reduced sickness presence. Importantly, this relates to

ambulance work, especially in relation to organisational Key Performance Indicators

(KPIs) which translate to the AOP in a way that increases stress, reduces the potential

for a regular break and extends work hours through involuntary overtime (van der

Ploeg & Kleber, 2003; Arial, Wild, Benoit, Chouaniere, & Danuser, 2011; Sterud,

Hem, Lau, & Ekeberg, 2011). In a systematic review of workplace-based health

promotion programs (WHP), it was reported that programs including organisational

leadership, health risk screening, individual health improvement programs and a

supportive workplace-based culture reduced presenteeism (Cancelliere, Cassidy,

Ammendolia, & Côté, 2011). Risk factors that contributed to presenteeism included

overweightness, poor diet, lack of exercise, high stress and poor relations with co-

workers and management. (Ishimaru, Kubo, Honno, Toyokuni, & Fujino, 2019) in a

study of Japanese paramedics (n = 254) demonstrated a positive relationship between

presenteeism and impaired work function and near misses.

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Absenteeism, presenteeism and co-morbidities

The literature reports a link between absenteeism and work-related injuries and

that presenteeism was of a greater cost. Campo and Darragh (2012) indicated moderate

Workplace Musculo-Skeletal Disorders (WMSD) were associated with higher levels

of presenteeism than minor WMSD. Both work impairment and work output were

affected by WMSD and it was suggested that costs associated with WMSD

presenteeism are substantial. Similarly, a large Australian investigation using data

from the Work Outcomes Research Cost-Benefit study (n = 78,000) from 59 large

companies representing 10 industry groups, reported there was greater productivity

loss when health conditions were co-morbid with psychological distress (Holden et

al., 2011). This raises the question of whether psychological distress was a mediating

factor in lost productivity and whether all health conditions and injuries should have a

specific psychological intervention to reduce the potential affects.

The other magnifier of absenteeism or presenteeism may be work overload.

Whilst it is difficult to determine how this is defined in ambulance, it could relate to

lack of formal rest breaks, end-of-shift overtime and overtime on days when off duty

staff should be recovering. Whilst workload was referred to in a number of studies on

ambulance personnel as heavy and increasing, it is difficult to quantify in anything but

the most general terms (e.g. case numbers per shift) (Archer, 2012; Coxon et al., 2016).

Complicating the understanding of work overload in ambulance services is the

anecdotal evidence that cases per shift had decreased from eight per shift on average

to currently five per shift. The primary reason for this has been suggested as delays in

off-loading patients at hospital. However, this doesn’t mean the AOP have less free

time, as they spend more time on each ‘case’ at hospitals waiting to off-load patients.

This introduces the spectre of increased workplace sedentary behaviour of paramedics.

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Job satisfaction

A review of fire (n = 231), police (n = 223) and AOPs (n = 232) in New Zealand,

Brough (2004) showed that organisational stressors (see Table 2.4) in ambulance

personnel were predictive of job satisfaction to a greater extent than exposure to

trauma. However, organisational stressors did not necessarily predict psychological

stress. Sterud et al. (2011) confirmed the relationship between organisational stressors

and job satisfaction, adding that lack of leader support was also a predictor of job

satisfaction and that the lack of co-worker’s support was predictive of psychological

distress. Additionally, Jules and Bourque (2009) suggested internal organisational

communication practices can be linked to job satisfaction and affective organisational

commitment only if the ambulance personnel had high rates of communication

satisfaction.

Furthermore, in an analysis of the 2005 LEADS survey data (Patterson, Moore,

Sanddal, Wingrove, & LaCroix, 2009), two variables were identified that were

correlated with “intent to leave” which were job satisfaction and opportunities for

advancement and pay and benefits. It is clear that ‘intent to leave’ is linked to job

satisfaction (Chapman, Blau, Pred, & Lopez, 2009; Patterson et al., 2009) and is

considered to be an ever growing problem in the U.S. where issues such as low pay

and benefits, little time to recover, low appreciation by employers and little

opportunity for advancement are raised (Brown, W. E., Jr., Dawson, & Levine, 2003).

In a survey of Montana U.S. paramedics regarding workforce retention (n = 1,008)

(Perkins, DeTienne, Fitzgerald, Hill, & Harwell, 2009), nine percent were considering

leaving EMS in the next year and 24% in the next five years. Amongst those

considering leaving the following reasons were given: pondering retirement (47%),

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career change (16%), family issues (16%), organisational issues (13%), work hours

(12%), job stress (11%), and pay/benefits (9%).

Finally, a systematic review and meta-analysis (n = 485 studies representing

267,995 individuals) on the measures of job satisfaction related to measures of

physical and mental health, (Faragher, Cass, & Cooper, 2005) found a strong

relationship between job satisfaction and mental problems (burnout, self-esteem,

depression and anxiety). Reisel, Probst, Chia, Maloles, and König (2010), in a study

of managers in the U.S. (n = 320), demonstrated job insecurity was negatively related

to job satisfaction and that it has both direct and indirect effects on work behaviours

and emotions.

Conclusion

It is important to now recognise the health status of AOP is broader than just

mental wellbeing and is connected to not only lifestyle, the work environment and

organisational factors but how AOP regard their own self-worth. Consequently,

ambulance services in Australia could consider supporting through confidential

assessments and intervention, the health of their personnel on a yearly basis that

include physical, mental and psychological components. Not unlike the current mental

health programs where personnel are able to utilise six free sessions with a

psychologist, access could be extended to include a range of health professionals that

include sleep physicians, exercise physiologists, physiotherapists and dieticians.

Reviews of working hours, fatigue systems and ensuring personnel have formal rest

breaks from work could be considered. Based on the Hierarchy of Controls, changing

the way people work (for example, reducing shift length) has the potential to improve

health through reduction in fatigue in comparison to AOP exercising further or

reducing their own fatigue (Morris & Cannady, 2019). It is more effective to change

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the workplace than trying to change the worker (Waters, Collins, Galinsky, & Caruso,

2006).

2.4 Factors Influencing Health and Wellbeing of AOP

The previous section identified what was known of the health and wellbeing of

AOP. Section 2.4 explores the factors that may influence that health status. While there

was considerable opinion and speculation in the grey literature, there was little solid

evidence as to the factors independently associated with injury or with poor physical

and mental health, or of the way in which those factors influence health status in the

peer reviewed literature.

A systematic review of 25 articles on the effects of paramedic jobs on health

status identified a number of risks associated with being a paramedic (Hegg-Deloye,

Sandrine et al., 2013). These risks were acute and chronic stress, obesity, sleep,

fatigue, CVD and cardiorespiratory fitness. Another systematic review in regard to

diseases associated with shift work in all workers identified immunological issues,

such as hypertension, metabolic syndrome, insomnia, CVD, obesity, depression and

cognitive impairment at work (Shariat, Bahri Mohd Tamrin, Daneshjoo, & Sadeghi,

2015).

Whilst some of the risks may be related to shift work or shift work disorder,

other risks are not so clear. For example, (Orellana et al., 2016) reported 4.6% of EMS

personal to have nasal Methicillin-Resistant Staphylococcus Aureus (MRSA). Other

hazards identified specifically to EMS were manual handling and uncontrolled work

environments (Dropkin, Moline, Power, & Hyun, 2015), and especially, driving

(Maguire, Brian J., 2011). The factors associated with health status in ambulance

personnel may be broadly categorised into individual, factors related to the nature of

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ambulance work, the working environment and the organisational environment. The

following explores what is known about these domains and their influence on health

and wellbeing.

2.4.1 Individual Factors

As identified previously, AOP are commonly younger than the general

population and more likely in modern times to be women. As they remain in the

service, they tend to reflect population ageing characteristics with higher rates of

chronic disease and increasing health risk factors. The patterns are dissimilar in

different work categories which partly reflects the different roles and the older age of

people in supervisory or management roles. Little can be done about the impact of

ageing (which has positive and negative benefits) (Yaldiz, Fraccaroli, & Truxillo,

2017). The move to gender balance in the workforce has other advantages, such as

increased flexibility of organisations, a well-rounded workforce which is diverse and

has multi-dimensional interests (Hakim, 2016) and realising the potential of a

workforce with an improved work life balance and flexible work schedules (Stanfield,

Campbell, & Giles, 2004). These clearly outweigh any potential risks.

Obesity

Lifestyle choices, particularly when contributing to obesity, may impact on

health and wellbeing in AOP. Svedin et al., (2012) examined Swedish ambulance

officers with metabolic syndrome, obesity and those who exercised regularly, and

concluded that lifestyle factors, such as regular exercise, a good diet, maintaining

normal weight and job satisfaction could minimise the risk of CVD and metabolic

syndrome. Studnek, J. R., Bentley, et al. (2010) showed that low rates of physical

activity in EMS personnel in the U.S. were a significant contributor to obesity rates.

In a study of obesity related work limitations in a non-ambulance workforce in the

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U.S., Hertz (2004) identified the type, amount or quality of work that obese younger

workers have, are similar to middle age workers. As a consequence, these obesity

related limitations may include less effective CPR (Brachet, David, & Duseja, 2010;

Russo et al., 2011). There is however, limited research into the complex interactions

between risk factors and the prevalence of illness amongst ambulance personnel

(Broniecki, M., Esterman, May, & Grantham, 2010).

Physical exercise

A meta-analysis of the health benefits of physical exercise in the general

population suggested improvements in fall related injuries, depression and emotional

distress as well as reductions in the risk of breast cancer, CVD, diabetes, and colorectal

cancer (Nehrlich et al., 2006). Several studies have demonstrated relatively low rates

of independent physical activity amongst ambulance personnel (Boreham et al., 1994;

Galeano, 1996; Brown, W. E., Dickison, Misselbeck, & Levine, 2002). There appears

to be some difference between urban and rural paramedics. In a study of Australian

metropolitan paramedics (Courtney, J., Francis, & Paxton, 2010) using the

International Physical Activity Questionnaire, 14% less physical activity was reported

for general population participants in a 12-country study. A study on rural paramedics

(Courtney, James A. et al., 2013) found that they exercised less than comparative

community groups. Intuitively, improving physical fitness may be considered to

impact on the rates of injury and illness. However, (Broniecki, Monica et al., 2012)

reported that improving physical fitness of ambulance officers was not associated with

any reduction in rates of injury or illness.

Diet

In one of the few studies on paramedic diet, Anstey, Tweedie, and Lord (2016)

described paramedics in Queensland as having long periods of not eating or

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opportunistic eating and that these habits were influenced by the environment in which

paramedics’ work. Consumption of fast and take away food has been implicated in

weight gain through high energy densities and glycaemic loads and large portion sizes

(Rosenheck, 2008). In a review of the evidence in relation to meal timings, it was

suggested that meal timing has metabolic effects that may influence body weight

(Lomangino, 2013). Finally, the type of nutrition; particularly the consumption of fruit

and vegetables has an association with a range of conditions including Type 2 Diabetes

(Carter, Gray, Troughton, Khunti, & Davies, 2010), CVD and cancer (Manoharan &

Jothipriya, 2016).

Healthy food choices may not be available to paramedics who take a rest break

away from their station, as some were reluctant to take prepared food in ambulance

vehicles. As a result of irregular breaks, paramedics may eat quickly and at irregular

intervals. Patterson et al. (2014) described paramedics as having very ad hoc eating

habits and behaviours because of shift work. Regular meal times are known to

positively influence the excess intake of food (Jakubowicz, Froy, Wainstein, & Boaz,

2012). In addition, shift work and poor sleep patterns have been shown to influence

food selection and meal timing and may be a causative factor in obesity and CVD rates

(Hegg-Deloye, Sandrine et al., 2013).

Conclusion

The literature suggests the benefits of enhancing the physical and mental

resilience of individuals within ambulance. This has three broad strategies which

include: selecting resilient individuals for the work (Bentley et al., 2013), preparing

them for the work through education and training (Makhoul, Sinden, MacPhee, &

Fischer, 2016), and sustaining resilience during their working life (Gayton & Lovell,

2012).

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2.4.2 Pre-Employment testing

A whole of working life approach to health surveillance ensures workers have

the physical and mental capacity (Hasle, 2017) to match work demands and are

carrying out work within their limitations. This should begin at the recruitment stage,

where individuals are selected only if they have the requisite physical and

psychological capacity to undertake the work and that they are not unnecessarily

exposed to greater risk of injury because of a non-matching of worker capacity and

work demands and managing risk to reduce demands on aging workers. The concept

described in Figure 2.7 is important when considering the implications for policy

makers that are described in chapter six and shows that work ability changes as

workers age and changes are needed at the individual and especially the organisational

level to maintain worker effectiveness.

The Richmond Ambulance Authority in the U.S. conducts pre-hire physical

agility testing including a computer-based evaluation of a candidate’s musculoskeletal

strength and likelihood of failure and has reported reduced injury claims of 45% (EMS,

2013) as a result. A systematic review on pre-employment physical testing as a

predictor of musculoskeletal injuries in paramedics, identified that physical fitness,

gender and age, were associated with a high rate of injury however, there was little

evidence to quantify the relationship (Jenkins, Smith, Stewart, & Kamphuis, 2016).

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Taken from (Parker, Anthony W. & Worringham, 2004)

Figure 2-7. Whole of Working Life Approach to Health Surveillance.

Effective medical and work-related screening processes are essential in

identifying potential high-risk factors for OHS injuries and illness in AOP; however,

the efficacy of these practices remain in doubt (D. Chapman, 2007; Drewitz-Chesney,

2012; Thornton & Sayers, 2014; Kukowski, King, & DeLongis, 2016). Potential risk

factors for paramedics identified in relation to musculoskeletal injuries include

conceptual, intuitive and anxious personalities, hypermobile joints, self-limited weight

lifting, played less sport, or exercised less (Broniecki, M., Esterman, May, &

Grantham, 2011). Predicting mental capacity was similarly unclear. However, Regehr,

C., Goldberg, Glancy, et al. (2002) identified that personality traits characterised by

egocentricity, suspiciousness, hostility and manipulation were associated with mental

health leave. In addition, a systematic review of personality traits for emergency

services personnel, found that conscientiousness, whilst protective for PTSD, may also

put personnel at risk of burnout (Mirhaghi, Mirhaghi, Oshio, & Sarabian, 2016).

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James, Reneman, and Gross (2016) suggested that testing for these factors using a

functional capacity evaluation approach, may match a person’s physical and mental

ability to the occupational demands of AOP and provide a baseline measurement of an

ambulance employee’s capacity to participate in ambulance work. However, the

efficacy of the assessment is only as good as the ability to assess work demands

(Leijten et al., 2015).

Conclusion

It was unclear which personality was better suited to be a paramedic. There was

no consistent screening device, nor any agreement as to the ‘ideal’ physical and mental

characteristics required. It is possible that overt risk through the presence of either

physical or mental illness may impair an individual’s capacity and increase their risk.

Beyond that, any attempt to presume an ideal physical or mental resilience may be

considered discriminatory and subject to flawed and biased judgement.

2.4.3 Education and Training

No single piece of equipment or training program has been identified as effective

in reducing the most common physiological and psychological injuries in AOP.

Consistently, the literature that describes multiple approaches aimed at improving

psychological and physical resilience, have been shown to be successful in health care

workers (Brown, W. E., Jr. et al., 2003; Verbeek et al., 2012). The transfer of education

to universities has shifted much of the training of paramedics (at least) to external and

thus pre-employment agencies. Most curricula do address elements of safety including,

safe manual handling procedures and healthy lifestyles. However, there was little

evidence of the effectiveness of specific resilience training (Wild et al., 2018).

Broniecki, Monica et al. (2012) found no association between individuals who had

recent manual handling training and injury rate or claim rate. In contrast, Okada, Ishii,

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Nakata, and Nakayama (2005) in a study of Japanese paramedics, found that only 8.2%

of the respondents who reported lower back injuries had received any instruction on

relieving physical stress (i.e. posture, ergonomics and how to avoid or minimise

physical stress). The transformation of paramedic training to universities in many

countries has resulted in greater screening for and development of intellectual capacity.

However, universities have not screened for physical or mental capacity.

2.4.4 The Nature of Ambulance Work

Ambulance work (particularly that of paramedics), involves exposure to some

of life’s most challenging scenarios, in an environment which is not possible to control.

Highly disturbing and emotionally charged incidents can be confronting, particularly

when they are repetitive (Morales, McEachern, MacPhee, & Fischer, 2016; Behnke,

Rojas, Karrasch, Hitzler, & Kolassa, 2019). Increasingly, AOP operate in an

environment of growing workload, injuries, fatigue and high demand for clinical

accuracy and an uncontrolled safety environment (Weaver, Wang, Fairbanks, &

Patterson, 2012; Courtney, James A., Francis, & Paxton, 2013; Roberts, Sim, Black,

& Smith, 2015). Although paramedic employment is regarded as physically active,

they may also be exposed to periods of sedentary activity (Coffey, Macphee, Socha,

& Fischer, 2016).

Additionally, equipment must be carried, often at the same time as a patient.

Consequently, paramedics have to carry heavy loads, although this may be mitigated

with the use of appropriate load reduction strategies (such as using shoulder straps),

which may reduce heart rate variability and cortisol concentration leading to a

reduction in CVD through a reduction in stress (Karlsson, Niemelä, Jonsson, &

Törnhage, 2016). Work related fatigue was described by Sluiter, de Croon, Meijman,

and Frings-Dresen (2003), P. 62, as: “the short-term effect of a working day and an

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intermediate variable between work demands and the development of subjective health

complaints and sickness absence”. Given the nature of ambulance work, it is not

unreasonable to assume fatigue in ambulance work may be significant. How AOP have

managed this fatigue has depended on educational strategies and knowledge about age

and the occupation specific effects of fatigue.

The lack of formal rest breaks during the work schedule of paramedics may

influence injuries of the back, neck or shoulder and increase fatigue (Broniecki,

Esterman, & Grantham, 2012). Unfortunately, the nature of ambulance work and the

persistent and often unexpected requests for urgent assistance, impact negatively on

the access to formal rest breaks. However, strategies such as ‘encouraging workers to

take breaks’, can lead to positive long term occupational health effects and reduce

fatigue (Zacher, Brailsford, & Parker, 2014).

2.4.5 Occupational Violence

Occupational violence is an emerging issue in ambulance services. An analysis

of injury reports from paramedics and firefighters in an urban U.S. system reported an

assault rate of 4%; 79% of assaults were against paramedics and 59% were deliberate

(Mechem, Dickinson, Shofer, & Jaslow, 2002). A Swedish study (Petzäll, Tällberg,

Lundin, & Suserud, 2011) reported that 66% of paramedics had experienced threats or

violence during their work. A study of first responders from rescue stations in Slovenia

(Gabrovec, 2015), identified that 49.6% reported physical abuse, 26.8% suffered

injuries inflicted by patients and 24.4% experienced sexual harassment. In a

longitudinal study of EMS providers in the U.S., the majority of EMS professionals

reported they or their work partner had been assaulted (Bentley & Levine, 2016).

Bigham et al. (2014), in a study of Canadian paramedics, identified that 75% reported

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violence including verbal (67%), intimidation (41%), physical assault (26%), sexual

harassment (14%) and sexual assault (3%).

Brough (2005b) categorised occupational violence amongst paramedics on a six-

point scale of: 1) serious verbal abuse, 2) minor verbal abuse, 3) minor verbal threats,

4) threat of sexual assault, 5) sexual assault, and 6) physical assault with a dangerous

weapon. (Brough, 2005) also noted that violent incidents were predictive of adverse

levels of job satisfaction and that frequent verbal violence was the primary predictor

of job satisfaction and psychological strain. The number of paramedics, who report

occupational violence in the ambulance service which was studied, has steadily

increased (QAS, 2016a) and is shown in Figure 2.8. It should be noted that the rate of

deliberate physical attack is larger than the rate of verbal attacks and is increasing at a

greater rate than verbal attacks.

(QAS, 2016b)

Figure 2-8. Occupational Violence Rates in the Ambulance Service.

A study of ambulance cases in southern California, was able to assign factors

predictive of violent behaviour including police presence, gang members, perceived

0

50

100

150

200

250

300

Count

Fiscal year

Deliberate PhysicalAttack

Verbal Threat

Accidental Contact

Total

Linear (DeliberatePhysical Attack)

Linear (Verbal Threat)

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psychiatric disorder, presence of alcohol or drug use, gender, patient and hour of day

(Grange & Corbett, 2002). Similarly, a Slovenian study found higher rates of

occupational violence against female paramedics (Gabrovec, 2015), as did Arnetz and

Arnetz (2001) who suggested females were more likely to be the victims of sexual

harassment or assault. Gender (female) was the only predictor of intimidation, sexual

harassment and sexual assault in paramedics (Koritsas, Boyle, & Coles, 2009).

However, the QAS Paramedic Safety Taskforce final report identified that the count

of deliberate physical attack and verbal threats for females was less than males (QAS,

2016a).

The difference in rates of occupational violence reported in the literature was

dramatic and relates to the cultural context and how the data was collected. Paramedics

who have greater frequency of contact (e.g. urban environments) (Koritsas et al.,

2009), were more likely to report higher rates of occupational violence, compared with

rural environments. There was a lack of consistency of survey methods and data

collection by agencies throughout the world. Some assessments reported results for a

particular period of time, while others looked at violence over the lifetime of a

paramedic (Grange & Corbett, 2002; Petzäll et al., 2011). The literature varied with

the ambulance service under study reporting raw data and rates per 1000 incidents

(QAS, 2016a) and others reporting rates per respondent population or rates per

paramedic population (Maguire, Brian J, O'meara, O'neill, & Brightwell, 2018).

Paramedic students, despite 32.6% being exposed to violence on clinical

placement, were reluctant to report occupational violence for fear it would jeopardise

their chances of obtaining a job (Boyle & McKenna, 2017). In addition, violence

against ambulance personnel may be greater than has been reported as paramedics may

regard some aspects of the violence as insignificant and part of the job (Maguire, Brian

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J et al., 2018). This was supported by other findings where 65% of assaulted

emergency department personnel and 43% of health care workers did not report an

occupational violence incident (Findorff, McGovern, Wall, & Gerberich, 2005). In

conclusion, Maguire, Brian J et al. (2018) indicated there was no evidentiary basis for

any programs to decrease occupational violence against paramedics and that there was

an urgent need for more research into this issue.

2.4.6 The Nature of the Working Environment

Progressive changes in work ability are ongoing throughout life. By considering

work ability and its relationship with other factors, such as exercise, sleep, workload

and task level, the complexity of the OHS equation increases (Airila, Hakanen,

Punakallio, Lusa, & Luukkonen, 2012). The work environment in ambulance is

diverse and often difficult to control. Some AOP, such as EMDs have largely sedentary

roles and even paramedics spend considerable proportions of their time in sedentary

positions whilst at computers, caring for patients, completing paper work or driving.

At times, their work environment is the patient support compartment of the ambulance

vehicle, while at other times it can be in patient’s homes, public places or in difficult

(and dangerous) locations such as crash scenes. Thus, the environment is difficult to

‘control’ in an OHS sense and so paramedics must moderate risk through preparedness

and through modification of work practices to accommodate unpredictable and

unmanageable sites. The lack of control influences practical interventions.

Shift work

Most paramedics and EMDs in urban areas work a 24-hour rotating roster. It is

well known that shift work has physiological, psychological and psychosocial effects

as a result of disruption to the normal sleep-wake cycle (Raether, 2003). Shift work

has also been known to affect social and quality of life (Wright, Bogan, & Wyatt,

2013) and shift workers were more likely to have extra accidents and sickness absence

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(Ohayon, Lemoine, Arnaud-Briant, & Dreyfus, 2002). A systematic review reported

that shift workers were more likely to be at risk of weight gain and glucose intolerance

(Mander, Winer, & Walker, 2017). The Nurses’ Health Study II (NHS II) in the U.S.,

(Ramin et al., 2015) reported night shift workers had an increased risk of cancer and

CVD, higher rates of obesity, caffeine and calorie intake, and were more likely to be

current smokers and have shorter sleep durations than never night shift workers.

Additionally, a declaration by the International Agency for Research on Cancer in

2007 suggested that shift work is a probable Class 2A human carcinogen (Stevens et

al., 2011).

Two studies from Victoria, Australia used a modified version of the Standard

Shift Work Index and found that shift working paramedics in both metropolitan and

rural areas were at increased risk of fatigue, depression and poor-quality sleep

(Courtney, J. et al., 2010; Courtney, James A. et al., 2013). A further Victorian study

of paramedics found that 10% of participants had excessive sleepiness, 29% had

nodded off whilst driving and 68% had poor sleep quality (Archer, 2012). A literature

review (Sofianopoulos et al., 2012) on the effects of shift work on paramedics

concluded that shift work has physical and psychological health and well-being effects

that can impact both work and personal life. These same studies demonstrated less

physical activity than a comparison study of physical activity levels in 12 countries;

72% were reported to be poor sleepers, and 28.86% reported chronic fatigue

(Courtney, J. et al., 2010; Courtney, James A. et al., 2013). Similar proportions of

paramedics in both metropolitan and rural areas were regarded as poor sleepers;

however, the rural cohort demonstrated significantly poorer sleep quality. This was a

counter intuitive result as the metropolitan study participants worked a roster that

consisted of two 10-hour day shifts, 24 hours off, two by 14-hour night shifts followed

by four 24-hour periods off work (10/14 roster), whilst only 50% of the rural

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participants worked this roster. Therefore, workload for the rural paramedics would be

less than that of metropolitan paramedics and should allow greater opportunity for

napping (Baulk et al., 2007). However, on-call rosters may pose their own challenges

with poor sleep and chronic fatigue (Nicol & Botterill, 2004).

Shift work disorder (SWD), which is more severe than the normal sleep

disturbances associated with shift work, is a clinically recognised condition, diagnosed

by the presence of excessive sleepiness and/or insomnia for ≥ one month during which

the worker is performing shift work (Krause et al., 2017). However, not all shift

workers develop SWD, however the reasons why remain unclear. According to Drake

(2010) the reasons behind susceptibility to SWD include vulnerability to insomnia,

sensitivity to sleep loss or variation within the circadian system that is often heritable.

Shift intolerant versus shift tolerant workers have a circadian cycle that is longer or

shorter than 24 hours, indicative of circadian desychronisation and an inability to adapt

to a shift work schedule (Reinberg & Ashkenazi, 2008). A study on melatonin rhythms

has shown that some workers were not able to adapt their circadian rhythms to the

pattern of sleep required by shift work (Blask, 2009). In addition, a shift work

simulation study of the first night shift (Santhi, Horowitz, Duffy, & Czeisler, 2007)

found the response times in tests of visual selective attention were significantly

affected. After 17 to 19 hours without sleep, performance of some participants was

worse than those with 0.05% Blood Alcohol Content (BAC) and response speeds were

up to 50% slower (Williamson, Feyer, Friswell, & Finlay-Brown, 2000).

The impact and cost of additional and extended shift hours is well documented

in the literature (Dembe, Erickson, Delbos, & Banks, 2005; Knauth, 2007) and has

been examined at a national level in the United States (Caruso, Hitchcock, Dick,

Russo, & Schmit, 2004). One study demonstrated that working at least 12 hours per

day was associated with a 37% increased hazard rate and 60 hours per week was

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associated with a 23% increased hazard rate. Every additional five hours over 40 hours

per week was associated with an average increase of 0.7 injuries per 100 hours worked

and every additional 2 hours per day over 8 hours was associated with an average

increase of 1.2 injuries per 100 worker hours (Dembe et al., 2005).

Shift work has also been shown to be associated with illness. A 2004 review of

extended hours in the U.S. demonstrated higher risk of myocardial infarction

associated with 61 hours per week (Caruso et al., 2004). Relationships between shift

work and hypertension, obesity, tobacco smoking and alcohol use was inconclusive,

nonetheless, increased morbidity and mortality was reported in eight out of 12 studies

(Caruso et al., 2004). The potential effects of extended shift hours are summarised in

Table 2.4-1.

Table 2.4-1 Potential Negative and Positive Effects of Extended Shift Hours

Potential Negative Effects Impact on

Potential Positive Effects

Impact on

accidents on & off the job W&P travel time P length & quality sleep P cost P Sleepiness P time with family P alertness P time for socialising P fatigue P time for domestic

duties P

Adverse effects on performance

W&P satisfaction with working hours

P

Prolonged exposure to stress & toxins

P Fewer handovers W

Adverse effects on health P overtime P ± Absenteeism W Communication problems

with managers P

Problems driving home P

Adapted from (Knauth, 2007) Abbreviations: Work = W, Personal = P, ↑ = increased, ↓ = decreased.

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Sleep patterns and health

Poor sleep quality is a known risk factor of shift workers as it disrupts the

circadian rhythm and positions a shift worker to be out-of-step with the nature of sleep

and leads to a decrease in the quality and quantity of sleep (Charles et al., 2007).

Neufeld, Carney, Dolezal, Boland, and Cooper (2017) reported shift working EMTs

had more fragmented sleep. In a study of job strain, shift work and subclinical heart

disease in paramedics, Wong, Ostry, Demers, and Davies (2012) concluded, exposure

to job stressors may lead to early signs of heart disease.

Sleep duration has been linked to obesity and the timing of eating patterns

(Baron, Reid, Kern, & Zee, 2011). “Late Sleepers”, individuals who had a mid-point

of sleep after 5:30AM, had higher fast food, full sugar soft drinks and lower fruit and

vegetable consumption. Additionally, short sleep duration is linked to a greater risk of

obesity and increased mortality (Grandner, Hale, Moore, & Patel, 2010). Shift workers

who are sleep disturbed may make unhealthy choices in relation to nutrition and

snacking and the greater the sleep restriction the greater the odds of choosing a sweet

snack (Heath et al., 2012).

The longer-term effects of sleep restriction are less well known, but it was

suggested that chronic sleep deprivation, may alter the way people deal with

challenges and may negatively impact on the way they deal with stress. Whilst human

based studies are somewhat restricted, animal laboratory studies suggest that sleep

restriction may gradually change some brain systems in a manner that is like what is

seen in some stress related disorders such as depression. Krause et al. (2017), P. 404,

in a meta-analytical review of the literature, reported: “sleep abnormalities were

robustly observed in every major disorder of the brain, both neurological and

psychiatric”. A systematic review of the literature in relation to sleep duration and

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mortality (Manoharan & Jothipriya, 2016) and a population based 22 year follow up

study (Hublin, Partinen, Koskenvuo, & Kaprio, 2007), demonstrated that people with

both short and long-term sleep disturbance were at a higher risk of mortality.

There are a range of factors that influence the amount and quality of sleep. A

study of rail workers showed that shift type and having dependents were significant

predictors of sleep quality (Paterson, Dorrian, Clarkson, Darwent, & Ferguson, 2012).

Such factors support the importance of physical, social and cultural environments and

the complexity of this issue. Organisational changes may add to the already

documented sleep disorders of shift workers. A study of police officers in Sweden

suggested that extensive organisational changes such as downsizing, restructuring and

changing work procedures led to a small increase in sleep disturbances and negative

health effects (Greubel & Kecklund, 2011).

Fatigue and shift work

Discussing fatigue without debating sleep is to not recognise the inexorable link

between the two issues (Dawson, D. & McCulloch, 2005; Dawson, Drew, 2012).

Measuring fatigue is challenging as most reports include self-perceptions. Barnekow-

Bergkvist, Aasa, Ängquist, and Johansson (2004), suggested that significant predictors

of fatigue in ambulance paramedics in Sweden, included VO2 max, isometric back

endurance, one-leg rising, isokinetic knee flexion, shoulder extension, strength and

height. In a study of sleep quality and its association with fatigue in EMS workers,

Patterson, Suffoletto, Kupas, Weaver, and Hostler (2010) reported sleep and fatigue to

be at an unhealthy level and indicated overweightness (41.9%), obesity (42.7%), a

diagnosis of one or more health conditions (59.6%), and the presence of severe mental

and physical fatigue (44.5%), were associated with poor sleep quality and fatigue.

These findings are similar to results previously reported in Sterud et al. (2011).

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Fatigue can have clinical and operational impacts. Patterson, Weaver, Frank, et

al. (2012) used the Pittsburgh Sleep Quality Index, Chandler Fatigue Questionnaire

and the EMS Safety Inventory in a study of paramedics found that amongst the 55%

of respondents who were fatigued, 18% had experienced an injury (OR=1.9), 41%

reported a medical error (OR=2.2), and 90% reported safety compromising behaviour

(OR=3.6). In a survey of Victorian paramedics, 73% reported fatigue caused by

workload, 75% had made fatigue related errors, and 24% had less than five hours sleep

per night and 27% had a diagnosed medical condition linked to work-related fatigue

(Sofianopoulos et al., 2012). Williamson et al. (2011) suggested that fatigue can result

in errors especially where the task requires sustained attention and monotony.

Landrigan et al. (2004) showed a 36% increase in serious medical errors and nearly

six times increase in severe diagnostic errors amongst medical interns undertaking 24

hours or longer shifts. A study of 742,000 emergency medical incidents attended by

2,400 paramedics in the state of Mississippi, (Brachet et al., 2010) associated fatigue

with workplace performance, particularly at the end of a long shift. Further analysis of

the same data indicated that this reduction in performance may result in a 0.76%

increase in 30-day mortality of patients (Brachet, David, & Drechsler, 2012).

In an attempt to reduce the risks associated with fatigue, a number of Australian

Ambulance Services introduced a Fatigue Risk Management System (FRMS) based

on levels of control that were developed by the Centre for Sleep Research, University

of Adelaide (Dawson, D. & McCulloch, 2005; QAS, 2011). However, this approach

may be flawed for three reasons. First, (Patterson et al., 2017) suggested evidence-

based guidelines for Fatigue Risk Management (FRM) in EMS should be based on a

clinical model of Problem/Population, Intervention, Comparison, and Outcome

(PICO) that would help EMS systems evaluate the quality of the evidence and

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evidence-based guidelines for fatigue risk management. Second, the FRMS used in

some Australian ambulance services is based on a self-report model and the cost is

borne by the AOP. Time off is deducted from accrued sick leave if available and if this

leave is not accessible, then the person reporting fatigue may lose wages for lost time

or must substitute other leave as recompense. Third, there were some AOP who do

other paid work, including overtime in an ambulance service, and the prescriptive rules

of the FRMS jeopardise this work and subsequent financial advantage.

Work-related naps/rest periods have been shown to decrease work related errors,

improve performance, cognition and alertness (Garbarino et al., 2004; Smith-Coggins

et al., 2006) and, in some cases, improvements in self-satisfaction and reductions in

self-reported fatigue are described (Bonnefond et al., 2001). Caution should be

exercised with longer naps where some workers develop sleep inertia, a condition

where people feel groggy for up to two hours post a nap (Takahashi, Arito, & Fukuda,

1999). It has been suggested that naps ten to twenty minutes in duration may help

reduce sleep inertia (Brooks & Lack, 2006). Any napping strategies that may be

introduced should include the ability for a worker to take an uninterrupted sleep, as the

forced and unexpected awakening of sleep can lead to poorer quality of sleep (Shoji,

Saitoh, & Sakai, 1995). Whilst there was some suggestion that night-time naps during

a night shift are advantageous (Takeyama, Kubo, & Itani, 2005), there are clear

organisational disadvantages with scheduling and production and the timing of that

nap (Takahashi, Arito, & Fukuda, 1999). In this context, all AOP can’t have a nap at

the same time and the longer the nap, the greater the cost to the organisation

(Takeyama et al., 2009). However, there is no evidence to suggest this would be offset

with increases in performance of AOP and a reduction in morbidity or mortality of

patients.

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2.4.7 The Organisational Environment

Ambulance services are large and complex organisations with a high public

profile and organised on a semi-military style (top down direction, uniforms, rank

structure and badges, levels of expertise and a military style approach to governance).

They are often public utilities, highly identifiable and visible and are directly

accountable to the community through governments and both individual and

organisational cultures impose risk and impact on the level of risk taking behaviour

(Nordlöf, Wiitavaara, Winblad, Wijk, & Westerling, 2015). Normalisation of the

nature of the ambulance mission, the risk associated with aiding the community and

its associated consequences may be part of the work-related health culture in

ambulance (Wankhade, 2016). The attitudes to health and safety are related to the

culture within the organisation, which in turn was determined by the organisation’s

leadership (Petrie, Gayed, et al., 2018). In health services, safety culture tends to focus

more on patient safety than worker safety. In addition, there has been considerable

research into patient safety, and it has been suggested that meagre attitudes to patient

safety may reflect an inadequate mind-set to worker safety (Bigham et al., 2012;

Gallego, Westbrook, Dunn, & Braithwaite, 2012).

Nonetheless, there is an increasing body of evidence that safety culture can

reduce injury rates within ambulance services (Eliseo et al., 2012; Weaver et al.,

2012), with other industries also reporting a relationship between safety culture and

injuries. A cross-sectional survey in a large construction project in the U.S. (Probst,

Brubaker, & Barsotti, 2008), reported a positive safety culture was related to a lower

injury rate and a lower underreported injury rate. In the health context, a poor safety

culture was associated with medical errors or adverse events (Mardon, 2010) and a

fear of retribution (Atkinson, Fullick, Grindey, & Maclaren, 2008). This retribution,

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whether perceived or actual, may take many forms, such as investigations, restrictions

on clinical practice and discipline and may be related to a poor organisational response

rather than the reality of the demands associated with the clinical case.

Safety culture

A safety culture is related to the preparedness of workers to report incidents. For

instance, medication errors were reported only nine percent of the time in an

anonymous survey of 352 San Diego paramedics, with four percent of these errors

never reported in an organisational clinical quality improvement (CQI) process (Vilke

et al., 2007). A case-based study of EMS personnel described 44% (n = 27) as

including near misses (injuries that did not result in harm to the patient, however had

the potential to do so) and 56% (n = 34) involved adverse events (injures caused by

medical management) (Fairbanks et al., 2008). Underreporting may be reduced with

greater feedback on incident reports as well as improved training in patient safety

(Cano-del Pozo et al., 2014) and frequent advice and training, which was linked to

safe workplace-based actions (Eliseo et al., 2012).

Safety culture is also contextual. A study of the safety culture across the South

Australian health system reported poorer safety cultures were reported in metropolitan

ambulance (Gallego et al., 2012). In the ambulance service, there was no known

available data that reports on this issue and whilst several variations and mistakes in

medical treatments are identified through an audit process, there is no knowledge of

how extensive the problem of underreporting medical errors is and why. Additionally,

as ambulance work is undertaken in relative isolation from ongoing clinical care, it is

known that some EMS personnel may not recognise an adverse event has taken place,

predominately in relation to clinical judgement (Fairbanks et al., 2008). A culture of

health promotion in an organisation has an association with worksite safety culture, as

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do policies on safety attitudes and culture (Aldana et al., 2012). Accordingly, concern

for improved safety culture in the U.S. has resulted in the National Emergency

Services Advisory Committee (NEMSAC) recognising a culture of safety in EMS as

the top priority in a list of 80 priorities (NHTSA, 2013).

2.5 Health Improvement Strategies

Whilst paramedic safety is of concern for ambulance organisations, research into

health-related risk factors, occupational violence, safety culture, near-miss reporting,

quality improvement techniques, and human factors engineering is deficient (Bigham

et al., 2012). The focus is on safety rather than health even though ambulance systems

use the terminology of ’health and safety’ (Bentley & Levine, 2016). It is important to

recognise that organisational and work redesign and individual cognitive or human

factors can assist in improving the health and safety or workers. In addition, the

evidence for health improvement in ambulance is not at all clear and in comparison,

to other emergency services, ambulance work is different in its nature, volume and

intensity. It has been recognised that the majority of research into wellness in

emergency services was focused on trauma exposure and PTSD and that AOP are at

risk (Petrie et al., 2018). Subsequently, every state ambulance service in Australia has

programs of wellness and it is known that these programs focus on mental health and

psychological distress. In contrast, there are no known programs of wellness in

Australian ambulance services that have a broad combined focus of wellness that

includes physical, mental and psychological health.

In the ambulance service being researched, health promotion is available via the

state health department and it is believed that no recurring health assessments happen,

and personnel are usually only referred for further assessment if they are unable to

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carry out their duties. On the other hand, it is known that health promotion targeting

physical activity, diet and tobacco smoking achieves positive benefits (Emmons,

Linnan, Shadel, Marcus, & Abrams, 2000). In addition, one of the major components

of any health improvement program is knowing the health status of the potential

participants and how such a program can influence at an individual level (Rula &

Hobgood, 2010). Accordingly, Chih-Wen et al. (2009) showed that repeat engagement

in health risk assessments is a critical issue for health improvement.

Worksite health promotion programs may be effective as workers are a captured

audience, interventions can be offered repeatedly, and workplaces provide access to

large numbers of people that may not be reached by other means (Sorensen et al.,

1999). Preeminent intervention outcomes were found to occur when there was a

nutrition and physical activity component, dieticians were involved in nutrition

education, changes in healthy food options occurred and were advertised, feedback on

diet was given to participants, employees were involved in planning and managing

programs, and participants understood change theory (Steyn, W Parker, EV Lambert,

& Mchiza, 2009). The report on ‘Intervention on Diet and Physical Activity’ suggests

that the involvement of family in workplace-based interventions creates an added

benefit (WHO, 2009).

Scott, Mannion, Davies, and Marshall (2003) and Blake and Lloyd (2008)

suggested interventions in the workplace to improve individual health, need to be

targeted at people after their health status has been assessed, rather than at an

organisational level based on anonymous health surveys. Both the individual’s health

and the workplace health culture need to be addressed with a combined approach of

strong public health and vigorous healthy workplace interventions. Two programs to

improve the health of ambulance personnel stand out. A wellness intervention in a

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North Dublin ambulance service identified 97% with unhealthy lifestyle behaviours,

and 74% indicated they had made positive lifestyle changes after the intervention

(Devaney & Noone, 2008). Additionally, a recent study in the U.S. to improve health

in EMS personnel was successful in reducing diastolic blood pressure, heart rate,

cholesterol, body weight, body fat and increased participant’s ability to do push ups

and sit ups (Oglesbee et al., 2015). Moreover, a systematic review of worksite physical

activity programs supported the implementation of these programs in the workplace

and suggested that this would assist in reducing the risk of musculoskeletal injuries

(Proper et al., 2003). On the other hand, a systematic review of the cost effectiveness

of worksite physical activity or nutrition programs showed that their success was

dependent on the willingness of the decision makers to fund such programs (Johanna

et al., 2012).

Furthermore, a meta-analysis of organisational wellness programs designed to

support healthy behaviour in the workplace and improve health outcomes, concluded

that participation was associated with reduced absenteeism and increased job

satisfaction (Parks & Steelman, 2008). This study described two types of programs:

those that involved a fitness component either on or off site and comprehensive type

programs which also included an educational component. Absenteeism and staff

satisfaction (feeling good about oneself, improving the employee’s attitude towards

the organisation, reduced turnover, and enhanced recruiting) was identified as a reason

why organisations introduce wellness programs. In the same way, a meta-analysis of

interventional trials, Plat, Frings-Dresen, and Sluiter (2011) found only seven trials

where there was an attempt to measure effectiveness. These trials involved a range of

personnel in emergency response and only one of these was specific to ambulance. Of

these, six were physical interventions and one was psychological. All but one of these

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studies was post 2004 while the only ambulance study was published in 1991 (Gamble

et al., 1991).

Employee Health Management Programs (EHMP) in the U.S. were found to be

unrelated to performance and negatively related to absenteeism and that these

outcomes worsen if these programs were not voluntary (DeGroot & Kiker; Probst et

al., 2008). It should be noted that employers in the U.S. pay for the health insurance of

employees, so the motivation for improving health may be financial rather than

genuine concern for employee wellbeing and improving the health of the workplace.

It has been shown that worksite-based programs were costlier but more effective in

reducing body weight, cholesterol and CVD risk and that the benefits to employees

may not immediately translate to benefits for the organisation (Johanna et al., 2012).

Additionally, lower back injuries could be mitigated by introducing workplace training

and education on safe lifting (Makhoul, Sinden, MacPhee, & Fischer, 2016). Whilst

this contradicts previous literature about recent manual handling training not reducing

injuries and controlling injury risks in paramedics (Lim, Black, Shah, Sarker, &

Metcalfe, 2011). Makhoul et al. (2016) reported that generating more work from the

lower body relative to the trunk was more likely to decrease peak load in the lumbar

vertebrae of L4/5 and would inform the development of training and education to

control risks.

Table 2.5-1 describes the outcomes of the randomised control trials (RCT) and

non-randomised control trails (NRCT) in terms of cardio respiratory fitness (CRF),

muscle flexibility, muscle strength, body weight and composition criteria. A large

body of work has been completed on workplace fitness standards and the health of

U.S. firefighters and in some instances; this has included dual role

firefighter/paramedics. It was shown that workplace fitness standards and

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interventions to improve workplace fitness, especially in relation to physical

interventions, improved CRF (Lin et al., 2010; McDonough, Phillips, & Twilbeck,

2015). The lasting message from a meta-analysis of EHMPs (DeGroot & Kiker, 2003)

is that they have to be continuous, preventative and include multiple elements related

to the needs of work demands, not just health promotion.

Table 2.5-1 Elements and Effectiveness of Workplace Physical Activity Programs in Relation to Cardio Respiratory Fitness

Element RCT NRCT Conclusion

Physical Activity 5 3 Strong evidence of a positive effect

CRF 3 6 Inconclusive evidence

Muscle Flex 4 (low quality) 4 Inconclusive evidence

Muscle Strength 1 4 Inconclusive evidence

Body weight 6 4 Inconclusive evidence

Body Comp 1 4 Inconclusive evidence

General health 3 3 Inconclusive evidence

Fatigue 2 (low quality) 0 Limited evidence

MSK Disorders 5 2 Strong evidence of a positive effect

Blood Pressure 1 3 No evidence

BSL 1 3 No evidence

Abbreviations: CRF = Cardio Respiratory Fitness, Flex = flexibility, Comp = composition, BSL = blood serum lipids, MSK = musculoskeletal, RCT = randomised control trial, NRCT = non-randomised control trials. Adapted from (Proper et al., 2003; Johanna et al., 2012).

2.6 Conclusion

The literature on health status, health risks, and the health culture of AOP and

ambulance services and how these elements interact and influence each other are

deficient. Additionally, the literature focuses on paramedics and there was little

evidence concerning EMDs, and no literature involving supervisor/managers in

ambulance.

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The creation of sustainable prevention programs that lead to improvement in

health of AOP requires awareness of how work-related factors can affect their health

including the rate of occupational injuries and chronic disease and how these rates

relate to risk factors such as shift work, sleep, fatigue, anxiety, diet and obesity.

Therefore, a more in depth understanding of the health status of AOP and of the factors

that appear to influence that status would help inform remedial strategies.

It is known from this literature review that injury rates for paramedics are higher

than the general population in Australia (Maguire, B.J. et al., 2014) and the U.S.

(Reichard et al., 2017). In addition, evidence exists of an association between mental

and physical health in a broader context (Scott et al., 2016). However, this association

was not evident in the literature related to ambulance services and furthermore, the

evidence in regards health being worse than the general population was variable

(Sterud et al., 2008b; Hansen et al., 2012). The wellness programs associated with

ambulance services in Australia focus on mental well-being. This approach was

appropriate given the evidence in relation to mental health and psychological distress

being consistently higher than the Australian population when considered in the

context of ambulance services (Petrie et al., 2018). However, the causative factors of

mental health disorders and psychological distress in AOP are little understood. In

contrast, the evidence in relation to causation, the relationship to chronic disease,

organisational factors, the work environment and lifestyle factors in ambulance

services is variable. Additionally, this evidence was more often related to shift work

than ambulance services (Shariat et al., 2015). This review identified three broad

elements that effect the health of AOP, which were individual characteristics, the

working and organisational environment and that health improvement strategies were

limited to mental health interventions.

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Literature review

This literature review identified gaps in the knowledge of the health status of

AOP and as such contributed to the development of the AHS 2015 and underpins the

research undertaken in this study. This literature review assisted in developing a mixed

methods research design using a backwards approach. Whilst a focus of this research

was the collection and analysis of data through the AHS 2015 and the semi-structured

interviews, the literature review demonstrated a lack of depth in the available research

designs and as such the research design was novel in its composition, including sound

research design principles, knowledge of the ambulance industry and AOP.

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3 Research Design

In progressing this research, an implicit theoretical perspective based on the

researcher’s knowledge of the ambulance industry was developed. It is counter

intuitive that an ambulance employee would not give thought to their own health. It

appears however, that this concept does not apply, and AOP consider mainly patient

health. Concepts, such as an individual’s understanding of their health are often linked

to domains that are easy to understand (Lakoff, 2003). The difficulty with

comprehending the health of AOP is that it is an abstract concept, coalesced with the

emergency service providers myth that they are invulnerable to the vagaries of

lifestyle, work, organisational and environmental factors that can contribute to poor

health. This has been demonstrated in relation to mental health (Petrie, 2018).

Although emergency service workers recognise the importance of their own health,

there seems to be a lack of knowledge of causation of the factors that impact on that

health and how to manage those factors when known e.g. stress (Rice, Glass, Ogle, &

Parsian, 2014).

A common theme in the examination of the health of AOP (primarily

paramedics) is the research that focuses on what we think was understood as causation

for poor health (mainly mental and psychological), rather than looking more broadly

and attempting to create models that predict health status in AOP in an individual and

organisational context. Consequently, the literature primarily describes well-being in the

context of AOP as mental health and psychological distress (Varker et al., 2017; Petrie et

al., 2018) for an individual and related to the trauma of ambulance work. It rarely considers

lifestyle, the organisation of the work and the working and organisational environment.

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Research design

Mixed methods research studies have emerged as a way of combining

qualitative and quantitative research paradigms to better understand the research

problem and take advantage of their strengths and differences (Creswell, 2011). The

type of strategy depends on several factors; and for this research the strategy chosen

was based on an implicit theoretical perspective described in chapter two using a

sequence of quantitative followed by qualitative research. The quantitative data and

analyses were used to inform the qualitative research. Of the six approaches involved

in mixed methods studies 1) sequential explanatory strategy, 2) sequential exploratory

strategy, 3) sequential transformative strategy, 4) concurrent triangulation strategy, 5)

concurrent nested strategy, and 6) concurrent transformative strategy, the first was

chosen as the mixed methods approach for this research (Terrell, 2012). This was

chosen because of its fundamental natural order, flexibility and ability to recognise and

report patterns. The sequencing of this research is described in Figure 3.1.

Figure 3-1. Sequential Explanatory Strategy

The literature review provided information concerning different factors

associated with the health profile of AOP. This was used to inform the next phase of

the research program, which was the development of a survey, designed to provide

information on the health status of different categories of AOP. The survey was also

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designed with a theoretical perspective of the multiple factors potentially influencing

health status and their relationship and impact on short and longer-term health

outcomes. These included, demographic, work, organisational, environmental and

lifestyle factors, all of which are associated with health risks and the design and

evaluation of health risk control solutions. This was then strategically combined with

a thematic analysis of semi-structured interviews for a more nuanced, contextualised

and collaborative understanding of the health of AOP in an ambulance service. It is

about their experience, how they feel and what their interpretation of concepts such as

‘regular rest breaks’ and ‘fatigue’ mean. This is unequivocally connected to the aim of

the research: ‘To explore the health status of ambulance operational personnel and to

develop a conceptual understanding that may inform policy development, management

and future research.’ This design, which was related to the hypotheses, aims and

objectives, creates a blueprint for the research through to a conceptual understanding

of the issue, implications, recommendations and a framework for the improvement of

the health of AOP, and is described in Figure 3.2.

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Research design

Adapted from (Schoonenboom, 2018)

Figure 3-2. Mixed Methods Design

3.1 Quantitative Methods

Data Collection

The principal primary data source of the research was termed the Ambulance

Health Survey 2015 (AHS 2015). The major component of the AHS 2015 originated

from the Australian Health Survey 2011-13 (Australian Government, 2011), and

consent to use components of the questionnaire was permitted by the Australian

Bureau of Statistics in December 2012. The Australian Health Survey 2011-13 was

the prime source of questions for the AHS 2015. There was no known validity and

reliability testing of the Australian Health Survey 2011-13 other than discussion on

how sampling variability and non-sampling errors were considered in terms of data

quality (ABS, 2013a). The majority of these items showed internal consistency in the

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analysis of the AHS 2015 survey with Cronbach’s’ Alpha ranging from 0.540 to 0.938

and Mean Inter-Item Correlations ranging from 0.225 to 0.387. Use of this tool allowed

comparisons to be made between the Australian population and ambulance service

employees and to evaluate the representative nature of the survey findings.

The survey was modified to include questions related to sleep patterns, non-

clinical anxiety and fatigue relevant (with permission) to the ambulance population.

These questions were used in an earlier survey of an Australian ambulance population

(Parker, A.W. & Hubinger, 2003). Despite a comprehensive testing for face validity

by the original designer, an analysis of the reliability of the AHS 2015, showed the

component relating sleep to have low internal consistency with a Cronbach’s Alpha of

0.363 a Mean Inter-Item Correlation of 0.140.

The questionnaire included a personal interest component designed to identify

factors which may have a positive or negative influence when determining the major

health related interests and the required and unmet health needs of AOP. These

included rest breaks, barriers to physical activity and the use of wellness facilities.

These survey components were taken from Tompkins County, New York State, U.S.,

Worksite Wellness Employee Interest Survey (Tompkins County, 2012), and were

reproduced with permission by acknowledgement. This contributed to developing a

conceptual framework of understanding to ensure that efforts in relation to AOP health

are not only placed where needed but addresses the interests of the workers.

Additional survey components related to work-related health culture, job

satisfaction and performance were integrated into the AHS 2015. Four questions

concerning work-related health culture were included. The questions related to how

people stay healthy, individual attitudes and personal perceptions concerning health

whilst working and were chosen to help in understanding the work-related health

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culture in an ambulance service. These questions came from the Lifegain Health

Culture Audit, with permission of the Human Resource Institute, LLC.

The job satisfaction component of the AHS 2015 was taken from the WELCOA

job satisfaction survey (Bellington, 2014), which in 2013 was freely available to be

used with citing. This job satisfaction survey had not been previously validated. These

questions were chosen as the researcher, who has 40 years exposure to the industry,

using psychometric reasoning (ability, attitudes and personality traits) regarded the

components of this job satisfaction survey to represent AOP understanding of job

satisfaction.

To allow a comparison between performance, health status, risk factors and other

elements of the AHS 2015, four questions in regards work performance were also

included in this survey. These questions were related to the previous four weeks before

the survey was undertaken and were taken from the World Health Organisation, Health

and Performance Questionnaire: Revised (WHO, 2010).

All AOP in the ambulance organisation (4,169) were canvassed and asked to

complete the survey. The research examined AOP in three categories (paramedics,

EMDs and supervisor/managers) the majority of whom were shift workers. A sample

of 474 had been calculated as the minimum required for the AHS 2015 (which was

approximately 12% of Queensland Ambulance Service population). The

Commissioner of this ambulance service gave permission for the research to be

conducted and accepted Queensland University of Technology (QUT) ethical approval

- number 14000000936 and provided names, postal and email addresses of the

workforce.

Participants were provided with appropriate information via postal and email,

full consent was obtained using a unique code that will allow future research

comparisons and evaluation and therefore kept individual returns anonymous. The

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survey included demographic descriptors which enabled comparison with known

population characteristics obtained from Australia’s Health Survey 2011-13 (ABS,

2013b). QUTs online survey tool ‘Key Survey’ was used to store the data.

3.1.1 Determining Sample Size

The following assumptions were made (Israel, 1992 Reviewed 2009) in

determining sample size of the study population:

1. Sampling error of ± .05 (this was the range in which the true value of the

population lies).

2. Confidence level was 95% (95 out of 100 samples will have the true population

value within the range of precision of ± .05). This probability of committing a

Type I error (alpha (α) = a false negative) was the same as the level of

significance.

3. The degree of variability was 0.5, which was the maximum variability of the

population.

4. That the salience of the population was very high when the health of AOPs was to

be considered, a self-health check was involved, and the personal information was

reviewed by the participant and overall results published.

5. All workers were invited to participate and could complete the survey and

measures only once but with multiple entry times allowed and equipment was

available at all ambulance stations (this should minimise loss through not having

to travel and encourage participation as a team). Invitees who worked in operations

centres, which are normally at a location with an ambulance station, were

encouraged to utilise the station equipment for self-measures.

6. Reminders were sent out regularly and supervisors were asked to support and

encourage participation.

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7. The AHS 2015 could be completed in three different forms (paper based,

electronically using MS word and on line using QUT’s Key Survey tool) increasing

the flexibility for the participant.

8. That the ambulance population (in terms of age and physical health) are normally

distributed which reduces the probability of volunteer bias (e.g. employees who

value their health will be the only ones to participate).

A simple formula for an estimate of proportions to calculate sample size was

used, (Yamane, 1973) and is described in Equation One.

Equation 1: 𝒏 𝑵/𝟏 𝑵 𝒆 𝟐.

Where (n) was the sample size, (N) was the population size and (e) was the level

of precision. With an adjustment factor of 1.3 and an ambulance population of 4169,

then: Sample size (n) was 4169/1+4169 (0.05)2 = 365*1.3 = 474.

The original database was copied and only the duplicate database was drawn

upon for statistical analysis by SPSS. In that manner, the original data collection was

always available and secure.

3.1.2 Quantitative Data Input

Sixty one survey returns were received via email, with the remainder (602)

completed by respondents using direct entry into the online survey tool. Responses

received by e-mail were entered into Key Survey by the researcher. The results were

imported into SPSS Version 22, where a comprehensive data review took place. First,

a code book was developed, and data was named and screened for errors manually.

Where there was no entry in a field of a record, the missing value field was defined as

‘999’ in the variable view in SPSS. The codebook was continually updated as variables

were computed, and a synopsis of variable categorisation is provided in Appendix F,

Table 8.21.

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The data was then screened for errors and consistency. Apart from manually

checking the data for inconsistencies; histograms, bar graphs, and scatterplots were

used to assist in determining the distribution of the data, a summary of the distribution

of the scores of the data included missing values and the relationship between two

variables. Scatterplots were particularly useful in determining outliers in the data that

may be invalid values (e.g. values that were not possible, i.e. 26 hours of sitting in a

24-hour period) and inconsistent values (e.g. years in ambulance that were greater than

the age of the respondent). Frequencies were calculated for each categorical and

continuous variable, to check minimum and maximum values and whether they make

sense e.g. age and for valid and missing values. An example of this is where a

respondent worked, which was entered using a coding system used by the ambulance

service for station category. The researcher knew there was some confusion in relation

to this coding, so respondents were also asked to indicate their ‘station name’ and in

this manner, station category was able to be checked and corrected if needed.

Advice was also obtained from a statistician and further adjustments were made

to conform to known processes. For instance, ‘0’ should be ‘No’ and ‘1’ should be

‘Yes’. In addition, some of the coding had to be reversed to prevent response bias with

negatively worded items and bring consistency to all items in the survey. For instance,

a response of ‘All of the time’ should indicate a high score and a response of ‘None of

the time’ a low score. Using SPPS ‘Transform’ function, some variables were

collapsed into groups. For instance, age was collapsed into age groups defined by the

Australian Bureau of Statistics and the 17-element job satisfaction scale was collapsed

into five groups as defined by Bellington (2014). Other variables had to be calculated

from the original data, such as Body Mass Index and Blood Pressure, because weight,

height, systolic and diastolic blood pressure were all entered in the data set as a single

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variable. Employment type was categorised into three main types, e.g. EMDs,

Supervisor/managers (Officers in Charge, other supervisors and managers) and

Paramedics (Patient Transport Officers, Student paramedics, Advanced Care

Paramedics and Critical Care Paramedics) using QAS employment categories (QAS,

2015).

3.1.3 Quantitative Data Analysis

Data analysis was conducted using the SPSS Version 22. Types of data and

variables influenced the analysis undertaken. The different categorisation of the

variables is reported in Appendix F. Some of these variables have different

categorisations from the variable originally collected as they have been computed. For

instance, age was collected and categorised as numerically continuous variable. It was

computed into age groups so as comparisons could be made with Australian population

data and ‘age group’ was then regarded as a categorical ordinal variable.

Table 3.1-1 Examples of Different Data/Variable Types Collected

CATEGORICAL/QUALITATIVE Data that has a label (even though it may be coded using numbers) e.g. Gender; Language; Postcode; Occupation.

NUMERICAL/QUANTITATIVE Data that was naturally numbers e.g. weight and height.

Nominal Variables that have labels but no natural ordering such as Educational institutions; marital status; housing tenure.

Ordinal Variables that are a name or a label but have a natural ordering: age group; shift worker; frequency of check‐ups.

Count Number of dependants; time as a paramedic; hours of exercise.

Interval Variables where there was a requirement to rate a level of agreement ‐ exercise regularly (at least 3 times per week)?

Ratio BMI; hip/waist.

Discrete Examples are as above in Nominal, Ordinal and Count – these variables are not continuous.

Continuous Data that takes on any numerical value, not just whole numbers e.g., age; height, distance walked in 10 minutes.

Data was summarised by looking at frequency, mean and standard deviation or

relative frequency of observations in each category. For example, the frequency of

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respondents in each age group. Where possible, these frequencies were compared

against other available data, such as that from the Australian Health Survey results.

Statistical significance (p-value), (α) had a cut-off of 0.05. The cross-tabulation

analysis consisted of analysing every variable against each other variable and was used

to develop knowledge of the individual, work and organisational related factors that

influence the health status of AOP. Pearson’s Chi-square (χ2) test and an Odds Ratio

(OR) were calculated together with a 95% confidence interval. This was the prime tool

used to determine statistically significant associations (SSA) and was reported as, for

example [χ² (12, n = 422) = 39.388, p = .000]. This reads as chi-square, degrees of

freedom and sample size in parentheses followed by the significance level.

Associations that had no statistically significance were not reported. However

contextually important results, even if not statistically significant, for instance those

reported in relation to mental health in AOP, were included in the regression analysis,

to limit bias in the modelling. Odds ratio was only reported when the strength of an

association between variables was important. Chi-square and logistic regression were

also used to adjust for confounding variables. For instance, obesity against intent to

leave, adjusted for gender. The Cochran-Mantel-Haenszel chi-squared test was used

and logistic regression allowed the use of multiple variables at the same time.

Given the large number of variables and cross tabulations, the potential for Type

I and II errors was high. However, the risk of a Type I error was reduced by setting the

level of significance at 95% (α = 0.05). Many analyses reported a level of significance

that was less than 0.001, which reduces the chances of a Type I error even further. The

chances of committing a Type II error were reduced by calculating an appropriate

sample size (that was a sample size that was large enough to calculate a practical

difference, when one truly exists). The data collection was not closed until this sample

size calculation had been exceeded in terms of respondent numbers. Subsequently, a

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regression analysis plan was developed based on the five main constructs reported in

this research. The five dependent (outcome) variables were classified under health

status, chronic disease, organisational symptomology, caring for self and risk factors.

They are further described in Table3.1-2.

Table 3.1-2 Regression Analysis – Dependent Variables

Dependant Variables

Health Status

Chronic Disease

Organisational Symptomology

Caring for self

Risk Factors

SR Health Asthma Job satisfaction Fatigue Back injuries

MHD CVD Health culture Sleep Overweightness

K10 Cancer Consider leaving

BtE - Time

BP

Disability Diabetes Rest Breaks BtE - Energy

Exercise

LTC Arthritis Hours worked

Sitting

≥ three CD

Diet

Abbreviation: SR = self-reported, MHD = Mental health disorder, K10 = Kessler Psychological Distress Scale, LTC = Long term condition, CD = Chronic disease, BtE = Barriers to exercise, BP = blood pressure, CVD = cardiovascular disease.

Each of the independent variables was assessed for statistically significant

associations with the dependant variable and included in the regression analysis plan

in Appendix D if significant or important. Effect size was also calculated using Phi or

Cramer’s V correlation coefficients and the inclusion criteria was included in the

regression analysis plan. However, one last step was needed to bring these independent

elements into context. The researcher went through every included independent

variable and asked the question of whether this independent variable could possibly

predict the dependant variable based on the knowledge obtained from the literature

review. For instance, was job satisfaction a predictor of a long-term condition? The

final plan therefore included elements that were within the specified effect size and

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variables that may predict the dependent variable, or were contextually significant,

even though the effect size was small.

Internal consistency (reliability) was tested using Cronbach’s Alpha and where

the number of scales were less than 10, the Mean Inter-Item correlation was also

considered as Cronbach’s Alpha is sensitive to scales with fewer than 10 items (Briggs

& Cheek, 1986). This is outlined further in section 3.3.

Variables such as gender, age, level in the organisation were potential

confounding variables associated with health status. Gender, for instance was related

to the prevalence of cardiovascular risk factors and to the three population sub groups.

Ignoring gender in the analysis leads to a bias in the results and it was therefore

important to analyse males and females separately. Gender, age and employment type,

were included in all regression modelling and station category (workload), shift work,

education and relationship status where included were there was a significant effect

size, or they were contextually significant e.g. diabetes was not regarded as statistically

significant in developing the regression plan as it had a Phi < 0.30. However it was

included as it has an evidence based relationship to CVD (Kannel & McGee, 1979;

Schnell et al., 2019).

3.2 Qualitative Methods

This phase of the research adopts a non-mathematical approach that focuses on

the understanding people create from the experience of the work, knowledge of the

outcomes of the literature review and quantitative analysis of the AHS 2015 data. It

was hoped that it would provide a holistic description of how this research can

contribute to organisational processes which lead to the improvement of the health of

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AOP and was directly related to one of the sub questions: ‘What strategies may lead

to the improvement of health?’

This research used a quantitative/qualitative sequential model which led to a

more effective understanding and appropriate strategic approach for the improvement

of health of operational personnel in an ambulance service. The qualitative research

design used an ethnographical component (a portrait of ambulance operational

personnel) and combined this with an understanding of the organisational culture that

may affect this portrait. Consequently, this led to the development of a semi-structured

interview approach to gather the qualitative data necessary for the thematic analysis.

The semi-structured interview approach was approved by the Queensland University

of Technology’s, Higher Research Ethics Committee and included an approach email,

a consent form, a participant information sheet and semi-structured interview

questions (Appendix E).

Sampling considerations included purposive sampling to enhance data quality

by selecting those who understood the ambulance organisational processes, selecting

productive people with insight, identification of others who may contribute,

availability, time and resources. This resulted in the use of three sampling techniques

which included convenience, opportunistic and snowballing. A convenience sample

was suggested by the ambulance service and three communications personnel were

opportunistically recruited, one of whom was an operations room supervisor. Nine

interviews were conducted, and five of eight participants identified an additional

contributor. This person was interviewed as the researcher felt it would make a

significant contribution to the discussion. Two interviews involved executives with

expertise in human resources and resource capability, one interviewee was a director

with a speciality in education, two others were managers (with specialities in OHS and

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counselling) and one was a working paramedic. All interviewees, except for EMDs

and the OHS manager, originated from a paramedic background. This qualitative

approach has several characteristics (Merriam, 2002), such as:

1. Understanding the meaning of participants’ perspectives using their language

rather than some pre-existing theory.

2. Exploring the real-world ambulance setting and conscience if there was even

such a construct.

3. The context that was embedded in the data gathered and the influence that has

on the outcomes of the research.

4. A holistic understanding of the picture presented by the data and the ambulance

participant’s perspectives.

5. The researcher was the data collector rather than a survey and tries to

understand through talking and observing the ambulance participants. This

allows flexibility of the guided interview protocol and allows the interviewer

to follow leads and directions which might further assist to answer questions.

6. It was an attempt to understand how improvements can be made, why we need

to make improvements and under what circumstance should these

improvements be made.

7. An attempt to build on the theory and was abstract in nature as it measures

meanings rather than quantity, amount, intensity or frequency.

8. The data gathered was context specific as it was based on the semi-structured

interview questions.

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9. The semi-structured interview approach allows for flexibility and follow up

based on unexpected comments.

10. The selection was primarily purposeful, and the participants are co-contributors

to the outcomes of the research.

11. The meanings captured will include the perspective of the researcher who has

already interpreted the literature and the quantitative data collected.

The advantage of thematic analysis was that it allows understanding of the

interview data which is underpinned by their knowledge and informs the emergence

of themes. Themes were impacted by the researcher’s thinking about the quantitative

data analysis and the literature review and creating emerging links as the researcher

understands them (Green, 2013).

3.2.1 Qualitative Data Collection

This second phase of the research program used a semi-structured guided

interview data collection method to identify and make recommendations regarding

potential health and wellbeing programs for AOP. Personnel who participated in the

semi-structured interviews, included stakeholders from the ambulance service such as

managers, supervisors, paramedics and EMDs.

Initially, the ambulance service provided recommendations for involvement in a

focus group. The ambulance employees suggested by the ambulance service worked

in different locations, some worked shift work in both the communications centre and

on road as a paramedic, whilst others were executives. It proved impossible to gather

this group together and ethics approval was requested to conduct this qualitative

research using semi-structured interviews. Semi-structured interviews were chosen as

this process allows a mix of structured and unstructured questions including probing

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the participant on points of interest that were raised. Whilst the questions were

predetermined and overall topics for discussion were the same for all participants, the

order of questions varied depending on the responses of the participants. A deliberate

snowballing technique was used with each participant to identify others who may be

worthwhile interviewing. Face-to-face interviews were conducted and whilst it was

recognised that there may be conversational diversions as a result, it was considered

that this may also provide valuable information. It was also known that the timing,

question number and level of detail of the interview should consider the participants

work commitments and availability and hence the interview was scheduled for a one-

hour period. The documentation in regards this process is contained in Appendix E.

Initially, proposed participants were canvassed from the Research and

Evaluation Unit of the ambulance service and selected from individuals who expressed

an interest. Whilst there was some argument in relation to credibility based on the

convenience participants, this method was chosen based on the time and location of

participants. Selection of participants by the ambulance service involved personnel

with a variety of experiences and understanding of the operation of the service and

who can provide meaningful and non-biased opinions in relation to policy of varying

employment types. Each potential participant was emailed using the higher research

ethics committee (HREC) approved email, a participation information sheet and a

consent form. Participants were followed up by phone and email to arrange interviews

in person, which were conducted in the latter part ofn2017 and early 2018 and recorded

for transcription.

3.2.2 Qualitative Data Analysis

Thematic analysis was used because of its fundamental nature, flexibility

and ability to recognise and report patterns in the data (Boyatzis, 1998). This

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particular qualitative data analysis technique captures a theme or something

important about the data and allows the researcher judgement in determining

the theme which was not necessarily dependant on quantitative measures (e.g.

no participants talked about this important issue). Thematic analysis also

allows an inductive approach via means of coding the data without having to

fit it into a pre-existing framework. That is, the thematic approach used here

was data driven (Patton, 1990). A six-step method was used to analyse the

interview data and is presented in Table 3.2-1.

Table 3.2-1 Stages of the Thematic Analysis

Stages Description

1. Data familiarisation

Transcribe recorded interviews, checking the transcripts by the researcher and noting ideas.

2. Coding Coding data features, collating and weighing data.

3. Looking for themes Collating codes into potential themes.

4. Reviewing themes Review and check themes are related to codes and transcribed data – develop a thematic map.

5. Defining and naming themes

Generate clear definitions and names for each theme.

6. Interpreting and discussing

Extract examples and relate back to the literature and data analysis.

Adapted from (Braun & Clarke, 2006)

Reliability, validity and generalisability are another matter, as there was ongoing

debate about its relevance to qualitative research (Noble & Smith, 2015). However,

Lincoln and Guba (1985) offer an alternative, which consist of: truth value (validity),

consistency (reliability) and applicability (generalisability). This approach was used to

describe the strategies used in the qualitative research of this mixed methods research

to determine truth value, consistency and applicability, and is described in Table 3.2-

2.

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Table 3.2-2 Strategies used for Enhancing the Credibility of the Qualitative Research

Truth value Reflection of researcher’s perspectives:

o Discussion and decisions documented.

o Debriefing with two supervisors and a peer to uncover

biases/assumptions that may have influenced findings.

Representative nature of the findings:

o The nine participants were willing to share their

experiences/thoughts on the findings of the LR and AHS 2015.

o Semi-structured interview recordings allowed for revisiting the

data and remaining true to the interviewee’s accounts.

o Use of rich extracts from participants to assist in determining if

the final themes were true to their comments

o Clear presentation of participant’s perspectives.

o Participants invited to comment on the research findings.

Consistency Auditability:

o Clear and transparent description of the research process from

the outline, development of methods and reporting of findings.

o Use of the same semi-structured questions for all participants.

o Using notes from interviews and comments on transcribed

interviews to maintain cohesion of research objectives.

o Emerging themes discussed with a supervisor who had

qualitative research expertise, which allowed assumptions to be

challenged.

Applicability Application of findings:

o A variety of participants were selected to give meaning to the

results across the broad spectrum of ambulance personnel with

a multiplicity of experiences who understood the real-world

context.

Abbreviations: LR = literature review

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3.3 Reliability and Validity of the Survey Instrument

A small pilot study involving five paramedics was conducted in April 2014, to

help clarify the survey and the research conduct and to ensure relevance to the context

of the research. The intent of the survey and its construct was reported by the five

participants as positive. The detail included in the draft survey was described as ‘too

much’. Therefore, the draft was reviewed against the feedback, research question and

objectives of the study and reduced considerably.

3.3.1 Validity

Whilst face validity was only one measure of validity and is known as

superficial, because it is about what it appears to measure not actually what it does

measure. However, it may have a strong relationship to validity. For instance, this

research considered face validity to have a strong relationship as it appears to measure

what was considered important to AOP. Face validity is becoming increasingly

important as it involves the user in involvement of the instrument and its content

(Connell et al., 2018). In April 2015, a second pilot study to assess the face validity of

the revised questionnaire (now known as the AHS 2015) was conducted using

paramedics, one supervisor and an EMD (n = 9) as these would be representative of

primary users of the survey. The participants were made aware of the hypotheses, aims,

objectives and research questions for the AHS 2015 and the meaning and importance

of determining its validity. They were also advised on the procedures to be followed

for self-measures. They were requested to review the participant information sheet,

recruitment and reminder letters and the AHS 2015, followed by completion of the

questionnaire. Their feedback response was guided by five questions provided on

completion of the survey, which were provided in a simple yes/no format with room

for comments. These questions are outlined in Table 3.3-1.

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The survey could be completed by using the Key Survey Tool on-line, Microsoft

Word on a computer and manually after printing the questionnaire. A flexible tape

measure and LifePak 12 monitor/defibrillator were provided for self-measurement and

participants were asked to provide feedback on the efficacy of the self-measure

instructions. Three participants were assigned to complete the survey in each of these

mediums, with completion time estimated at 30 minutes. The researcher left the area

while the pilot trial participants completed the survey and self-measures. This was to

simulate realistic completion.

Table 3.3-1 Face Validity Questions

Questions Response Comment

1 Is the questionnaire valid – is the questionnaire

measuring what it is intended to measure?

Yes

No

2 Does it represent the content? (Does this formal

survey instrument make explicit the type of

information that is required)

Yes

No

3 Is it appropriate for the sample/population? Yes

No

4 Is the questionnaire comprehensive enough to collect

all the information needed to address the purpose and

goals of the research?

Yes

No

5 Does the instrument look like a questionnaire? Yes

No

Positive responses were received from all five questions with all participants

from the three mediums. Pilot trail participants who completed the survey manually,

together with conducting self-measures, felt that the time for completion was too long

(> 30 minutes) which will potentially reduce returns and introduce a non-response bias.

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The other six participants completed the survey and self-measure within 30 minutes.

The instructions for self-measures were gauged to be adequately efficacious.

3.3.2 Reliability

Reliability was the second element considered in the evaluation of the AHS

2015. Validity has already been discussed in terms of the AHS 2015 in relation to

appearing to measure what it was intended to measure. Reliability, on the other hand,

is concerned with the ability of the AHS 2015 to measure consistently (Golaszewski,

Hoebbel, Crossley, Foley, & Dorn, 2008). There are many test items in the AHS 2015,

with varying scales and concepts and Cronbach’s Alpha and the mean Inter-Item

Correlation were used to test reliability of the AHS2015. These results are presented

in Table 3.3-2, together with the number of items in each scale. It is common with

scale items less than 10 to find low Cronbach’s Alpha (0.5) and therefore the Mean

Inter-Item Correlation may be more appropriate in a range of 0.2 to 0.4 (Briggs &

Cheek, 1986). Because of the lack of known validity of the Australian Health survey

2011-13, it was tested for reliability with the majority of items showing internal

consistency.

The questions on works hours, fatigue, non-clinical anxiety and sleep were the

same questions used in a 2003 review of the health and work of AOP (Parker, A. W.

& Hubinger, 2003). At that time, face validity of these questions was established with

an advisory group and pilot tested with 22 operational officers. The anxiety and fatigue

constructs in the AHS2015 showed internal validity with Cronbach’s Alpha of 0.507.

However, sleep and hours worked showed only a weak internal consistency.

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Table 3.3-2 Cronbach’s Alpha and Mean Inter-Item Correlations - AHS 2015

Construct Scale Items Cronbach’s Alpha Mean Inter-Item Correlations

Education/Quals 2 0.443 0.183 Shifts (Y/N) 2 345.242 0.995 Consider leaving 12 0.808 0.229 SR health 2 23.988 0.945 K10 10 0.796 0.331 Mental meds 6 0.717 0.318 Self-BMI 2 0.261 0.537 BMI grouped 4 0.426 0.124 BMI measured 5 0.399 0.149 Bodily pain 2 0.770 0.649 Disability 14 0.706 0.165 Exercise hours 15 0.540 0.156 Sitting hours 7 0.702 0.331 Smoking 3 0.648 0.296 Diet 2 0.463 0.316 Alcohol 19 0.263 0.048 Asthma 4 0.745 0.609 Cancer 3 0.471 0.225 CVD 8 0.579 0.138 Arthritis 3 0.938 1.000 Diabetes 13 0.870 0.387 Sight/hearing 12 0.509 0.094 LTC 24 0.765 0.113 MHD 14 0.099 0.252 Stressors 19 0.700 0.141 Overweight 10 0.590 0.321 BP 5 0.594 0.540 Rest breaks 10 0.421 0.365 Wellness incentive 2 0.695 0.551 BtE 15 0.824 0.236 Wellness when 4 0.319 0.108 Health culture 7 0.820 0.440 Job satisfaction 19 0.779 0.316 Anxiety/fatigue 14 0.507 0.114 Hours worked 2 0.058 0.747 Performance 4 0.005 0.012 Sleep 11 0.363 0.140

Abbreviations: Quals. = qualifications; SR = self-reported; K10 = Kessler psychological distress scale; Meds = medications; BMI = Body Mass Index; CVD = Cardiovascular disease; LTC = Long term condition; BP = Blood Pressure; BtE = Barriers to exercise.

Legend: Internal consistency.

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Research design

The personal interest component of the AHS 2015, which was taken from a

Tompkins County Worksite Wellness Employee Interest Survey included rest breaks,

use of wellness facilities and barriers to physical activity. These three constructs

showed internal consistency with Cronbach’s Alpha ranging from 0.421 to 0.824 and

Mean Inter - Item Correlations ranging from 0.114 to 0.440. The Tompkins County

Survey had not been previously validated.

The four questions on health culture was taken from the Lifegain Health Culture

Survey and according to (Golaszewski et al., 2008) a high Cronbach’s Alpha of 0.934

suggests considerable redundancy within the constructs. In the AHS 2015 the

Cronbach Alpha Coefficient was 0.820.

The job satisfaction component of the AHS 2015 was taken from a previously

non-validated job satisfaction survey. The Cronbach’s Alpha was 0.779 and the Mean

Inter-Item Correlation was 0.339, showing strong internal consistency.

The performance questions used in the AHS 2015 did not show any internal

consistency despite the WHO Health and Performance Questionnaire being valid and

reliable. Given the previous reliability and validity testing it was felt to still include

these components as a way of seeking information about how the health status of AOP

impacted on the performance of the individual.

3.3.3 Administration of the Survey

Following the Commissioner’s announcement of the research in March 2015,

the survey was posted and e-mailed by the researcher to all AOP in April 2015.

Recruitment details and participant information concerning the project were included.

Participants had the option of completing the survey in one of three ways:

1. By completing and making a hard copy and mailing back to the researcher.

125


2. The survey instrument had been designed using MS Word developer and could be

completed electronically and returning to the researcher via email.

3. Completion via Key Survey.

Several reminder letters were sent to respondents who had not replied to the

initial and subsequent requests to complete the survey, prior to closure of data

gathering in August 2015.

3.4 Ethical Considerations

This research required ethics approval and the ambulance service commissioner

indicated his approval for the research when approved by the QUT ethics committee.

The AHS 2015 participant information, an invitation letter, a recruitment email and a

reminder letter were developed and approved by the Queensland University of

Technology’s Higher Research Ethics Unit: Ethics approval number 14000000936 on

the 18 February 2015 (Appendix A).

A major potential ethical issue associated with the research was that the

researcher was a senior officer with the ambulance service which may influence

participant’s involvement. This was identified early in the research and overcome by

a major change in the methodology, in which the researcher was dissuaded from

supervising the survey, self-measures and taking serum for biological markers. The

original research design was reviewed and replaced by a cross sectional research

design, include self-measures, but no biological markers and which allowed the

respondents to remain anonymous. Consent for involvement was obtained using a

unique code for de-identification with the potential for comparison of data in future

surveys. Other ethical issues that were overcome include:

1. Confidentiality, security and anonymity of data – use of a code and Key Survey.

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Research design

2. Some of this data may be relevant to employment standards and access to the data

may have implications for the participant – data was not shared with the ambulance

service.

3. Sampling techniques and identification of participants – all AOP were canvassed

and most respondents replied via key survey. Those that used email or mail had

their data entered into Key Survey and once this had been validated, these surveys

were stored in a supervisor’s office and destroyed after checking and analyses had

been completed.

4. The candidate was a senior officer of the ambulance service and thus any suggestion

of enforcement was carefully avoided. This was managed by using the following

strategies:

a. Voluntary nature and confidentiality of the information received.

b. The research was about caring for paramedics, their health, concerns and

issues.

c. Assurance of confidentiality and ethical conduct.

d. Assurances that participation in this research will not affect a participant’s

relationship with the ambulance service or QUT.

e. The researcher would answer any questions.

f. Assurance of ability to withdraw and have all data deleted. No respondents

asked to be withdrawn.

g. Ability to not answer any question.

h. Ethical concerns can be directed to the research ethics unit.

i. Assurance of the access to reports and publications post analysis.

j. The researcher ensured all participants received a copy of the Privacy

Statement and how the researcher will comply with the Information Privacy

Principles.

Only one complaint was received, and this was managed by the researcher. This

complaint was from a paramedic who thought that a completed survey form that had

127


been completed by the researcher to assist potential respondents with any questions

they may have with the survey, matched the identity of an individual at an ambulance

station. A number of emails were received that asked to be removed from the

reminders and this was accomplished by a simple deletion of email addresses from the

reminder list.

3.5 Conclusion

This chapter outlines the research design of this mixed methods research. It

includes a discussion on collecting quantitative and qualitative date, sample size,

quantitative data input and analysis. (e.g. demographics, descriptive, inferential and

regression). Reliability and validity of the quantitative and qualitative data were

discussed, as well as the use of a thematic analysis technique and ethical

considerations. It provided insight into the development of the AHS 2015 and the

qualitative data collections and their relationship to the aims of the research.

Importantly, this chapter provides evidence of the methodological rigor applied to this

research and how the needs of participants and partners (e.g. Queensland Ambulance

Service and Queensland University of Technology) were satisfied.

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Results

4 Results

4.1 Introduction

The AHS 2015 survey data was used for the quantitative component of this

mixed methods research. The AHS 2015 was a large survey comprising 154 health

related questions that were developed during 2013-14 and administered in 2015. A

large component of the AHS 2015 consisted of questions from Australian Health

Survey 2011-13, enabling comparison where appropriate (e.g. using the Kessler K10

Psychological Distress Scale). The quantitative analysis was completed in two stages:

1) demographic and descriptive analysis and 2) inferential analysis.

The aim of the research was to explore the health status of ambulance operational

personnel and to develop a conceptual understanding that may inform policy

development, management and future research. The survey was offered to all

operational personnel (n = 4,129) in the Ambulance Service and achieved a response

rate of 16% (n = 663). The survey was designed to identify the characteristics of the

participants along with indicators of their health status, work status, perceptions of

work-related health culture and stressors, risk factors and health promoting activities.

The survey was applied to AOP in three occupational categories, as follows:

1. Ambulance Operational Personnel who work twenty-four-hour shift patterns,

have an intermittent and unpredictable work (response) pattern, work in an

unsupervised and highly stressful environment with critically ill patients and

highly distressed patients. Their work is often scrutinised by other health

professionals, supervisors, patients and bystanders and is subject to rigorous

clinical governance processes. They work in often inconvenient and unfamiliar

129


environments and they are subject to complaints, verbal and physical violence and

intimidation. They have little autonomy in their work, eat intermittently and often

complete long shifts without rest breaks (Mahony, 2001).

2. Operational supervisors/managers also respond to clinical cases although they

mainly provide support, lead and manage paramedics and EMDs from a clinical

and performance perspective. They tend to work more regular hours compared to

paramedics and EMDs.

3. Emergency Medical Dispatchers are not clinically trained and work in a call

centre environment at the interface between the public and operational personnel.

They can be subject to verbal abuse and emotional impacts from concerned

relatives and friends of patients seeking help. Both operational

supervisors/managers and EMDs generally have a sedentary component to their

work, which is known to have an inverse relationship with health (Tremblay et al.,

2010).

This is the first known research that attempts to identify the health of AOP,

including the personal, environmental and organisational influences on their health,

and how negative influencers (e.g. shift work) may be mitigated.

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Results

4.2 Stage One – Demographic and Descriptive Analysis

Demographic Profile

A total of 663 responses were received of which 66.8% (n = 443) were

operational paramedics, 20.8% (n = 138) operational supervisors and 12.4% (n = 82)

EMDs which reflects the proportion of each group within the service.

The overall mean age of respondents was 37 years with females having a mean

age of 34.8 (SD = 9.8) years and males a mean age of 42.1 (SD = 11) years. The mean

age of major employment types was: Emergency Medical Dispatcher 37 (SD = 9.6)

years, Advanced Care Paramedic 37.1 (SD = 11.0) years, Critical Care Paramedic 39.7

(SD = 7.8) years, Officer in Charge/Supervisor 46.1 (SD = 9.7) years and managers

49.9 (SD = 7.7) years.

Figure 4.1 demonstrates the age profile of men and women in the sample in

comparison with the age profile of the Ambulance service as a whole (QAS, 2015).

Figure 4-1. Age: Respondents vs. Ambulance Service.

0

5

10

15

20

25

20‐24

25‐29

30‐34

35‐39

40‐44

45‐49

50‐54

55‐59

60‐65

65+

Percentage of Total

Age Groups

% AAS PopulationFemale

%AAS PopulationMale

% RespondentFemale

% Repondent Male

131


The profile indicates a greater number of females in the 25-29 age group than

males, which was consistent with the ambulance service profile. The respondent

population age was representative of the ambulance population age overall and when

considered in terms of gender. There was more older males and younger females in

the service generally. Although 63.7% of respondents were male, the ratio of males to

females varied considerably within the different employment groups: EMDs (29:71),

Advanced Care Paramedics (62:38), Critical Care Paramedics (83:17) and

Supervisor/managers (81:19). There is an evolving gender balance of AOP of the

ambulance workforce, and this was related to females being the majority of university

graduates in paramedicine. Females were more likely to be over represented in the <

4 years’ service category [χ² (6, n = 663) = 91.239, p = .000] than males. However,

males tended to be over represented as years in ambulance increased. Age and gender

are little discussed in this section as there are multiple associations with other variables

that are included throughout this chapter. For instance, in the ‘Qualifications’ section

the proportion of females and males with undergraduate degree is explained. And in

the ‘Cardiovascular disease’ section it was shown that CVD grew with increased years

of service.

Relationship Status

A high proportion of respondents (82.7%) were in a stable relationship (e.g.

married), 9.2% had never been in a relationship, 5.9% were separated and 2.1% were

divorced.

Qualifications

Table 4.2-1 identifies the highest level of qualification held by the respondents.

The proportion with university qualifications (57.9%) reflects not only the entry

qualifications but also qualifications obtained prior to, and after undertaking

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Results

ambulance work. The proportion with degrees was inversely related to age and this

reflects the relative recency of the move to university entrance for paramedics (see

Table 4.2-2). Table 4.2-3 reflects the differing qualifications of AOP. Additionally,

females were more likely to have an undergraduate degree. However, a higher

proportion of males than females had postgraduate qualification, [χ² (4, n = 663) =

19.556, p = .001] and appointments to supervisory roles.

Table 4.2-1 Highest Qualification by Age

Trade Diploma Degree Higher degree

Other Total

Age

Gro

ups

(Yea

rs)

<20 0 0 1 1 0 2 21-25 0 7 68 6 3 84 26-30 7 13 57 18 4 99 31-35 2 25 31 12 9 79 36-40 5 38 27 21 3 94 41-45 3 38 27 19 6 93 46-50 4 30 24 23 6 87 51-55 3 28 10 16 7 64 56-60 2 23 11 4 5 45 61-65 1 6 3 4 0 14 >65 0 1 1 0 0 2

Total 27 209 260 124 43 663

Table 4.2-2 Highest Qualification by Employment Type

Diploma % Degree % Higher Degree % EMD 44 12 5

Paramedic 28 49 19 Supervisor/Manager 9 6 7

Total 81 67 31

Table 4.2-3 Qualifications

n % Trade certificate 27 4.1

Diploma 209 31.5 Undergraduate degree 260 39.2

Higher degree 124 18.7 Other qualification 43 6.5

Total 663 100.0

133


Years in Ambulance

The mean number of years working in an ambulance service was 12.5, (Range

1-46, SD 10.1) and the mean number of years of shift work was 13.3, (Range 1-46, SD

9.533).

Station Classification

Ambulance stations are categorised based on their human resources and extent

of coverage as shown in Appendix C, Table 8.1. This in turn reflects workload based

on Unit Hour Utilisation (QAO, 2013). For example, a Category One station is a single

officer station, while a category five station has a minimum of 17 officers associated

with two officer crews working across 24/7 periods.

The distribution of respondents to the survey by station category (see Table 4.2-

4) indicates that a majority worked at category five stations (54%) with 24/7 schedules

or non-station locations (22%). Respondents at non-station locations included EMD’s

and certain operational supervisors.

Table 4.2-4 Respondent Location by Station Classification

Station Classification n % One 27 4.1 Two 57 8.6

Three 34 5.1 Four 38 5.7 Five 361 54.4

Non-station 146 22.0 Total 663 100.0

Employment type

Predominantly, most respondents were full time permanent employees of the

ambulance service (91.4%), 3.5% were permanent part time, 2.9% were casual and the

remainder (2.3%) describe themselves as other.

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Results

Employment categories

Employment categories consisted primarily of Advanced Care Paramedics

(ACPs) 55.5%, Officer in Charge/Supervisor (OIC/Sup) 17.0%, EMD 12.4%, Critical

Care Paramedics (CCP) and a smattering of other types as described in Table 4-5. For

the purposes of this research, these employment categories were classified into EMDs,

paramedics and supervisor/managers. This is shown in Table 4.2-5.

Table 4.2-5 Respondent Employment Categories

Employment Type AHS 2015 Employment Type Grouped

n % n %

EMD 82 12.4 EMDs 82 12.4

PTO 16 2.4 Paramedics 443 66.8

Student 13 2.0

ACP 368 55.5

CCP 46 6.9

OIC/Sup 113 17.0 Supervisor/Manager 138 20.8

‘M’ Scale 23 3.5

Dir 1 .2

Exec 1 .2

Total 663 100.0 Total 663 100.0

Abbreviations: EMD = emergency medical dispatcher; PTO = patient transport officer; ACP = advanced care paramedic; CCP = critical care paramedic; OIC = officer in charge; Sup = supervisor; “M’ scale is a middle manager in operations; Dir = director; Exec = executive.

Figure 4.2 shows a comparison of the proportion of appointees across the three

major job categories in the survey sample and the overall ambulance service

workforce, which indicated similar proportions between the two groups. It shows a

higher representation of the EMD and supervisor/manager survey respondents.

135


Figure 4-2. Employee Categories: Respondents vs. Ambulance Service.

4.2.1 Health Status of Individual Workers

The second aspect of the survey sought to identify indicators of the health status

of individuals. Indicators included type and level of engagement with health services,

individual perceptions of health status, and presence of illness or disability.

Type and level of engagement with personal health services provider

Of all respondents, 60.8% reported having regular check-ups with a General

Practitioner (GP) and 87 and 53% having their blood pressure and cholesterol checked

respectively in the last 12 months.

The rate at which respondents sought medical advice varied with their

educational status. Males with undergraduate degrees were less likely to have check-

ups with a GP, [χ² (12, n = 422) = 39.388, p = .000] and to have cholesterol checks [χ²

(8, n = 422) = 38.407, p = .000] than those with lower education. In contrast, as years

in ambulance and years of shift work increased, males and female respondents reported

having more health, blood pressure and cholesterol checks.

12.4

66.8

20.8

9.7

77.1

10.7

EMD OPERAT IONAL SUP ERV I SOR /MANAGER

PER

CEN

TAGES OF EA

CH GROUP

Respondnents QAS Population

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Results

General health status

The findings indicated that a lower proportion of respondents reported their

health to be excellent by comparison with the Australian population (ABS, 2012e). A

higher proportion also reported fair or poor health. The proportion reporting a lower

health status was twice as high than found in an earlier health related survey of AOP

(Parker, A. W. & Hubinger, 2003) (see Table 4.2-6).

Table 4.2-6 Self-Reported Health: Respondents vs. Other Sources

Excellent Very

good

Good Fair Poor Total

Australian Population 24.4 38.1 28.8 7.3 1.5 100.0

Respondents 8.6 35.0 38.8 15.1 2.4 100.0

(Parker, A.W. &

Hubinger, 2003)

25.5 ….55.6…. 17.8 1.1 100.0

In addition, a more detailed analysis showed that there was a relationship

between self-reported health status and risk factors, including accessing of health care.

Males with poor self-reported health consumed greater than 20 alcoholic drinks per

week [χ² (16, n = 447) = 28.312, p = .029] and had increased their alcohol intake in the

previous 12 months [χ² (8, n = 411) = 22.445, p = .004]. Similarly, as health status

decreased, systolic blood pressure increased to more than 140mmHg [χ² (12, n = 644)

= 35.729, p = .000] and diastolic blood pressure to more than 90mmHg [χ² (8, n = 644)

= 35.138, p = .000]. However, this relationship was not as strong when blood pressure

(systolic/diastolic) was analysed with health status. As health status decreased, obesity

[χ² (8, n = 628) = 101.954, p = .000] and overweightness [χ² (12, n = 663) = 119.402,

p = .000] increased. Other associations with decreasing self-reported health are shown

in Appendix C, Table 8.2.

137


The second element in the reporting of health status was the identification of

people who have evidence of chronic illness, (both mental and physical).

Mental Health, Anxiety and Stressors

Results suggest 38.7% of respondents reported having mild to severe

psychological distress in accordance with the Kessler Psychological Distress Scale

(K10) (Coombs, 2005) with the severity of their symptoms being rated using a five-

point Likert scale of none too severe. As shown in Table 4.2-7, comparison of the rate

of psychological distress in the ambulance service was less than that of the Australian

population (ABS, 2012d).

Table 4.2-7 Kessler Psychological Distress Score: Respondents vs. Australian Population

Mild % Moderate % Severe %

Australian population 68.0 19.5 8.0

Respondents 24.4 9.2 5.1

As health status decreased, the number of respondents who reported

psychological distress increased, [χ² (12, n = 663) = 96.695, p = .000], (OR = 2.389,

95% CI 1.7:4.8, p = <0.05). Respondents with poor self-reported health were more

likely to experience anxiety [χ² (8, n = 560) = 28.938, p = .000] than those with good

self-reported health.

In addition, 98.2% of respondents reported feeling depressed at some level, in

the previous four weeks. Males with a diagnosed mental health condition and an

increased K10 score reported constant anxiety at work more frequently than females

[χ² (3, n = 422) = 25.463, p = .000], with that anxiety rated as moderate to high [χ² (2,

n = 352) = 28.573, p = .000]. Additionally, 11.3% of all respondents had a diagnosed

mental health condition. This included depression (7.5%), generalised anxiety disorder

138

Results

(3.5%) and post-traumatic stress disorder (PTSD) (2.6%). Medication for mental

health conditions was taken by 11.2% of respondents.

The effects of job insecurity (e.g. the loss of a valued job, demotion and career

insecurity) as a result of organisational restructuring or downsizing has been known to

increase stress and create stress reactions that can be described as somatic,

psychological and behavioural (Sverke, 2006). Therefore, personal and family

stressors were examined in relation to mental health. Males with a diagnosed mental

health condition were more likely to report job insecurity [χ² (5, n = 422) = 30.862, p

= .000] than those without a diagnosed mental health condition, whilst males, [χ² (15,

n = 422) = 64.714, p = .000] and females, [χ² (9, n = 241) = 24.842, p = .003] with a

high K10 score were more likely to report divorce and job insecurity.

Males, [χ² (6, n = 422) = 43.349, p = .000] and females, [χ² (4, n = 241) = 24.818,

p = .000] with a diagnosed mental health condition were more likely to report a

physical disability compared to those who did not have a diagnosed mental health

condition. Females with a diagnosed mental health condition [χ² (2, n = 241) = 7.730,

p = .021] were more likely to report arthritis. As the K10 score increased, females with

asthma increased [χ² (9, n = 27) = 17.991, p = .000]. In addition, males, [χ² (1, n = 422)

= 5.450, p = .020] and females, [χ² (1, n = 241) = 16.408, p = .000] who had a diagnosed

mental health condition were more likely to have asthma than those without a

diagnosed mental health condition. Females in other paid employment or who did

voluntary work were more likely to have a diagnosed mental health condition [χ² (5, n

= 241) = 12.927, p = .02] than males in the same category. Males with thoughts of

leaving [χ² (4, n = 422) = 24.841, p = .000] were more likely to experience a diagnosed

mental health condition than females. Other associations between diagnosed mental

health conditions and job satisfaction are shown in Appendix C, Table 8.3.

139



p = .002] were more likely to report a cancer diagnosis as their K10 score increased.

However, only males who had been diagnosed with cancer were likely to report having

a diagnosed mental health condition [χ² (1, n = 422) = 9.551, p = .002]. CVD was

associated with mental health and a higher K10 score was associated with an increase

in cardiovascular conditions [χ² (3, n = 663) = 8.737, p = .001]. Males, [χ² (3, n = 422)

= 36.364, p = .000] and females, [χ² (3, n = 241) = 12.927, p = .005] with thoughts of

leaving ambulance employment were more likely to have a higher K10 score compared

to those without thoughts of leaving.

There were several statistically significant associations between the K10 score

and alcohol. As the K10 score increased, there was a concomitant increase in the

number of males who consumed greater than 20 drinks per week [χ² (12, n = 290) =

31.242, p = .002]. In addition, females with moderate psychological distress consumed

five or more alcoholic drinks at least twice a week [χ² (18, n = 43) = 32.672, p = .004]

and as psychological distress worsened, respondents who had increased alcohol

consumption over the previous year, also increased [χ² (6, n = 411) = 34.468, p = .000].

In addition, as the K10 score increased, males [χ² (9, n = 422) = 82.817, p = .000] and

females [χ² (9, n = 241) = 37.697, p = .000] who experienced frequent to constant

anxiety were more likely to describe that anxiety as moderate to high [χ ² (2, n = 352)

= 28.573, p = .000] than those who experienced moderate to mild fatigue.

Two questions in the AHS 2015 asked about family and personal stressors.

Males with a diagnosed mental health condition [χ² (4, n = 422) = 25.565, p = .000] or

a higher K10 score [χ² (12, n = 422) = 67.999, p = .000] reported a serious accident,

death or a serious disability of a family member or a close friend. Forty-nine-point

nine percent of respondents reported one or more family stressors (serious illness or

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Results

accident, death of a family member or close friend, mental illness or serious disability)

and 17.5% reported one or more problems for self (divorce of separation, alcohol or

drug problems, witness to violence, abuse or violent crime, trouble with police or a

gambling problem). The breakdown of these results is presented in Appendix C, Table

8.4.

Physical disability

As shown in Appendix C, Table 8.5, 46.9% of all respondents reported one or

more disabilities and 20.2% described disabilities that restricted their work-related

physical activity.

Chronic Disease

Table 4.2-8 Leading causes of Ill Health: Australian vs. Respondent Population

Age

group

1st AP RP 2nd AP RP

25-44 Anxiety Disorders

6.7%

MHD

10.2%

Back problems

6.5%

Back Problems

37.5%

45-64 CHD 7.5% CHD 21% Other MSK

5.7%

Back Problems

33.4%

Abbreviations: AP = Australian population, RP = respondent population, MHD = mental health diagnosis, CHD = coronary heart disease, MSK = musculoskeletal.

Table 4.2-8 shows the first and second leading causes of ill health in the

Australian and respondent population. Back problems, mental health disorders and

coronary heart disease are higher than the Australian population. The rate of chronic

disease for females (55.6%) in the respondent population was shown to be lower than

that of males (64%) [OR = 1.418, 95% CI 1.027-1.958, p = 0.034], which was the

reverse of that of the Australian population (females 25% vs. males 21%) (AIHW,

141


2016c). This may reflect a decreasing number of female paramedics after the age of

34.

Arthritis

A total 15.6% of respondents reported experiencing arthritis related conditions

and these are shown in Table 4.2-9.

Table 4.2-9 Arthritis

Type %

Arthritis 9.0

Rheumatism 1.1

Gout 4.1

Osteoporosis/Osteopenia 1.4

Total 15.6

Males with 15 years of shift work or more, who reported arthritis [χ² (6, n = 422)

= 18.874, p = .004] stayed away from work because of the functional impact of arthritis

[χ² (6, n = 33) = 33.246, p = .000]. In contrast, females in the 10-14-year shift work

group reported a functional impact of osteoporosis [χ² (12, n = 241) = 19.245, p =

.006]. Adding to this, Critical Care Paramedics (CCPs) and supervisors were likely to

have gout [χ² (16, n = 663) = 65.010, p = .000]. Males who were separated/divorced

had osteoporosis or osteopenia [χ² (6, n = 422) = 18.543, p = .005] and females who

have arthritis were likely to be divorced [χ² (8, n = 241) = 49.111, p = .000].

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Results

Asthma

Asthma rates in respondents were significantly higher (26.7%) than that of the

Australian population (10.2%) (ABS, 2012a), [OR = 3.33, 95% CI (1.5 – 7.3), p =

0.002] (see Figure 4.3).

Abbreviations: AS = Ambulance Service, AP = Australian Population

Figure 4-3. Asthma: Age and Gender - Ambulance Service vs. Australian Population

Males with asthma also reported experiencing a serious illness [χ² (4, n = 422) =

9.729, p = .045] and job insecurity [χ² (5, n = 422) = 15.012, p = .010]. Whilst tobacco

smoking rates were low (8.9%) compared to the Australian population, males [χ² (1, n

= 402) = 4.246, p = .039] and females [χ² (1, n = 241) = 9.551, p = .002] who smoked

were more likely to have asthma than those who did not smoke.

Cancer

Considering cancer is a known risk factor for shift workers (Blask, 2009), a number

of questions were included in the AHS 2015. Seven-point four percent of respondents had

0

5

10

15

20

25

30

35

40

45

50

AP AS AP AS AP AS

Male Female Total

Percentage as a proportion

of each age group

Age Group

15‐24 25‐34 35‐44 45‐54 55‐64

143


a diagnosis of cancer at some stage in their life and 1.8% currently experienced cancer as

against 1.3% in the Australian population. Of importance, as health status decreased [χ²

(8, n = 422) = 21.100, p = .007], skin checks had also decreased. However, as years in

ambulance increased, so did regular skin checks [χ² (12, n = 663) = 24.953, p = .015] and

the likelihood of a cancer diagnosis [χ² (6, n = 663) = 35.021, p = .000]. There was a

similar relationship with increasing shift work years and an increased likelihood of a

cancer diagnosis [χ² (12, n = 663) = 24.722, p = .016].

When adjusted for age, managers, supervisors and CCPs were all more likely to be

diagnosed with cancer after 45 years of age compared to those who were younger than 45

years of age. Males who were separated/divorced were also more likely to have a diagnosis

of cancer [χ² (3, n = 422) = 13.056, p = .005]. Additionally, males [χ² (6, n = 422) = 22.220,

p = .001] and females [χ² (4, n = 241) = 12.027, p = .017] with vision and hearing

disabilities were more likely to have a diagnosis of cancer. There was no statistically

significant association between tobacco smoking and a current diagnosis of cancer.

However, those who consumed seven or more alcoholic drinks per week were more likely

to have had a cancer diagnosis [χ² (32, n = 663) = 75.451, p = .000] than those who

consumed six or less alcoholic drinks per week.

Cardiovascular Disease (CVD)

Of all respondents, 12.4% had been diagnosed with a cardiovascular disease

compared to 5.0% in the Australian population (ABS, 2012c). Appendix C, Table 8.6

describes the frequency of cardiovascular disease in the respondent population. Table

4.2-10 shows CVD by age group for both the respondent and the Australian

population.

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Results

Table 4.2-10 CVD Diagnosis: Age - Respondents vs. Australian Population

Age Group AP AS

15-24 0.2 3.3

25-34 0.7 4.8

35-44 1.4 7.4

45-54 3.8 20.0

55-64 8.6 35.2

Abbreviations: AP = Australian Population, AS = Ambulance Service.

The proportion of respondents with a CVD diagnosis were consistently higher

in each age group and as age increased respondents also reported increased rates of

CVD. In contrast to those who did not experience CVD, males who have experienced

CVD reported: having their blood pressure checked in the last 12 months [χ² (2, n =

422) = 11.936, p = .003], having a systolic blood pressure greater than 130mmHg [χ²

(3, n = 416) = 17.949, p = .000] and a diastolic blood pressure greater than 90mmHg

[χ² (2, n = 416) = 6.954, p = .031]. In addition, males who were considered to be obese

[χ² (2, n = 399) = 7.139, p = .026] were more likely to report being diagnosed with

CVD, than males who were of normal weight. Males, [χ² (1, n = 422) = 12.895, p =

.000] and females, [χ² (1, n = 241) = 9.980, p = .002] who have CVD were more likely

to report diabetes, [OR = .169, 95% CI (.076 - .379)] as against those who do not have

CVD. As shift work years increased, the proportion of supervisor/managers and PTOs

who have CVD [χ² (6, n = 663) = 53.351, p = .000] also increased.

Diabetes

In total, 4.4% of respondents reported having diabetes as against 4.0% in the

Australian population (ABS, 2012b) (see Table 4.2-11). Of those who reported

diabetes, the majority had Type 2 Diabetes in both the AHS 2015 respondents and the

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Australian population. In addition, males had higher rates of diabetes than females;

5.0/2.9 for AHS 2015 respondents as against 4.6/3.6 for the Australian population. The

rate of diabetes in respondents from the 55-64-year age group was twice that of the

Australian population. Other diabetes associations are shown in Table 4.2.12. This

table shows all diabetes in the respondent and Australian population. Type one

diabetes accounted for 0.5% of the respondent population.

Table 4.2-11 Diabetes: Age & Gender, Respondents vs. Australian Population

Males Females Total

AP AS AP AS AP AS

15-24 0.4 0.0 0.5 0.0 0.5 0.0

25-34 1.1 0.0 0.3 3.1 0.8 1.6

35-44 2.5 2.3 1.1 3.3 1.8 2.7

45-54 4.6 7.0 3.6 2.5 4.1 5.8

55-64 8.9 15.4 7.5 16.7 7.8 15.5

Abbreviations: AP = Australian Population, AS = Ambulance Service

Table 4.2-12 Other Diabetes Associations

Gender Chi-squared d.f. n Sig

Poor SR Health Males 18.997 4 422 0.001

Five or more alcoholic drinks Males 31.453 12 94 0.002

↑ Bodily pain Males 25.847 5 422 <0.001

Disability Males 24.660 4 422 <0.001

Personal stressors Males 24.514 5 422 <0.001

Obesity Males 8.115 2 339 0.017

Hypertension Males 26.675 2 412 <0.001

↑ SWY Males 13.175 6 422 0.040

Abbreviations: SR = self-reported, ↑ = increased, SWY = shift work years, d.f. = degrees of freedom, Sig = significance.

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Results

Respondents who self-reported experiencing diabetes had annual check-ups with

their GP [χ² (3, n = 663) = 8.466, p = .037]. Males, [χ² (2, n = 422) = 30.697, p = .000]

and females, [χ² (2, n = 241) = 22.885, p = .000] who had diabetes, reported

osteoporosis, a cardiovascular condition and a higher K10 score. Females who had

diabetes were also more likely to be diagnosed with cancer [χ² (1, n = 241) = 10.909,

p = .001].

4.2.2 Work Status

Station category

The majority of ACPs and CCPs were employed at category five stations (this is

where most of these positions are based), EMDs were based at operations centres

whilst supervisors were evenly split between station and non-station locations. In

contrast to those who were not employed at category five stations, males employed at

category five stations were more likely to have their cholesterol checked [χ² (10, n =

422) = 29.334, p = .001] and less likely to have a diagnosis of diabetes [χ² (5, n = 422)

= 12.361, p = .030]. As years in ambulance increased, males, [χ² (30, n = 422) = 49.944,

p = .013] and females, [χ² (30, n = 241) = 43.891, p = .049] were more likely to not

work at category five stations compared to those who have fewer years in ambulance.

Shift Work

A large proportion of respondents (n = 88.4%) were engaged in shift work.

Among these participants, males were less likely to have health check-ups [χ² (3, n =

422) = 9.005, p = .029] compared to males who did not engage in shift work. In contrast

to those who did not perform shift work, males [χ² (24, n = 442) = 38.766, p = .029]

and females [χ² (4, n = 241) = 14.176, p = .007] who performed shift work were more

likely to report bodily pain interfering with normal work. These males were likely to

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experience hearing problems [χ² (1, n = 422) = 7.051, p = .008] and have worsening

hearing as shift work years increased [χ² (6, n = 422) = 17.089, p = .000]. Males with

greater than 15 shift work years were more likely to experience arthritis [χ² (6, n =

422) = 18.874, p = .004] and diabetes, [χ² (6, n = 442) = 13.175, p = .040], than those

with less than 15 shift work years. As a reflection of age, years in service and having

already worked a mean of 13.42 years of shift work, males who do not perform shift

work were more likely to have a CVD [χ² (1, n = 422) = 22.302, p = .000]. In contrast,

as shift work years increase, employees will be older, and it was therefore likely that

males and females had CVD [χ² (6, n = 663) = 53.351, p = .000], cancer [χ² (6, n =

663) = 45.528, p = .000] and females had osteoporosis [χ² (12, n = 241) = 19.245, p =

.006].

Years of Service in Ambulance (YIA)

Table 4.2-13 describes years of service of respondents in five year age groups.

Table 4.2-13 Experience -Years of Service

<5 5-9 10-14 14-19 20-24 25-29 >29 Total

n 161 184 92 63 47 63 53 663

% 24.7 27.7 13.8 9.4 7.0 9.4 8.0 100.0

Managers were likely to have greater than 30 years in ambulance with EMDs

and ACPs likely to have less than 10 years in ambulance [χ² (48, n = 663) = 303.945,

p = .000]. As years in ambulance increased, respondents tended not to have an

undergraduate degree, but had a post graduate qualification [χ² (24, n = 663) = 104.390,

p = .000].

For females there was a positive association between years of service and alcohol

consumption. Females will have had at least one alcoholic drink in the previous seven

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Results

days [χ² (108, n = 157) = 143.347, p = .013] and consume five or more alcoholic drinks

in one sitting at least once per week [χ² (30, n = 43) = 51.585, p = .008]. Females drank

more than males in the 1-5 drinks per week group with years of service less than five

years, 5-9 years 14-19 years, and 25-29 years and overall 62.4% of females drank

between 1-5 drinks per week compared to males at 50.7%. Females who drank more

than 5 drinks in one sitting at least once per week or beyond had a higher proportion

in the less than five and 5-9 years age group. However, overall 5.0% of males drank 5

or more drinks at least once per week compared to 3.7% of females.

Other Work

Fifty-four-point three percent of respondents reported doing other work:

primarily home duties 23.5%, voluntary work 12.8% and paid employment 11.9%.

This is shown in Figure 4.4.

Figure 4-4. Respondents who do other Work

Female shift workers were less likely to do other paid or voluntary work, [χ² (5,

n = 241) = 12.794, p = .025]. As years in ambulance increase, both genders tended to

do other work [χ² (30, n = 663) = 66.494, p = .000].

46%

12%4%

13%

23%2% None

Paid employment

Own business

Voluntary work

Home duties

Other

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4.2.3 Risk Factors

BMI Self-Reported

Respondents were asked if they considered themselves to be underweight,

normal weight or overweight and this was compared to computed BMI from self-

measured height and weight of respondents and that of the Australian population (see

Table 4.2-14 for the findings). Importantly, 59.4% who said they were overweight (this

was a description rather than a measure) underestimated their obesity (calculated with

their self-reported measures), with 43.8% of this group self-measuring as overweight

(BMI 25.0 - 29.9) and 54.3% as obese (BMI ≥ 30) [χ² (6, n = 628) = 320.664, p =

.000]. Females, [χ² (4, n = 229) = 156.178, p = .000] who self-reported being

overweight were more likely to self-measure as obese (BMI ≥ 30) as against those who

do not report being overweight. EMDs, supervisors and managers tended to regard

themselves as being overweight [χ² (24, n = 663) = 48.461, p = .002]. Overall the

respondent population was reported to be less overweight than the Australian

population using BMI as the measure. Appendix C, Table 8-7 describes the

relationship between self-reported overweightness, health status indicators and gender.

Males report increasing weight associated with increasing shift work years [χ² (18, n

= 442) = 35.150, p = .009].

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Results

Table 4.2-14 Overweightness: Respondents & Other Sources

Self-Reported

%

Self-Measured

%

AOP 2003 (Parker, A.W. & Hubinger, 2003)

AP (ABS, 2012f)

Males 46.4 70.9 NR 70.3

Females 36.5 45.9 NR 56.2

Overall 41.5 59.4 67.6 63.4

Abbreviations: AP = Australian population, NR = not recorded.

BMI Self-Measured

Supervisors, managers, EMDs and PTOs were more likely to be overweight or

obese [χ² (16, n = 628) = 51.057, p = .000] than paramedics. In addition, males who

were employed at category two, three or four stations were more likely to be

overweight [χ² (10, n = 399) = 18.943, p = .041] compared to males who were

employed at category five stations. Additionally, respondents who were current shift

workers tended to be overweight or obese [χ² (2, n = 628) = 8.851, p = .012]. As shift

work years [χ² (12, n = 628) = 45.245, p = .000] and years in ambulance [χ² (12, n =

399) = 27.439, p = .007] grew, rates of obesity also increased. Table 4.2-15 describes

the relationship between self-measured weight, health status indicators and gender.

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Table 4.2-15 Self-Measured Overweightness16 & Health Status Indicators

Males - overweight Females -overweight

↑ K10 score χ² (6, n = 339) = 15.973, p = .014 χ² (6, n = 229) = 21.361, p = .002

Mental Health χ² (2, n = 229) = 10.387, p = .006

Sight/Speech χ² (12, n = 399) = 29.561, p = .003

Bodily Pain χ² (10, n = 399) = 19.718, p = .032.

Cardiovascular χ² (2, n = 399) = 7.139, p = .026

Diabetes χ² (2, n = 399) = 8.115, p = .017.

Respondents who were overweight and obese report being more vulnerable to

fatigue whilst working day shifts [χ² (6, n = 604) = 16.625, p = .011], evening shifts

[χ² (8, n = 223) = 16.462, p = .036] (females only) and night shifts χ² [(8, n = 604) =

17.956, p = .022] as against those who were not overweight or obese. Females who

were obese were less likely to report a positive job satisfaction score [χ² (4, n = 229) =

11.778, p = .019] as against those who were not obese. Males [χ² (8, n = 399) = 60.976,

p = .000] and females [χ² (8, n = 229) = 40.762, p = .000] with poor health status were

more likely to report being obese than those with fair, good and excellent health status.

Waist – Hip Ratio

Respondents were provided with a set of guidelines and asked to measure waist

and hip circumference from which the waist-hip ratio was calculated. Waist-hip ratio

is regarded as a more accurate measure of central adiposity which brings with it a

greater risk of CVD (Ortega et al., 2010). Excessive central adiposity for females is a

16 Overweightness was calculated from self-reported measures

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Results

waist-hip ratio > 0.81 and for males a waist-hip ratio of > 0.91 (Nishida, Ko, &

Kumanyika, 2010).

Table 4.2-16 describes central adiposity of the respondents based on waist-hip

ratio which was higher by approximately 10% for both genders compared to the

Australian population. Using waist-hip ratio, 81.6% of males and 69.4% of females

were overweight. Overall, 77.7% of respondents were overweight using waist-hip

ratio. In this section the terms overweightness and central adiposity were used

interchangeably.

Table 4.2-16 Central Adiposity of Respondents

Male % Female %

< 0.81 4.0 30.6

0.81 - 0.90 14.4 38.9

> 0.90 81.6 30.5

Legend: Red shading = above normal central adiposity. Green shading is normal.

There was a statistically significant association between waist-hip measurement

and self-measured BMI. Males, [χ² (4, n = 399) = 45.596, p = .000] and females, [χ²

(4, n = 162) = 11.662, p = .020] who were overweight and obese, reported waist-hip

measurements greater than 0.90 as against those of normal weight. Of those who self-

reported normal weight, 67.9% had a higher than normal central adiposity, whilst

individuals who self-reported they were overweight, 93.5% had higher than normal

central adiposity [χ² (6, n = 520) = 40.283, p = .000]. Of the respondents that were

diagnosed with CVD, 88.24% had a higher than normal waist-hip ratio [χ² (2, n = 520)

= 11.968, p = .003] and those who completed year 10 at high school were more likely

to have a higher than normal waist-hip ratio than people who completed year 12 [χ² (8,

n = 520) = 25.873, p = .001]. Managers and patient transport officers (paramedic

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category) were more likely to have a higher than normal waist-hip ratio [χ² (12, n =

520) = 22.150, p = .036] than other employment categories.

As health status decreased, the number of males with a waist-hip ratio greater

than 0.90 increased [χ² (8, n = 520) = 40.565, p = .000]. Females who were current

shift workers reported being more overweight or obese [χ² (2, n = 167) = 8.873, p =

.012], compared to those who do not perform shift work. As shift work years [χ² (12,

n = 520) = 47.890, p = .000] and years in ambulance [χ² (12, n = 520) = 53.400, p =

.000] increased, so did waist-hip ratio.

Blood Pressure

As self-reported health status decreased, there was an increase in the proportion

of respondents with a systolic blood pressure greater than 140mmHg [χ² (12, n = 644)

= 35.729, p = .000] and those with a diastolic blood pressure greater than 90mmHg [χ²

(8, n = 644) = 35.138, p = .000]. Four-point two percent of respondents self-measured

a blood pressure greater than 140/90mmHg compared to 21.5% in the Australian

population (ABS, 2013e) [OR = 0.147, 95% CI (0.049 – 0.447), p = 0.001].


p = .000] who were overweight and obese were more likely to report a systolic blood

pressure > 130mmHg and a diastolic blood pressure > 90mmHg than those who were

not overweight or obese. Respondents who had a waist-hip ratio of > 0.90 also reported

systolic blood pressure > 130mmHg [χ² (6, n = 519) = 39.927, p = .000] and a diastolic

blood pressure > 90mmHg [χ² (4, n = 519) = 11.403, p = .022]. As systolic BP

increased, respondents reported increased fatigue [χ² (9, n = 664) = 22.193, p = .008],

and males reported an increased intensity of fatigue [χ² (6, n = 397) = 13.378, p = .037].

Females who smoke reported systolic blood pressures > 140mmHg [χ² (3, n = 228) =

19.083, p = .000] and diastolic blood pressures > 90mmHg [χ² (2, n = 228) = 6.861, p

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Results

= .032]. Table4.2-17 shows the statistically significant associations for those

respondents who reported a blood pressure ≥ 140/90mmHg.

Table 4.2-17 Hypertension & Health Status Indicators

Gender Chi-squared d.f. n Sig

MHD Male 8.705 3 416 0.033

Bodily Pain Male 25,879 15 416 0.039

Disability (S & H) Male & Female 35.308 18 644 < 0.05

Arthritis Female 17.164 6 228 <0.05

CVD Male 17.949 3 416 <0.001

Diabetes Male 17.053 3 416 0.001

Abbreviations: MH = mental health disorder, CVD = cardiovascular disease, d.f. – degrees of freedom, n = frequency, Sig = significance, S&H = sight and hearing.

Exercise

Exercise hours for walking, moderate and vigorous exercise were totalled for

two weeks, grouped and analysed against all other data elements and are described in

Table 4.2-18.

Australian population hours were not included as they were based on pedometer

readings over eight days, whilst AHS 2015 data was self-reported. Only 223

respondents answered all questions that allowed exercise hours to be calculated. The

proportion of female respondents who exercised < 10 hours per fortnight increased [χ²

(9, n = 223) = 18.202, p = .033] as health status decreased.

Alcohol use showed an inverse relationship with exercise. As the proportion of

respondents who exercised at higher levels increased, the number of alcoholic drinks

consumed by those respondents in seven days decreased [χ² (12, n = 161) = 21.424, p

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= .045]. Additionally, the number of times five or more alcoholic drinks were

consumed in one year [χ² (15, n = 81) = 28.643, p = .018] decreased.

Table 4.2-18 Exercise Hours - Respondents

< 10 hours 10-19 hours 20-29 hours >29 hours

% 18.4 48.4 17.1 16.1

Sitting

Sitting hours in a workday, included all sitting in a 24-hour period, not just that

which occurred at work. Supervisors and EMDs [χ² (24, n = 580) = 67.716, p = .000]

were more likely to sit for > 14 hours per day than paramedics. Those respondents who

do not work at stations and work at category two stations tended to sit for greater than

14 hours per day [χ² (15, n = 580) = 45.984, p = .000]. Mean sitting hours are shown

by gender in Table 4.2-19.

As sitting hours increased, females who consumed five alcoholic drinks twice

per week also increased [χ² (18, n = 35) = 30.270, p = .035] and males who had

increased alcohol consumption over the previous year also increased [χ² (6, n = 364)

= 13.080, p = .042]. As sitting hours increased, males, [χ² (6, n = 352) = 21.552, p =

.001] and females, [χ² (6, n = 198) = 14.643, p = .023] who were overweight and obese

increased as did respondents who smoke [χ² (3, n = 580) = 9.574, p = .023].

Respondents with a high K10 score were more likely to sit for greater than 10 hours

per day [χ² (9, n = 373) = 20.152, p = .017] compared to those respondents with a low

K10 score.

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Results

Table 4.2-19 Mean Sitting Hours - Respondents

Sitting Hours Male (%) Female (%) Mean

5-9 10.2 11.6 10.7

10-14 29.8 27.6 29.0

> 14 60.0 60.8 59.3

Tobacco Smoking

Eight point nine percent of respondents reported current tobacco smoking

compared to 18.2% in the Australian population (ABS, 2013d). EMDs and PTOs were

more likely to smoke than other employment types [χ² (8, n = 663) = 16.759, p = .033].

As the health status of respondents declined, it was more likely that smoking increased

[χ² (4, n = 663) = 15.361, p = .004] compared to those who have improved health

status.

Females who smoked were more likely to consume five or more alcoholic drinks

twice per week [χ² (6, n = 43) = 13.805, p = .032]. Females who smoked also reported

systolic blood pressures greater than 140mmHg [χ² (3, n = 228) = 19.083, p = .000]

and a diagnosis of cancer [χ² (1, n = 241) = 14.680, p = .000], than females who did

not smoke. Females who had thoughts of leaving were more likely to smoke [χ² (1, n

= 241) = 5.092, p = .024], compared to those females without thoughts of leaving.

Males [χ² (1, n = 402) = 4.246, p = .039] and females [χ² (1, n = 241) = 9.551, p

= .002] who smoked tended to report having asthma, coughing, wheezing, SOB and

chest tightness. Respondents who were underweight and overweight were more likely

to smoke [χ² (3, n = 422) = 12.647, p = .005] compared to those who were of normal

weight. Males, [χ² (9, n = 422) = 17.165, p = .046] and females, [χ² (7, n = 241) =

14.277, p = .046] who smoked tended to have sinusitis, anaemia and bronchitis.

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Males who smoked, reported being obese [χ² (2, n = 399) = 9.519, p = .009] and

reported their sight problems were due to diabetes [χ² (2, n = 246) = 7.761, p = .021].

Males who smoked, ‘strongly disagree’ the work unit had a sense of community [χ²

(4, n = 422) = 11.521, p = .021] and a shared vision [χ² (4, n = 422) = 11.847, p =

.019]. As the K10 score increased, males who smoked also increased [χ² (3, n = 299)

= 8.275, p = .040]. Males who took medication for mental illness reported being

smokers [χ² (1, n = 422) = 4.088, p = .043].

Diet

Current Australian guidelines call for two serves of fruit and five serves of

vegetables per day. Appendix C, Table 8.8 shows 38.5% (51.7% in the Australian

population) of respondents do not eat the recommended quantity of fruit and 95.8 %

(91.7% in the Australian population) do not eat the recommended quantity of

vegetables.

As fruit consumption increased, the proportion of male respondents who drank

6-10 alcoholic drinks per week decreased [χ² (24, n = 290) = 40.614, p = .018] and also

reported alcohol consumption had decreased over the last year [χ² (14, n = 647) =

27.296, p = .018]. As vegetable consumption increased, the proportion of males who

drank 6-10 alcoholic drinks per week also decreased [χ² (28, n = 290) = 55.260, p =

.002]. As fruit consumption increased, males who report alcohol or drug abuse also

decreased [χ² (35, n = 422) = 97.927, p = .000] as did males who reported a serious

illness [χ² (28, n = 422) = 114.403, p = .000]. Appendix C, Table 8.9 describes the

associations between fruit and vegetable consumption, health and work status

elements. Males who consumed one serve or less of fruit [χ² (7, n = 422) = 26.578, p

= .000] and vegetables [χ² (7, n = 422) = 16.489, p = .021] per day tended to be

smokers.

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Results

Alcohol Consumption

The mean number of alcoholic drinks consumed by respondents in one week was

eight. Appendix C, Table 8.10 shows the reported frequency of alcohol consumption

by categories of staff. Six percent of respondents drank alcohol daily compared to 8%

in the Australian population (NHMRC, 2009), whilst 50.1% of respondents drank

alcohol weekly compared to 41% in the Australian population. Respondents had a

mean weekly consumption of alcohol of one drink per day. For comparative purposes,

consumption of alcohol between 15 but not more than 28 drinks per week was

considered high risk drinking (AIHW, 2016). Eight-point three percent of respondents

drank alcohol at this level compared to 18.2% in the Australian population (AIHW,

2016c). Managers drank alcohol more often than other employment types in the five

to six (17.4%) and three to four (30.4%) days per week categories.

Approximately 3.3% (17% in the Australian population (NHMRC, 2009)) of

respondents did not drink alcohol in the last year and 2.4% had never consumed

alcohol (10% in the Australian population (NHMRC, 2009)). The most common

drinking level was 1-2 days per week (26.3%). One hundred and thirty-seven

respondents (20.7%) consumed more than five alcoholic drinks per day at least once

per week.

Females who work 180 hours per month consumed up to 10 alcoholic drinks per

week [χ² (12, n = 151) = 30.128, p = .003]. Males with poor self-reported health drank

greater than 20 alcoholic drinks per week [χ² (16, n = 290) = 29.684, p = .020] and

males with poor to fair self-reported health increased their alcohol intake in the

previous 12 months [χ² (8, n = 411) = 22.445, p = .004]. As years in ambulance

increased, females consumed five or more alcoholic drinks at least once per week [χ²

(30, n = 43) = 51.585, p = .008]. Males who reported sleeping 4-6 hours on rostered

159


days off, consumed five or more alcoholic drinks three times per week [χ² (8, n = 432)

= 16.455, p = .036]. Males who reported poor to fair quality of sleep when working

night shifts consumed alcohol up to 10 times per week [χ² (16, n = 177) = 31.303, p =

.012], and females, [χ² (24, n = 43) = 38.385, p = .032] who had poor quality sleep on

rostered days off, were more likely to consume five or more alcoholic drinks three

times per week. Females who reported constant fatigue at work consumed up to 10

alcoholic drinks per week [χ² (12, n = 157) = 58.655, p = .000] or five alcoholic drinks

up to twice per week [χ² (18, n = 43) = 44.420, p = .001]. Females [χ² (16, n = 131) =

38.314, p = .001] who reported lack of time as a barrier to exercise reported consuming

five or more alcoholic drinks > twice per week. Males who report a lack of time [χ²

(12, n = 411) = 22.988, p = .028] and lack of energy [χ² (19, n = 411) = 21.019, p =

.021] as a barrier to exercise had increased alcohol consumption over the previous 12

months.

Stressors

Males who reported stressors because of job insecurity, alcohol, drugs or

violence [χ ² (15, n = 422) = 426.436, p = .000] were overweight and reported having

stressors because of serious illness or death of a close friend or relative [χ² (12, n =

422) = 453.444, p = .000]. Managers were more likely to report a serious illness [χ²

(32, n = 663) = 46.564, p = .046] compared to paramedics and EMDs. Males who

reported divorce or job insecurity [χ² (5, n = 422) = 14.600, p = .012] tended to have

thoughts of leaving and had an increased K10 score [χ² (15, n = 422) = 64.714, p =

.000]. Females, [χ² (9, n = 241) = 24.842, p = .003] who reported divorce and job

insecurity had an increased K10 score and a diagnosed mental health condition [χ² (5,

n = 422) = 30.862, p = .000]. Males who experienced one or more stressors are reported

in Table 4.2-20. Males who were divorced [χ² (5, n = 422) = 17.464, p = .004] tended

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Results

to report cancer. Males who reported job insecurity [χ² (5, n = 422) = 24.514, p = .000],

stressors because of gambling or a serious illness or disability [χ² (4, n = 422) = 24.660,

p = .000] tended to have diabetes. Males, [χ² (5, n = 422) = 24.527, p = .000] and

females, [χ² (3, n = 241) = 16.521, p = .001] who reported stressors because of divorce,

job insecurity and a serious illness, tended to smoke tobacco.

Table 4.2-20 Male Respondents with One or More Stressors

Variable Chi-squared d.f. n Sig

16-20 Alcoholic drinks/wk 9.506 4 290 0.050

≥ 5 alcoholic drinks, 4 times/wk 23.687 12 94 <0.050

↑ alcohol last year 10.139 2 411 <0.050

Constant anxiety 10.322 3 422 <0.050

High anxiety 7.493 2 352 <0.050

Abbreviations: < = less than, ≥ = greater and equal to, wk = week, ↑ = increased, d.f = degrees of freedom.

Sleep

The AHS 2015 was designed with the identical questions in regards sleep as

those used in ‘A Review of the Work Practices, Workload and Health Profiles of

Queensland Ambulance Service Operational Personnel: Implications for Resource

Allocation and Health Management’ (Parker, A.W. & Hubinger, 2003), so that

comparisons could be made, (see Figure 4.5).

In relation to the AHS 2015 data, seven to eight-hour sleep patterns decreased

between day, evening and night shifts and on-call, and increased on rostered days off.

Respondents who sleep < 4 hours increase from day shift to evening shifts and night

shift. In comparing the two data sets, except for 7-8 hours and > 8 hours on rostered

days off (where there was a slight increase), all other comparisons show respondents

slept less hours in 2015 than were reported in 2003.

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Abbreviations: RDO = Rostered days off.

Legend: 2003 = Health and Work Profiles in Ambulance Service Operational Personnel (Parker, A.W. & Hubinger, 2003); 2015 = Ambulance health Survey 2015.

Figure 4-5. Sleep Hours: 2003 vs. 2015

EMDs and managers reported sleeping between 4-6 hours, whilst patient

transport officers, students, advanced care and critical care paramedics and supervisors

tended to sleep 6-8 hours [χ² (24, n = 663) = 80.858, p = .000] when working day

shifts. Respondents with a higher degree were predisposed to sleeping 4-6 hours on

rostered days off [χ² (12, n = 663) = 24.784, p = .016]. As those who sleep 4-6 hours

increased [χ² (12, n = 422) = 30.652, p = .002] health status decreased and quality of

sleep for males, [χ² (16, n = 422) = 40.522, p = .001] and females, [χ² (16, n = 241) =

33.868, p = .006] also decreased, whilst working day shifts. As health status declined,

7-8-hour male sleepers on rostered days off also declined [χ² (12, n = 422) = 41.363, p

= .000] and males who described their sleep as ‘poor’ increased [χ² (16, n = 422) =

47.977, p = .000].

Females were more likely to sleep less hours when working day shifts than males

[χ² (18, n = 241) = 32.313, p = .020] and as shift work years increased, male, [χ² (24,

n = 299) = 38.419, p = .031] and female, [χ² (18, n = 188) = 50.375, p = .000] 4 – 6

0

10

20

30

40

50

60

70

<4hours2003

<4hours2015

4‐6hours2003

4‐6hours2015

7‐8hours2003

7‐8hours2015

>8hours2003

>8hours2015

Day

Evening

Night

RDO

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Results

hour sleepers increased. Males, [χ ² (24, n = 442) = 76.789, p = .000] and females, [χ ²

(24, n = 241) = 56.312, p = .000] reported decreasing hours of sleep, as years in

ambulance increased. Sleep hours by shift pattern and on rostered days off are shown

in Figure 4.6.

Abbreviations: 2003 = Health and Work Profiles in Ambulance Service Operational Personnel (Parker, A.W. & Hubinger, 2003); 2015 = Ambulance Health Survey 2015; RDO = Rostered days off.

Figure 4-6. Sleep Hours vs. Shift Pattern

Unsurprisingly, sleep quality decreased as shifts progressed from day, through

evening to night. However, sleep quality for all respondents improved on rostered days

off. Additionlly, respondents who had poor and very poor sleep were less in 2015 than

2003 and those who had fair sleep in 2015 were more than 2003. Whilst hours of sleep

had declined between 2003 and 2015, quality of sleep improved.

Appendix C, Table 8.11 describes positive and negative associations with sleep

not previously mentioned. Males who report poor quality of sleep on rostered days off,

reported increased systolic BP [χ² (12, n = 416) = 21.342, p = .046] and decreased

0

10

20

30

40

50

60

Day Shift Evening Shift Night Shift RDOs

Very Poor2003

Very Poor2015

Poor2003

Poor2015

Fair2003

Fair2015

Good2003

Good2015

Very Good2003

Very Good2015

163


sleep > 8 hours [χ² (9, n = 644) = 19.602, p = .021]. Males, [χ² (9, n = 422) = 34.515,

p = .000] and females, [χ² (6, n = 241) = 14.250, p = .027] who reported very poor to

poor quality of sleep whilst working day shift were more likely to sleep less than six

hours compared to those who reported fair to good quality sleep.

4.2.4 Organisational Symptomology

Thoughts of Leaving the Ambulance Service

Fifty-one-point six percent of respondents had thoughts of leaving the

ambulance service in the last year. Of these, 60.5% (n = 207) were males and 39.5%

(n = 135) were females. The reasons for considering leaving the ambulance service are

described in Figure 4.7.

The odds of a male respondent having thoughts of leaving the ambulance service

were 2.25 times greater than a female [OR = 2.25, 95% CI (1.28 -3.96), p = 0.005].

However, females and males had similar reasons for considering leaving if the data

was considered in relation to the top five motives. The top five were other, family

pressures, shift work, health and work pressures.

Males with thoughts of leaving were more likely to report arthritis [χ² (1, n =

422) = 4.445, p = .035], moderate body pain [χ² (5, n = 422) = 22.351, p = .000], bodily

pain that interferes with work [χ² (4, n = 422) = 24.841, p = .000], a diagnosed mental

health condition [χ² (1, n = 422) = 5.585, p = .018], having a long term condition such

as sinusitis, allergies, anaemia, and emphysema [χ² (9, n = 422) = 31.021, p = .000],

poor to fair self-reported health [χ² (4, n = 422) = 14.124, p = .007], and greater shift

work years [χ² (6, n = 422) = 16.046, p = .014] than those without thoughts of leaving.

Additionally, males, [χ² (1, n = 422) = 12.051, p = .001] and females, [χ² (1, n = 241)

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Results

= 5.321, p = .021] with thoughts of leaving the ambulance service were more likely to

have cancer than those without thoughts of leaving.

Figure 4-7. Reasons for Considering Leaving the Ambulance Service17

Females who had thoughts of leaving, reported drinking more alcohol than the

same time last year [χ² (2, n = 236) = 6.390, p = .004] and had greater years in

ambulance [χ² (6, n = 241) = 20.538, p = .002] than those females without thoughts of

leaving.

Work Related Health Culture

Work related health culture was measured using four questions from the Lifegain

Health Culture Audit. The questions related to how people stay healthy, individual

attitudes and personal perceptions concerning health whilst working and were chosen

to help in understanding the work-related health culture in an ambulance service. There

were no statistically significant associations between gender and any elements of

work-related health culture. Critical care, advanced care paramedics and EMDs, who

are most of the active AOP, were more likely to regard the work-related health culture

17 The total for Figure 4-7 is more than 100% as respondents could select more than one answer.

165


in a negative sense [χ² (8, n = 594) = 35.454, p = .000] than supervisor/managers. An

overall score from all respondents is provided in Table 4.2-21 and shows that 60.8%

of respondents reported a negative work-related health culture in their work

environment.

Table 4.2-21 Work Related Health Culture

n %

Negative Health culture 403 60.8

Positive Health culture 260 39.2

Total 663 100.0

Respondents who had a trade certificate or an undergraduate degree were more

likely to agree there was a sense of community in the workplace than those with a

diploma. As self-reported health status decreased [χ² (16, n = 422) = 27.5656, p = .036]

and shift work years increased [χ² (24, n = 442) = 36.847, p = .045], it was likely that

males who agreed there was a sense of community decreased. Reporting their

supervisor did not support a healthier lifestyle was associated with CVD [χ² (4, n =

663) = 11.689, p = .020], males who took medication for mental illness [χ ² (4, n =

422) = 10.622, p = .000] and thoughts of leaving [χ² (4, n = 663) = 24.225, p = .000].

A positive outlook at work [χ² (8, n = 663) = 19.967, p = .010] and a sense of

community [χ² (8, n = 663) = 17.908, p = .022] was inversely associated with arthritis

cancer, bodily pain, CVD, those who do not work at stations and thoughts of leaving.

Respondents who were employed at category five stations and those who were not

employed at stations were more likely to not report a positive outlook in the workplace,

[χ² (4, n = 663) = 9.937, p = .042].

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Results

Job Satisfaction

There were 17 elements to the job satisfaction scale. These elements and

frequencies are described in Appendix C, Table 8.13. Respondents were able to select

more than one element. Whilst there were many statistically significant associations

(SSA) between the 17 elements of job satisfaction and other variables, only the most

important are described in this section and males report most of the negative effects.

The overall results of the job satisfaction assessment are presented in Table 4.2-22.

Table 4.2-22 Job Satisfaction in an Ambulance Service

n %

Depressing Job (1-10)

Bad Job (11-16)

Ok Job (17-22)

Good Job (23-28)

Great Job (29-34)

Total

110 16.6

135 20.4

165 24.9

128 19.3

125 18.9

663 100.0

Job satisfaction by age groups and gender is shown in Figure 4.8 and Figure 4.9.

It shows a difference between genders via age groups. For instance, females in the 20-

24 age group show an even distribution of the five categories of job satisfaction. While

males report no ‘bad’ or ‘depressing’ job satisfaction in this age group. ‘Bad’ and

‘depressing’ rates are higher for females in 25-34 age group and are consistent with

males in the 35-44 age group. In addition, job satisfaction improves for female after

the 25-34 age group and male rates continue to decline until the age of 55, where they

start to improve.

167


Figure 4-8. Male Job Satisfaction vs. Age Grouped

Figure 4-9. Female Job Satisfaction vs. Age Grouped

Managers felt less positive at work [χ² (8, n = 663) = 41.437, p = .000], knew

less of what was expected of them at work [χ² (8, n = 663) = 22.326, p = .004] and had

fewer positive interactions at work [χ² (8, n = 663) = 15.506, p = .050] than EMDs or

paramedics. Current male shift workers were more likely to not report a positive job

satisfaction score [χ² (2, n = 422) = 8.400, p = .015] than male non-shift workers.

As shift work years increased, males reported being not recognised and

appreciated at work [χ² (12, n = 422) = 27.891, p = .006], felt as though their supervisor

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

15‐24 25‐34 35‐44 45‐54 55‐64

Male

Ok Good Great

Depressing Bad Linear (Bad)

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

15‐24 25‐34 35‐44 45‐54 55‐64

Female

Ok Good Great Depressing Bad

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Results

does not care about them as a person [χ² (12, n = 422) = 21.415, p = .045] and did not

trust the leadership team, [χ² (12, n = 663) = 23.224, p = .026]. Females with a higher

degree felt as though their opinion counts, [χ² (8, n = 241) = 15.625, p = .048].

Appendix C, Table 8.13 shows those who had increased alcohol consumption

over the last year tended not to have a positive job satisfaction score (see Appendix C,

Table 8.14). Respondents with ‘good’, ‘very good’ and ‘excellent’ self-reported health

agreed that their personal values fit with organisational values [χ² (8, n = 663) =

19.760, p = .011] and trusted the leadership team [χ² (8, n = 663) = 17.083, p = .029]

as against those who had ‘fair’ and ‘poor’ self-reported health.

Respondents who were employed at category two stations were more likely to

report positive job satisfaction scores in their workplace. Respondents who were not

employed at stations [χ² (10, n = 663) = 18.711, p = .044] or with ‘thoughts of leaving’

[χ² (2, n = 663) = 35.678, p = .000] were less likely to report a positive job satisfaction

score in their workplace.

Anxiety whilst working

The questions on anxiety in the AHS 2015 were less about trying to diagnose an

anxiety disorder and more about finding out how prevalent non-clinical anxiety was in

the workplace, and how that changes with three elements: not getting a rest break

during a shift, being fatigued at work and working involuntary overtime. Eighty-four-

point four percent of respondents reported anxiety whilst working: 60% occasionally

and 24.4% frequently to constantly. In addition, respondents described their anxiety at

work as 55.2% mild, 25.8% moderate and 3.5% high. Forty-three-point seven percent

reported their anxiety increased when fatigued. Increased anxiety when working

involuntary overtime was reported as 73.6% ‘none to a little’ and 26.4% as ‘quite a bit

to very much’.

169


Compared to females with undergraduate degrees, males with undergraduate

degrees were more likely to experience increased anxiety when fatigued [χ² (8, n =

402) = 16.901, p = .031]. Additionally, females with higher degrees were predisposed

to reporting anxiety when working involuntary overtime [χ² (12, n = 241) = 35.108, p

= .000] compared to males with higher degrees. Respondents who were employed at

category five stations tended to report increased anxiety when working involuntary

overtime [χ² (15, n = 663) = 65.141, p = .000]. Respondents with poor self-reported

health experienced anxiety frequently [χ² (8, n = 560) = 28.938, p = .000] and described

that anxiety as high [χ² (16, n = 447) = 28.312, p = .029]. As shift work years increased,

females experienced increased anxiety because of having to work involuntary overtime

[χ² (18, n = 241) = 33.625, p = .014]. As anxiety in females increased, so did alcohol

consumption [χ² (6, n = 236) = 18.133, p = .006]. Females who described moderate

anxiety consumed 6-10 alcoholic drinks in seven days [χ² (8, n = 138) = 15.873, p =

.044].

Males who had been diagnosed with cancer experienced anxiety at work on a

frequent basis, [χ² (3, n = 422) = 14.692, p = .002] and described anxiety as moderate

[χ² (2, n = 560) = 7.270, p = .026]. Males who had ‘thoughts of leaving’ the ambulance

service tended to experience: moderate anxiety [χ² (2, n = 352) = 28.854, p = .000],

high anxiety when fatigued [χ² (2, n = 402) = 14.353, p = .001] and working

involuntary overtime [χ² (3, n = 422) = 34.692, p = .000]. The remaining paragraphs

report on the positive associations discovered during the analysis of the AHS 2015

data in relation to non-clinical anxiety.

Respondents who reported their immediate supervisor supported efforts to adopt

a healthier lifestyle did not report moderate to high anxiety [χ² (12, n = 663) = 24.773,

p = .016] and described their anxiety as mild [χ² (8, n = 352) = 15.599, p = .048]. Males

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Results

who believed their immediate supervisor supported efforts to adopt a healthier lifestyle

were less likely to report high levels of anxiety when working involuntary overtime

[χ² (12, n = 422) = 35.934, p = .000] compared to those who reported their supervisor

does support a healthier lifestyle. Males, [χ² (12, n = 422) = 50.886, p = .000] and

females, [χ² (12, n = 241) = 34.182, p = .001] who described a positive outlook in the

workplace tended to report only occasional anxiety at work and described that anxiety

as mild. Males [χ² (12, n = 422) = 47.990, p = .000] and females [χ² (12, n = 241) =

23.001, p = .028] who described a sense of community in the workplace were less

inclined to report moderate to high anxiety. Males were more likely to describe that

anxiety as mild [χ² (8, n = 352) = 20.308, p = .009].

Males, [χ² (12, n = 422) = 30.082, p = .003] and females, [χ² (12, n = 241) =

47.522, p = .000] who described a shared vision in the workplace did not report

moderate to high anxiety and described it as ‘mild’ [χ² (8, n = 352) = 27.341, p = .001].

Males [χ² (12, n = 422) = 30.028, p = .000] and females [χ² (12, n = 241) = 30.028, p

= .003] who described a shared vision in the workplace were less inclined to report

moderate to high levels of anxiety when working involuntary overtime.

Respondents who had a positive job satisfaction score were less likely to report

moderate to high anxiety [χ² (6, n = 663) = 20.293, p = .002]. Respondents with a

positive job satisfaction score only reported ‘not at all to a little’ vulnerability to

anxiety when they worked involuntary overtime χ [² (6, n = 663) = 16,895, p = .010].

Fatigue

A large number of rosters include a cycle of 4 shifts on and 4 * 24 hour periods

off work. Within that roster shifts are often 12 hours and can be extended up to 13 or

14 hours with a late case. There is little choice for paramedics in completing an

extended shift. What also extends a shift is post shift fuelling, cleaning, and completing

171


patient reports, drug audits and time sheets. Table 4.2-23 depicts how often fatigue

was experienced, how it was described and during which part of a shift fatigue was

more prominent. Figure 4.10 shows the intensity of fatigue for the different shift cycles

and how that fatigue changes throughout the events. It should be noted, that having no

rest break during a shift had worse fatigue effects than that which occurs during night

shifts and that fatigue experienced as a result of involuntary overtime had nearly the

same affects as that which occurs during night shifts. Linear trend lines for quite a bit

and very much fatigue show an increased rate of fatigue throughout the events.

Additionally, ‘very much’ fatigue increased at a greater rate than ‘quite a bit’ of

fatigue.

Table 4.2-23 Fatigue Experience in the Ambulance Service

How often fatigue? Occasionally Frequently Constantly

49.9% 39.1% 7.2%

How described? Mild Moderate High

43.0% 46.6% 6.6%

Which part of shift? Start Middle End On-Call

7.5% 27.0% 51.4% 10.3%

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Results

Figure 4-10. Fatigue Changes Throughout the Shift Cycle.

All operational positions described some fatigue on night shifts with, advanced

care and critical care paramedics experiencing higher levels of fatigue than other

employment types [χ² (28, n = 638) = 173.721, p = .000]. Current male shift workers

experienced increased fatigue if they did not get a rest break during a shift [χ² (3, n =

403) = 12.375, p = .006] and if they worked involuntary overtime [χ² (3, n = 403) =

15.536, p = .001].

Respondents with thoughts of leaving, experienced fatigue more often [χ² (3, n

= 663) = 55.142, p = .000]. They also described fatigue in the moderate to severe range

[(2, n = 638) = 24.447, p = .000]. Additionally, age had a strong positive correlation

with shift work years, [r = 0.782, n = 662, p < 0.001] and as shift work years or age

increased, those respondents who reported vulnerability to fatigue whilst working

evening shifts reported increased fatigue [χ² (24, n = 638) = 43.630, p = .008] as against

those who did not report vulnerability to fatigue whilst working evening shifts.

Males who were employed at category five stations experienced increased

fatigue [χ² (15, n = 422) = 25.982, p = .038] and males, [χ² (20, n = 402) = 66.763, p =

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

Percentage

Not at all

A little

Quite a bit

Very much

Linear (Quite a bit)

Linear (Very much)

173


.000] and females, [χ² (20, n = 232) = 48.238, p = .000] who were employed at category

one, two, three and four stations were vulnerable to fatigue when working on-call.

Respondents who described a work-related health culture as negative, reported

increased fatigue [χ² (3, n = 594) = 30.542, p = .000].

In contrast to those who described a negative job satisfaction score, males who

reported a positive job satisfaction score, described less moderate to high fatigue at

work [χ² (6, n = 422) = 24.196, p = .000] and reported that fatigue as mild [χ² (4, n =

403) = 14.502, p = .006]. Males who described a positive job satisfaction score

reported less moderate to high fatigue when working day shifts [χ² (6, n = 403) =

21.253, p = .002], night shifts [χ² (8, n = 403) = 18.647, p = .017], on-call [χ² (8, n =

634) = 19.967, p = .010] (males and females) and involuntary overtime [χ² (6, n = 403)

= 18.329, p = .005]. Table 4.2-24 describes vulnerability to fatigue throughout the shift

cycle.

Table 4.2-24 Shift Cycle Fatigue and a Positive Job Satisfaction Score

Vulnerability Effects Chi-squared n d.f. p

Day shift ♂ A little 21.253 403 6 0.002

Night shift A little – quite a bit 18.647 403 8 0.017

Regular Breaks A little 19.967 634 8 0.010

Involuntary overtime ♂ A little 18.329 403 6 0.005

Involuntary overtime ♀ A little 17.598 235 6 0.007

On call♂ & ♀ None-a little 13.920 592 6 0.031

Abbreviations: χ² = chi-squared, n = respondents, d.f. = degrees of freedom, p. = significance, ♂ = male, ♀ = female.

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Results

4.2.5 Personal Interests / Caring for Self

Formal Breaks

As Appendix C, Table 8.17 suggests, regular rest breaks were associated with an

increased job satisfaction score, less fatigue, lower barriers to exercise, higher

performance than most other workers on the job, improved quality and hours of sleep

and their workplace having a positive outlook, shared vison and sense of community.

Those least likely to get regular rest breaks were ACPs and managers [χ² (8, n = 663)

= 41.437, p = .000].

The question was asked in the AHS2015 why individual do not take breaks,

which is different from asking who gets regular breaks. These results are presented in

Table 4.2-25and show some surprising results. A proportion of EMDs reported they

do not get formal breaks, even though they have formal breaks built into their roster.

Paramedics and supervisor/managers indicated the number one reason they do not take

regular breaks is pressure to get work done and the second most reason was eating on

the run. The latter may be habit and related to pressure to get work done. It should be

noted that a formal break has benefits other than being able to have a meal.

Appendix C, Table 8.18 reports on the negative associations if regular rest breaks

are not achieved, such as reduced health status, higher K10 score, bodily pain, thoughts

of leaving and sitting hours.

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Table 4.2-25 Why Respondents Don’t Take Rest Breaks?

1. 2. 3. 4. 5.

EMD 14.6% 1.2% 4.9% 1.2% 4.9%

Sup/Man 39.1% 31.9% 4.3% 4.3% 5.8%

Paramedic 40.2% 35.2% 8.1% 1.1% 2.5%

Total 36.8% 30.3% 6.9% 1.8% 3.5%

Sig. <0.001 <0.001 >0.05 <0.05 >0.05

Abbreviations: Sup/Man = supervisor/manager, Sig = significance, 1 = Pressure to get work done, 2 = I eat on the run, 3 = I feel that time spent (e.g. chatting, returning, waiting at hospital, etc.) takes up my break time, 4 = I just don’t want to, 5 = I feel guilty.

Gender

Gender based results were predominantly described throughout chapter four.

However, gender-based issues that have not been previously mentioned are included

in this section. Females experienced symptoms of asthma or received treatment for

symptoms more often than males [χ² (1, n = 663) = 6.765, p = .009]. Males were more

likely to have a cardiovascular or circulatory disorder [χ² (1, n = 663) = 13.187, p =

.000] and have cholesterol checks [χ² (2, n = 663) = 19.931, p = .000] than females.

Females were more likely to be EMDs, but not so in terms of CCPs, supervisors/

managers [χ² (8, n = 663) = 70.862, p = .000], and be permanent part-time than males

[χ² (3, n = 663) = 21.783, p = .000]. Additionally, females had worked less than nine

years of shift work [χ² (6, n = 663) = 81.170, p = .000], did other work classified as

home duties [χ² (5, n = 663) = 25.015, p = .000] and were over represented in the

younger age group (18-24) [χ² (6, n = 663) = 91.239, p = .000]. Females were more

likely to have an undergraduate degree, whilst males were more likely to have post-

graduate qualifications [χ² (4, n = 663) = 19.556, p = .001].

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Results

Barriers to exercise

Barriers to exercise were included in the AHS 2015 to assist in understanding

strategies that may lead to health improvement of AOP. As outlined in Table 4.2-26,

lack of time and lack of energy were identified as the major barriers to exercise.

Females were more likely than males to describes lack of energy to exercise when

working greater than 160 hours per month, χ² (12, N = 232) = 25.017, p = .015. The

relationships between these two elements and other variables of significance in the

data set will be the only two reported on further in this section.

Table 4.2-26 Major Barriers to Exercise

Never-rarely Sometimes Often-very often

n % n % n %

Lack of time 101 15.3 231 34.8 331 49.9

Lack of energy 181 27.3 281 42.4 201 30.3

Increased time and energy for exercise was associated with having regular rest

breaks [χ² (5, n = 663) = 13.137, p = .022], as was increased vegetable consumption

for males [χ² (42, n = 422) = 62.940, p = .020]. Appendix C, Table 8.19 describes the

statistically significant associations (SSAs) with lack of time and energy as a barrier

to exercise, such as: increased rates of asthma, overweight, bodily pain, increased K10

score, thoughts of leaving and shift work years.

Wellness Programs

Whilst 75.9% of respondents indicated they would be involved in health

programs if there were incentives, the only health status indicator that was associated

with being involved in wellness programs with incentives, was increasing bodily pain

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[χ² (10, n = 663) = 19.311, p = .036]. There were three elements of both job satisfaction

and work-related health culture that were associated with being more likely to be

involved in health programs with incentives. These are described in Appendix C, Table

8.20.

Waist-hip ratio was inversely related to incentive-based wellness programs.

Respondents who had a waist-hip ratio > 0.81, [χ² (4, n = 520) = 12.990, p = .011]

were more likely to not be involved in incentive-based wellness programs compared

to those with a waist-hip ratio < 0.81. Males who were likely to be involved in an

incentive-based wellness program reported sleeping 7-8 hours whilst on rostered days

off [χ² (6, n = 663) = 12.598, p = .050] as against those who slept < 7 hours.

As years in ambulance increased, wellness facility use would also increase [χ²

(24, n = 662) = 43.965, p = .008], however incentives were less likely to make a

difference to involvement in wellness programs [χ² (12, n = 663) = 27.692, p = .006].

The use of wellness facilities was more likely to decrease as health status decreases [χ²

(16, n = 662) = 27.709, p = .034]. Respondents to the AHS 2015 indicated that rostered

days off were the preferred option for being involved in wellness programs (90.8%),

followed by after work (69.7%). Only 59.7% indicated they would be involved in

wellness programs during work.

Hours worked

Respondents were asked to calculate all hours worked, in the four weeks prior

to completing the survey, in all paid employment. Hours worked in the ambulance

service for all AOP would be expected to be 160 hours per person per month. Whilst

some of the hours worked were involuntary overtime, it was assumed that other hours

worked were the respondents choice and as such were included in this section. Hours

worked are described in Table 4.2-27. The mean number of hours worked was 165

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Results

hours, SD = 51.11, with males working more hours than females (a mean of 172 vs

164 hours). Hours worked by age are described in Table 4.2-28. There was little

variation in hours worked between age groups and no associations between age and

hours worked when adjusted for gender. Finally, supervisors/managers were more likely

to work greater than 200 hours per month [χ² (24, n = 645) = 62.786, p = .000], as were

respondents with a higher degree [χ² (12, n = 645) = 28.574, p = .005].

Table 4.2-27 Hours Worked

Hours worked n %

<161 132 19.9 161-180 259 39.1 181-200 131 19.8 >200 123 18.6 Total 645 97.3 Missing 18 2.7

Total 663 100.0

Table 4.2-28 Hours Worked vs. Age

Age Mean n Std. Deviation

15-24 172.38 59 33.805

25-34 162.69 184 41.592

35-44 171.44 178 42.160

45-54 171.12 144 41.675

55-64 165.92 61 28.362

>64 130.67 3 32.332

Abbreviations: Std = standard deviation.

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4.3 Stage Two - Regression Modelling

This section was informed by a broad structural diagram, which is a conceptual

modelling tool, and defined dependent variables in five broad categories. Together with

the previous section, it assisted in creating an understanding of the associations and effect

sizes on dependent variables in these five categories as described in Table 4.3-1 and

Figure 4.11. The process of planning for this regression modelling is described in

Appendix D.

Figure 4-11. Diagrammatic Approach to a Structural Connectedness Model

Abbreviations: MHD = mental health disorder, K10 = psychological distress score, LTC = long term condition, SR -= self-reported, CVD = cardiovascular disease, MSK = musculoskeletal injury, BMI = body mass index, BP = Blood pressure, LR = logistic regression

The independent (predictor) variables from this analysis in the final models were

significantly associated with the dependant variable using Pearson’s Chi Square.

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Results

Effect size was also calculated using Phi and Cramer’s V correlation coefficient.

Whether an independent variable was idiomatically associated with the dependant

variable was also considered in determining the inclusion of that variable. For instance,

performance at work was not reasonable as a predictor of asthma. However, being

overweight has already been associated with being a predictor of asthma (Beuther &

Sutherland, 2007). The full regression plan is shown in Appendix D.

Table 4.3-1 Regression Modelling – Dependant Variables by Category

Categories Dependent Variables

Health status SR Health MHD K10 Physical Disability

LTC

Chronic disease Asthma CVD Cancer Diabetes Arthritis Three

Organisational symptomology

Job Satisfaction

Health Culture

Consider Leaving

Rest Breaks

Caring for self Fatigue Sleep BtE Time

BtE Energy

Hours Worked

Risk factors MSK Injury

Weight BP Exercise Sitting Diet

Abbreviations: SR = self-reported, MHD = Mental health disorder, LTC = Long term condition, CVD = Cardiovascular disease, BtE = Barriers to exercise.

The modelling used binary or ordinal logistic regression (depending on the

number of factors in the dependant variable) and was run with all included independent

and confounding variables. The advantage of ordinal or binary logistic regression was

that it allowed the testing of several independent variables and an ordinal or binary

dependent variable in a single model. Using a backward elimination method, the

analysis continued until the most parsimonious model was reached.

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4.3.1 Health Status

Determinants of Self-Reported Health

Ordinal logistic regression was used to estimate the effects of age; waist/hip

ratio; BMI; SBP; DBP; alcohol consumption in the last 7 days; increased alcohol

consumption in the previous year; diabetes; gender and arthritis on self-reported

health. The final model was statistically significant [χ² = 97.22, 4 d.f. p < 0.001] and

the assumption of proportional odds (null hypothesis) could not be rejected (χ² = 1.80,

4 d.f. p = 0.773). The effect of age, waist/hip ratio, BMI and diabetes were statistically

significant (see Table 4.3-2).

Table 4.3-2 Predictors of Self-Reported Health

Predictor OR Sig 95% CI

Lower Upper

Diabetes 3.698 0.007 0.352 2.264

Age 1.017 0.028 0.002 0.033

BMI 0.866 0.000 -0.180 0.033

Waist/Hip 0.118 0.004 -3.589 -1.868

Abbreviations: OR = odds ratio, Sig = significance, CI = confidence interval.

The results indicated that the odds of an individual being in the next lowest

health category increased by a factor of 1.02 for every year of additional age (this was

more significant than it appears – it was a factor of 10.20 times in a decade), decreased

by a factor of 0.82 for every 10% decrease in waist hip-ratio, decreased by a factor of

0.13 for every additional unit decrease of BMI and increased by a factor of 3.70 if you

had diabetes. However, we cannot rule out the possibility of the OR for diabetes being

‘one’. The confidence interval contains one, indicating an equal possibility of two

responses: ‘yes’, it could be true or ‘no’, it may not be true.

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Results

Determinants of a Mental Health Disorder

The results of the binary logistic regression modelling were statistically

significant, [χ² (4, n = 506) = 72.36, p < .001], indicating the model was able to

distinguish between respondents who reported and did not report a mental health

disorder. The model explained 23% of the variability of the mental health diagnosis

and correctly classified 88.4% of cases. Table 4.3-3 shows that three independent

variables made a statistically significant contribution to the prediction of mental health

diagnosis (asthma, anxiety and K10). The strongest predictor of a mental health

diagnosis was non-clinical anxiety with an OR = 4.11, followed by Asthma with an

OR = 2.74 and the K10 score with an OR = 1.15. Controlling for all other factors in

the model, the risk of having a mental health disorder was 4.11 times higher if the

person had moderate anxiety, 2.74 times higher if the person had asthma and 1.15

higher if they had a high K10 score.

Table 4.3-3 Predictors of MHD

95% C.I.

β S.E. Wald d.f. Sig. OR Lower Upper

Anxiety -1.410 .545 6.698 1 .010 4.11 1.41 11.90

Asthma -1.010 .296 11.629 1 .001 2.74 1.64 4.90

K10 .137 .023 34.209 1 .000 1.147 1.095 1.200

Constant -3.020 .790 14.599 1 .000 .049

Abbreviations: β = log odds ratio, S.E. = standard deviation, Wald = Wald chi-square, d.f. = degrees of freedom, sig = p value, C.I. = confidence interval, OR = odds ratio

Determinants of the Kessler (K10) Psychological Distress Score

Using ordinal logistic regression, the final model of the Kessler Psychological

Distress Scale (K10) was statistically significant, [χ² (9, n = 662) = 133.05, p < .001],

183


and the predictor variables included self-reported health, a cancer diagnosis and job

satisfaction (see Table 4.3-4). The assumption of proportional odds (null hypothesis)

could not be rejected (χ² = 31.55, 16 d.f. p = 0.773).

Table 4.3-4 Predictors of Psychological Distress

OR Sig.

95% CI

Lower Upper

Poor SR Health 3.51 .000 .813 1.699

Cancer 0.37 .001 -1.546 -.429

Depressing JS 7.63 .000 1.433 2.633

Bad JS 3.70 .000 .731 1.892

Abbreviations: SR = self-reported, JS = job satisfaction, OR = odds ratio, Sig = significance, CI = confidence interval.

The results indicated, the odds of an individual being in the next highest K10

Category (mild → moderate → severe) increased by a factor of 7.63 if the job was

regarded as ‘depressing’, increased by a factor of 3.70 if the job was regarded as ‘bad’,

increased by a factor of 3.51 if self-reported health was poor and decreased by a factor

of 0.62 if there has never been a cancer diagnosis.

Determinants of Disability

The final disability model was developed using binary logistic regression and

was statistically significant, [χ² (9, n = 662) = 39.60, p < .001]. This model was able

to distinguish between respondents who had a disability and those who did not have a

disability and explained 8% of the variability of the disabilities and correctly classified

62.5% of cases. Table 4.3-5shows predictor variables of disability, which include self-

reported health, age, station category and work-related health culture.

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Results

The strongest predictors of an individual’s disability were poor self-reported

health (2.12 times higher), poor work-related health culture (1.73 time higher),

working in a Category 5 station (1.71 times higher), and age (for every 10 years of age

gained the risk of having a disability increased by a factor of 10.02).

Table 4.3-5 Predictors of Disability

B S.E. Wald d.f. Sig. OR

95% CI

Lower Upper

Age .016 .007 4.790 1 .029 1.016 1.002 1.031

Poor SR Health .750 .229 10.733 1 .001 2.118 1.352 3.317

Station Category (5) .536 .204 6.882 1 .009 1.709 1.145 2.551

W-R Health Culture .549 .170 10.440 1 .001 1.732 1.241 2.416

Constant -1.876 .347 29.295 1 .000 .153

Abbreviations: β = log odds ratio, S.E. = standard deviation, Wald = Wald chi-square, d.f. = degrees of freedom, sig = p value, C.I. = confidence interval, SR = self-reported, W-R = work related, OR = odds ratio. Determinants of Long Term Conditions

The final long-term conditions (LTC) model (see Table 4.3-6) was developed

using binary logistic regression and was statistically significant, [χ² (6, n = 663) =

43.29, p < .001]. This model explained 9.0% of the variability of long term conditions

and correctly classified 68.5% of cases, whilst distinguishing between respondents

who had and did not have a long-term condition.

The strongest predictors of developing a long-term condition were constant

fatigue (8.87 times higher) and frequent fatigue (3.52 times higher). The risk of

developing a long-term condition increased by a factor of 1.04 for every one hour

increase in sitting. Sleep had a protective effect on developing a long-term condition.

Respondents who reported fair sleep had a decreased risk of developing a long-term

condition by a factor of 0.70.

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Table 4.3-6 Predictors of Long Term Conditions

B S.E. Wald df Sig. OR

95% C.I.

Lower Upper

Frequent Fatigue 1.258 .431 8.519 1 .004 3.520 1.512 8.195

Constant Fatigue 2.183 .627 12.104 1 .001 8.873 2.594 30.348

Sit Hours in a work day .043 .020 4.504 1 .034 1.044 1.003 1.087

Fair Sleep Quality -1.207 .498 5.877 1 .015 .299 .113 .794

Constant .347 .673 .265 1 .607 1.414

Abbreviations: β = log odds ratio, S.E. = standard deviation, Wald = Wald chi-square, df = degrees of freedom, sig = p value, C.I. = confidence interval. OR = odds ratio

4.3.2 Chronic Disease

This chronic disease section is made up of six dependent variables, which

include asthma, cardiovascular disease, cancer, diabetes, arthritis and three or more

chronic diseases.

Determinants of Asthma

Binary logistic regression was used to develop the final model for predicting

asthma. Whilst the final model was statistically significant, [χ² (2, n = 663) = 36.97, p

< .001], it contained only one predictor. The risk of having asthma increased 3.13 times

if the respondent also experienced hay fever or allergic rhinitis. Whilst BMI was not

statistically significant, it was contextually significant (Beuther & Sutherland, 2007)

and reported in Table 4.3-7. That is, if you were an overweight respondent your

chances of having asthma increased by a factor of 1.02.

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Results

Table 4.3-7 Predictors of Asthma


95% C.I.

Lower Upper

BMI .025 .017 2.218 1 .136 1.025 .992 1.059

Hay Fever 1.198 .205 33.978 1 .000 3.313 2.215 4.956

Constant -2.307 .477 23.431 1 .000 .100

Abbreviations: β = log odds ratio, S.E. = standard deviation, Wald = Wald chi-square, d.f. = degrees of freedom, sig = p value, C.I. = confidence interval, OR = odds ratio.

Determinants of Cardiovascular Disease

The final model was developed using binary logistic regression (see Table 4.3-

8). And was statistically significant [χ² (2, n = 663) = 7.81, p = 0.020], and included

only one predictor variable. The model described 7.2% of the variability of CVD and

correctly classified 87.8% of cases.

Table 4.3-8 Predictors of Cardiovascular Disease


95% CI

Lower Upper

Poor Sleep Quality -.599 .417 2.058 1 .151 .549 .242 1.245

Fair Sleep Quality -1.056 .397 7.074 1 .008 .348 .160 .757

Constant -1.131 .364 9.679 1 .002 .323

Abbreviations: β = log odds ratio, S.E. = standard deviation, Wald = Wald chi-square, d.f. = degrees of freedom, sig = p value, C.I. = confidence interval.

Respondents who reported fair sleep were 0.84 times less likely and those with

poor to very poor sleep, 0.76 times less likely to experience cardiovascular disease.

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Determinants of Cancer

The final model for cancer was statistically significant, [χ² (4, n = 507) = 44.79,

p < 0.001] and included three predictors as described in Table 4.3-9. The model

described 20% of the variability of cancer and correctly classified 93.2% of cases.

Respondents who described frequent to constant anxiety were 11.55 times more

at risk of developing cancer compared to those with occasional to no anxiety. For every

one-year increase in age, the risk of cancer increased by a factor of 1.05 and for every

one-year increase in shift work years, the risk of cancer increased by a factor of 1.04.

Table 4.3-9 Predictors of Cancer


95% CI

Lower Upper

Constant Anxiety 2.446 1.047 5.459 1 .019 11.548 1.483 89.912

Age .052 .019 7.497 1 .006 1.053 1.015 1.093

SWYR .042 .015 8.250 1 .004 1.043 1.013 1.073

Constant -7.085 1.286 30.333 1 .000 .001

Abbreviations: β = log odds ratio, S.E. = standard deviation, Wald = Wald chi-square, d.f. = degrees of freedom, sig = p value, C.I. = confidence interval, SWYR = shift work years, OR = odds ratio.

Determinants of Diabetes

The final model for diabetes was statistically significant, [χ² (4, n = 616) =

25.43, p < 0.001] and included the predictor variables as described in Table 4.3-10.

The model described 18% of the variability in diabetes and correctly classified 97.1%

of cases. Individuals who described high fatigue had an increased risk of developing

diabetes of 6.28. BMI and age were also determinants of diabetes. For each one unit

increase in BMI the risk of developing diabetes increased by a factor of 1.12. For every

one-year increase in age the risk of developing diabetes increased by a factor of 1.08.

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Results

Table 4.3-10 Predictors of Diabetes


95% CI

Lower Upper

BMI .111 .037 8.840 1 .003 1.118 1.039 1.203

High Fatigue 1.837 .730 6.327 1 .012 6.279 1.500 26.284

Age .074 .024 9.511 1 .002 1.077 1.027 1.129

Constant -10.546 1.726 37.312 1 .000 .000

Abbreviations: β = log odds ratio, S.E. = standard deviation, Wald = Wald chi-square, d.f. = degrees of freedom, sig = p value, C.I. = confidence interval, BMI = Body Mass Index, OR = odds ratio.

Determinants of Arthritis

The final model for arthritis (see Table 4.3-11) was statistically significant, [χ²

(4, n = 642) = 26.16, p < 0.001] and was developed using binary logistic regression.

The model described 9.1% of the variability of arthritis and correctly classified 91.6%

of all cases. It included two predictor variables.

Table 4.3-11 Predictors of Arthritis


95% CI

Lower Upper

BMI .060 .022 7.106 1 .008 1.061 1.016 1.109

Moderate Body Pain 1.596 .646 6.098 1 .014 4.933 1.390 17.507

Severe Body Pain 2.396 .789 9.232 1 .002 10.979 2.341 51.499

Constant -5.147 .859 35.927 1 .000 .006

Abbreviations: β = log odds ratio, S.E. = standard deviation, Wald = Wald chi-square, d.f. = degrees of freedom, sig = p value, C.I. = confidence interval, BMI = Body Mass Index, OR = odds ratio.

Individuals who described severe body pain had an increased risk of

experiencing arthritis by a factor of 10.98 and those who described moderate body pain

had an increased risk of experiencing arthritis by a factor of 1.39. BMI was also

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associated with arthritis and for each one unit increase in BMI there was an increased

risk of developing arthritis by a factor of 1.06.

Determinants of Three or more Chronic Diseases

Ordinal logistic regression model was used to develop the final model for three

or more chronic diseases and included BMI, age and the Kessler psychological distress

score (K10) (see Table 4.3-12). Overall, the model was statistically significant, [χ ² (5,

n = 641) = 99.11, p < 0.001]. The risk of having three or more chronic diseases

increased by a factor of 1.05 for every-one unit increase in BMI, increased by a factor

of 1.03 for every-one-year increase in age (that was a factor of 13.0 for every decade

of life), decreased by a factor of 0.55 as the K10 score decreased from severe to

moderate and decreased by a factor of 0.78 as the K10 score decreased from moderate

to mild.

Table 4.3-12 Predictors of Three or more Chronic Diseases

95% CI

Sig. OR Lower Upper

CD (3) 0.000 141.747 3.744 6.164

BMI 0.000 1.052 0.022 0.079

Age 0.000 1.03 0.017 0.044

K10 (Moderate) 0.048 0.451 -1.585 -0.007

K10 (Mild) 0.000 0.219 -2.230 -0.806

Abbreviations: CD = chronic disease, Sig = significance, OR = odds ratio, CI = confidence interval, K10 = Kessler Psychological distress scale, BMI = Body Mass Index

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Results

4.3.3 Organisational Symptomology

Determinants of Job Satisfaction

Ordinal logistic regression was used to develop a model for job satisfaction (see

Table 4.3-13), which was statistically significant, [χ ² (13, n = 662) = 208.12, p <

0.001]. The assumption of proportional odds (null hypothesis) could not be rejected,

[χ² = 75.22, 39 d.f., p < 0.001]. The model included statistically significant predictor

variables of fatigue and anxiety, employment type, self-reported health, family

stressors, age and the highest level of school. The risk of an individual being in the

next lowest job satisfaction category:

Increased by a factor of 2.96 as an individual went from no fatigue to occasional

fatigue and by a factor of 4.03 from occasional to frequent fatigue.

Increased by a factor of 4.02 as an individual went from no anxiety to occasional

anxiety and by 2.72 from occasional to frequent anxiety.

Increased by a factor of 3.27 if you were a supervisor/manager.

Increased by a factor of 1.59 if an individual experienced a family stressor.

Decreased by a factor 0.02 for every one-year increase in age.

Decreased by a factor of 0.60 as self-reported health improved from poor/fair to

good.

Decreased by 0.92 as the highest school qualification increased one unit.

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Table 4.3-13 Predictors of Job Satisfaction

95% CI

Sig OR Lower Upper

Fatigue (Occasionally) 0.000 4.036 0.782 2.009

Anxiety (Never) 0.000 4.026 0.894 1.891

Supervisor/Manager 0.000 3.274 0.803 1.570

Fatigue (Never) 0.028 2.965 0.782 2.009

Anxiety (Occasionally)) 0.000 2.722 0.643 1.360

Family Stressors 0.001 1.588 0.182 0.743

Age 0.005 0.981 -0.032 -0.006

SR Health (Poor) 0.000 0.399 -1.336 -0.501

Highest school (<year 10) 0.016 0.072 -4.782 -0.494

Abbreviations: SR – self-reported, Sig = significance, OR = odds ratio, CI = confidence interval.

Determinants of Work Related Health Culture

Binary logistic regression was used to develop a model of work related health

culture, which was statistically significant [χ ² (5, n = 663) = 62.31, p < 0.001].

Predictor variables included thoughts of leaving, Kessler psychological distress scale

(K10) and disability (see Table 4.3-14). This model explained 12.2% of the variability

in thoughts of leaving and correctly classified 66.5% of cases.

The risk of reporting a positive rather than a negative work-related health

culture:

Decreased by a factor of 0.62 if an individual had thoughts of leaving the

ambulance service.

Decreased by a factor of 0.38 as an individual went from well in terms of

psychological distress to mild psychological distress.

Decreased by a factor of 0.30 if an individual had a disability.

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Results

Having a disability, thoughts of leaving the ambulance service and mild

psychological distress were predictors of an individual reporting a negative work-

related health culture.

Table 4.3-14 Predictors of Work-Related Health Culture


95% C.I.

Lower Upper

Consider Leaving -.969 .171 31.978 1 .000 .380 .271 .531

K10 score -.482 .208 5.391 1 .020 .617 .411 .928

Disability -.358 .175 4.183 1 .041 .699 .496 .985

Constant .361 .134 7.303 1 .007 1.435

Abbreviations: β = log odds ratio, S.E. = standard deviation, Wald = Wald chi-square, d.f. = degrees of freedom, sig = p value, C.I. = confidence interval, OR = odds ratio, K10 – Kessler psychological distress score, OR = odds ratio.

Determinants of Thoughts of Leaving

Binary logistic regression was used to develop a model on thoughts of leaving

the ambulance service. The model was statistically significant, [χ ² (10, n = 662) =

166.89, p < 0.001]. Predictor variables included age, Kessler psychological distress

score (K10), a cancer diagnosis, long term conditions and job satisfaction (see Table

4.3-15). This model explained 29.7% of the variability in thoughts of leaving and

correctly classified 72.1% of cases. The risk of an individual having thoughts of

leaving the ambulance service:

Increased by a factor of 3.52 if there was a cancer diagnosis.

Increased by a factor of 1.74 if the K10 score increased from well to mild

psychological distress.

Increased by a factor of 1.53 if that individual had an LTC.

Decreased by a factor of 0.54 if job satisfaction improved from depressing to bad.

Decreased by a factor of 0.75 if job satisfaction improved from bad to okay.

193


Decreased by a factor of 0.88 if job satisfaction improved from okay to good.

Decreased by a factor of 0.93 if job satisfaction improved from good to great.

Table 4.3-15 Predictors of Thoughts of Leaving


95% C.I.

Lower Upper

Age -.023 .008 7.754 1 .005 .977 .962 .993

K10 score .552 .216 6.529 1 .011 1.737 1.137 2.652

Cancer Diagnosis 1.258 .401 9.846 1 .002 3.519 1.604 7.723

LTC .427 .196 4.746 1 .029 1.532 1.044 2.250

JS Depressing -.769 .326 5.564 1 .018 .464 .245 .878

JS Bad -1.383 .310 19.920 1 .000 .251 .137 .460

JS Okay -2.084 .327 40.558 1 .000 .124 .066 .236

JS Good -2.694 .352 58.423 1 .000 .068 .034 .135

Constant 1.811 .453 15.976 1 .000 6.115

Abbreviations: β = log odds ratio, S.E. = standard deviation, Wald = Wald chi-square, d.f. = degrees of freedom, sig = p value, C.I. = confidence interval, JS = job satisfaction, OR = odds ratio, K10 = Kessler psychological distress score, LTC = long term condition.

Determinants of Rest Breaks

A statistically significant model for rest breaks (see Table 4.3-16) was produced

using binary logistic regression, [χ ² (7, n = 663) = 93.56, p < 0.001]. The model

explained 19% of the variation in rest breaks and correctly classified 73.6% of cases.

Being a male, a supervisor/manager, a paramedic or having frequent to constant fatigue

were predictors of an increased risk of not having a rest break. Increased sitting time

decreased the risk of having a regular rest break.

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Results

Table 4.3-16 Predictors of Rest Breaks


95% C.I.

Lower Upper

Gender (male) -.439 .199 4.853 1 .028 .645 .436 .953

Supervisor/Manager --2.949 .764 14.896 1 .000 .052 .012 .234

Paramedic -2.931 .746 15.447 1 .000 .053 .012 .230

Fatigue (Frequently) -1.691 .767 4.863 1 .027 .184 .041 .829

Fatigue (Constantly) -2.557 .815 9.844 1 .002 .078 .016 .383

Sitting hours 0.047 .024 3.894 1 .048 1.048 1.000 1.098

Constant 4.660 1.106 17.746 1 .000 105.621


Males have a decreased risk of not having a regular rest break by a factor of 0.35.

Supervisor/managers have a decreased risk of not having a regular rest break by a

factor of 0.48.

Paramedics have a decreased risk of not a having regular rest break by a factor of

0.47.

Individuals who reported frequent fatigue have a decreased risk of not having a

regular rest break by a factor of 0.81.

Individuals who reported constant fatigue have a decreased risk of not having a


A one hour increase in sitting time will result in an increased risk of not having a


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4.3.4 Caring for Self

Determinants of Fatigue

Ordinal logistic regression was used to develop a statistically significant model

of fatigue, [χ ² (14, n = 637) = 117.234, p < 0.001]. The predictors included job

satisfaction, consumption of five or more alcoholic drinks in one session per week, age

and disability and are shown in Table 4.3-17.

Table 4.3-17 Predictors of Fatigue

95% CI

Sig OR Lower Upper

JS Depression Job 0.000 7.170 1.373 2.566

JS Bad 0.000 5.055 1.059 2.181

JS Ok 0.000 3.017 0.572 1.637

JS Good 0.000 2.695 0.435 1.548

Alcohol 5 or more (3 times per week) 0.039 5.931 0.093 3.468

Alcohol 5 of more (once per week) 0.031 5.728 0.157 3.334

Age 0.020 0.983 -0.032 -0.003

Disability 0.000 0.452 0.103 0.836

Abbreviations: SR – self-reported, Sig = significance, OR = odds ratio, CI = confidence interval, JS =job satisfaction.

The risk of an individual moving to the next highest level of fatigue:

Increased by a factor of 7.17 when job satisfaction was described as depressing.

Increased by a factor of 5.05 when job satisfaction was described as bad.

Increased by a factor of 3.02 when job satisfaction was described as okay.

Increased by a factor of 2.70 when job satisfaction was described as good.

Increased by a factor of 5.93 when five or more alcoholic drinks were consumed at

least three times per week.

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Results

Increased by a factor of 5.73 when five or more alcoholic drinks were consumed at

least once per week.

Decreased by a factor of 0.02 per year of age or a factor of 0.30 per decade.

Decreased by a factor of 0.55 if an individual had a disability.

Determinants of Sleep Hours

Binary logistic regression was used to assess the impact of several independent

variables on sleep hours (defined as (1) = six hours or less and (2) = seven hours or

more) whilst on rostered days off. The model was statistically significant, [χ ² (4, n =

641) = 44.94, p < 0.001] (see Table 4.3-18). The model explained 12.5% of the

variability of sleep hours and correctly classified 87.2% of cases.

Table 4.3-18 Predictors of Sleep Hours


95% C.I.

Lower Upper

Total SWYR -.048 .012 15.924 1 .000 .953 .931 .976

SR Health (good) .865 .307 7.921 1 .005 2.375 1.300 4.337

SR Health (excellent) 1.022 .327 9.746 1 .002 2.779 1.463 5.278

BMI -.053 .021 6.530 1 .011 .949 .911 .988

Constant 3.395 .717 22.440 1 .000 29.819


For every year increase in shift work an individual had a decreased risk of sleeping

six hours or less on rostered days off by a factor of 0.05.

Self-reported good health showed an increased chance of sleeping seven hours or

more on rostered days off by a factor of 2.73.

Self-reported excellent health showed an increased chance of sleeping seven hours

or more on rostered days offs by a factor of 2.78.

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Each one unit decrease in BMI was associated with a 0.05 decreased chance of

sleeping seven hours or more on rostered days off.

Barriers to Exercise – Determinants of Lack of Time

A statistically significant model was developed using binary logistic regression

for lack of time as a barrier to exercise [χ ² (4, n = 648) = 77.908, p < 0.001]. The

model explained 19.4% of the variability in ‘lack of time’ and correctly classified

85.8% of cases (see Table 4.3-19).

EMDs had an increased risk of describing lack of time as a barrier to exercise by a

factor of 5.30.

Respondents who had thoughts of leaving the ambulance service had an increased

risk of describing lack of time as a barrier to exercise by a factor of 1.88.

For every year of age decrease, there was a decreased risk of describing lack of time

as a barrier to exercise by a factor of 0.02.

An increase of one hour of exercise was associated with a decreased risk of

describing lack of time as a barrier to exercise by a factor of 0.03.

A one unit increase in alcohol consumption per week was associated with an

increased risk of describing lack of time as a barrier to exercise by a factor of 0.96.

A one unit increase in serves of fruit per day was associated with a decreased risk

of not describing lack of time as a barrier to exercise by a factor of 0.31.

Having regular rest breaks was associated with a decreased risk of describing lack

of time as a barrier to exercise by a factor of 0.73.

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Results

Table 4.3-19 Predictors of Lack of Time


95% C.I.

Lower Upper

Alcohol in one week -.039 .014 7.365 1 .007 .962 .935 .989

Rest Breaks -1.316 .353 13.929 1 .000 .268 .134 .535

Exercise Hours -.027 .008 10.987 1 .001 .973 .957 .989

Serves of Fruit/day -.371 .132 7.925 1 .005 .690 .533 .894

Consider Leaving .630 .240 6.887 1 .009 1.878 1.173 3.008

Age -.026 .011 6.026 1 .014 .975 .955 .995

EMD 1.682 .503 11.185 1 .001 5.374 2.006 14.396

Constant 4.407 .692 40.578 1 .000 82.021


Barriers to Exercise – Determinants of Lack of Energy

Binary logistic regression was used to create a model for lack of energy as a

barrier to exercise, that was statistically significant [χ² (7, n = 662) = 79.162, p <

0.001]. The model explained 16.3% of the variability of ‘lack of energy’ and correctly

classified 74% of cases (see Table 4.3-20).

Respondents who had moderate psychological distress had an increased risk of

describing lack of energy as a barrier to exercise by a factor of 5.30.

Individuals who considered leaving an ambulance service had an increased risk of

describing lack of energy as a barrier to exercise by a factor of 1.67.

A one unit decrease in exercise hours showed a decreased risk of describing lack of

energy as a barrier to exercise by a factor of 0.08.

A one unit decrease in age showed a decreased risk of describing a lack of energy

as a barrier to exercise by a factor of 0.04.

199


A one unit increase in the number of serves of vegetable per day showed a decreased

risk of describing lack of energy as a barrier to exercise by a factor of 0.20.

Table 4.3-20 Predictors of Lack of Energy


95% C.I.

Lower Upper

Exercise Hours -.039 .008 25.805 1 .000 .962 .948 .976

Consider Leaving .518 .193 7.188 1 .007 1.679 1.149 2.452

Age -.038 .008 20.708 1 .000 .962 .947 .978

Serves Vegies/day -.216 .079 7.522 1 .006 .806 .690 .940

K10 (Moderate) 1.710 .650 6.916 1 .009 5.529 1.546 19.779

Constant 2.928 .452 42.026 1 .000 18.685


Determinants of Hours Worked

A statistically significant ‘hours worked’ regression model (see Table 4.3-21)

was developed using ordinal logistic regression, [χ ² (2, n = 645) = 18.597, p < 0.001].

This model only included one predictor variable which was statistically significant.

Supervisor/managers had an increased risk of moving to the next highest hours worked

group by a factor of 2.08.

Table 4.3-21 Hours Worked

95% CI

Sig OR Lower Upper

Supervisor/manager 0.000 2.084 0.381 1.088

Abbreviations: SR – self-reported, Sig = significance, OR = odds ratio, CI = confidence interval.

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Results

4.3.5 Risk Factors

Determinants of Non-Clinical Anxiety

Ordinal logistic regression was used to develop a statistically significant model

of anxiety, [χ ² (7, n = 542) = 72.048, p < 0.001]. The predictors included job

satisfaction, normotensive systolic blood pressure and a disability; and is shown in

Table 4.3-22.

Table 4.3-22 Predictors of Anxiety

95% CI

Sig OR Lower Upper

JS Depression 0.000 13.290 1.717 3.457

JS Bad 0.000 7.389 1.142 2.858

Normotensive SBP 0.017 0.460 -1.410 -0.141

No Disability 0.005 0.587 -0.907 -0.162

Abbreviations: JS = job satisfaction, CI = confidence interval, Sig = significance, OR = odds ratio, SBP = systolic blood pressure.

The risk of an individual moving to the next highest level of anxiety (mild →

moderate → high):

Increased by a factor of 13.29 if job satisfaction was described as

depressing.

Increased by a factor of 7.38 if job satisfaction was described as bad.

Decreased by a factor of 0.54 if systolic blood pressure was normal.

Decreased by 0.41 if there was no disability.

201


Determinants of Back Injury

Binary logistic regression was used to create a model of ‘back injury’ as a risk

factor for AOP. This model was statistically significant [χ ² (8, n = 558) = 47.460, p <

0.000]. The model explained 11.2% of the variability of back injuries and correctly

classified 67% of cases. The regression model confirmed the results of the association

analysis and is presented in Table 4.3-23.

Table 4.3-23 Predictors of Back Injury

B SE Wald d.f. Sig. OR

95% CI

Lower Upper

SWYR Current .021 .009 5.770 1 .016 1.021 1.004 1.039

Mental Diagnosis .668 .278 5.776 1 .016 1.950 1.131 3.363

Family Stressors .413 .189 4.784 1 .029 1.511 1.044 2.186

Personal Probs. .587 .235 6.261 1 .012 1.798 1.136 2.847

JS (good) -1.099 .344 10.185 1 .001 .333 .170 .654

Constant -1.003 .280 12.801 1 .000 .367

Abbreviations: β = log odds ratio, S.E. = standard error, Wald = Wald chi-square, d.f. = degrees of freedom, sig = p value, C.I. = confidence interval, OR = odds ratio, Probs. = problems, JS = job satisfaction.

Respondents who had a mental health disorder, had an increased risk of a back

injury by a factor of 1.95.

Respondents who experienced personal problems had an increased risk of a back


Respondents who experienced family stressors had an increased risk of a back


Respondents who described the job as ‘good’ had a decreased risk of a back injury

by a factor of 0.67.

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Results

Respondents who were current shift workers had an increased risk of having a

back injury by a factor of 1.02.

Determinants of Obesity

BMI

Binary logistic regression was used to create a model of ‘BMI’ as a risk factor

for AOP. This model was statistically significant [χ ² (7, n = 641) = 101.827, p < 0.000].

The model (see Table 4.3-24) explained 19.9% of the variability of overweightness

and correctly classified 68.3% of cases.

Table 4.3-24 Predictors of Obesity - BMI

B SE Wald d.f. Sig. OR

95% CI

Lower Upper

Gender (females) -.952 .194 24.043 1 .000 .386 .264 .565

Age Groups .398 .088 20.312 1 .000 1.490 1.253 1.771

Supervisor/manager -1.132 .301 14.183 1 .000 .322 .179 .581

Mild K10 .751 .333 5.080 1 .024 2.119 1.103 4.070

Moderate K10 .948 .474 3.995 1 .046 2.582 1.019 6.543

Constant .379 .410 .852 1 .356 1.460

Abbreviations: β = log odds ratio, S.E. = standard error, Wald = Wald chi-square, d.f. = degrees of freedom, sig = p value, C.I. = confidence interval, OR = odds ratio.

Those respondents who reported moderate psychological distress had an

increased risk of being overweight by a factor of 2.58

Those respondents who reported mild psychological distress had an increased

risk of being overweight by a factor of 2.12.

For every increase in a 10-year age group (e.g. 25-34 to 35-44) those who were of

normal weight had an increased risk of becoming overweight by a factor of 1.49.

203


Being a supervisor/manager showed a decreased risk of being overweight by a

factor of 0.68.

Female respondents had a decreased risk of being overweight by a factor of 0.61.

Central Adiposity - Waist-Hip

A statistically significant ‘Waist-Hip’ model was developed using ordinal

logistic regression, [χ ² (8, n = 519) = 167.11, p < 0.001]. Predictor variables included

gender, anxiety and job satisfaction and are shown in Table 4.3-25.

For each one unit increase in age group, there was an increased risk of moving to

the next highest waist-hip group by a factor 1.40.

Males had an increased risk of being in the next highest waist-hip group by a

factor of 8.68.

Those respondents who reported a job satisfaction score as “depressing” had an

increased risk of moving to the next highest waist-hip group by a factor of 3.04.

An increase in one level of anxiety (e.g. none too mild) increased the risk of

moving to the next highest waist-hip group by a factor of 2.17.

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Results

Table 4.3-25 Predictors of Central Adiposity – Waist-Hip

Estimate SE Wald d.f. Sig.

95% CI

OR Lower Upper

Normal Weight .442 .426 1.076 1 .299 1.555 -.393 1.276

Overweight 2.184 .437 24.932 1 .000 8.881 1.327 3.041

Age Groups .339 .096 12.443 1 .000 1.405 .150 .527

Male 2.161 .220 96.312 1 .000 8.679 1.730 2.593

Anxiety .774 .353 4.817 1 .028 2.168 .083 1.466

JS Depressing 1.111 .398 7.775 1 .005 3.037 .330 1.892

Abbreviations: β = log odds ratio, S.E. = standard error, Wald = Wald chi-square, d.f. = degrees of freedom, sig = p value, C.I. = confidence interval, OR = odds ratio, JS = job satisfaction.

Determinants of Blood Pressure

Systolic Blood Pressure (SBP)

Table 4.3-26 Predictors of Systolic Blood Pressure

OR SE Wald d.f. Sig.

95% CI

Lower Upper

Normotensive 1.231 .586 .126 1 .723 -.941 1.358

Prehypertensive 8.540 .590 13.194 1 .000 .987 3.302

Age Group 1.223 .089 5.151 1 .023 .027 .375

Male 2.803 .230 20.072 1 .000 .580 1.482

BMI 0.516 .222 8.856 1 .003 -1.098 -.226

Normal WH 0.586 .235 5.162 1 .023 -.996 -.073

OW-♀, NW- ♂ 0.536 .227 7.591 1 .006 -1.072 -.181

Diabetes 0.424 .401 4.572 1 .032 -1.643 -.071

CVD 0.618 .255 3.551 1 .059 -.980 .019

Abbreviations: β = log odds ratio, S.E. = standard error, Wald = Wald chi-square, d.f. = degrees of freedom, sig = p value, C.I. = confidence interval, OR = odds ratio, OW = overweight, NW = normal weight, WH = waist/hip, CVD = cardiovascular disease.

205


A statistically significant model was developed for SBP using ordinal logistic

regression, [χ ² (7, n = 625) = 114.99, p < 0.001]. Predictor variables included gender,

BMI, waist-hip, diabetes and CVD. This model is shown in Table 4.3-26.

The risk of moving from normotensive to prehypertensive increase by a factor of

1.22 for each one unit increase in an age group.

Males had an increased risk of being prehypertensive by a factor 2.80.

Respondents of normal weight had a decreased risk of being prehypertensive by a

factor of 0.48.

Respondents who had a normal waist hip ratio had a decreased risk of being

prehypertensive by a factor of 0.41.

Respondents who had a waist-hip ratio greater than 0.81 and less than 0.90 an

increased risk of being prehypertensive by a factor of. 0.46.

Respondents who had diabetes had an increased risk of being prehypertensive by

a factor of 0.03.

Respondents who had CVD had an increased risk of being prehypertensive by a

factor of 0.06.

Diastolic Blood Pressure (DBP)

A statistically significant model was developed for DBP using ordinal logistic

regression, [χ ² (13, n = 627) = 88.60, p < 0.001]. Predictor variables included being a

male, BMI, being a supervisor/manager, working in a category three station, and

bodily pain. This model is show in 4.3-27.

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Results

Table 4.3-27 Predictors of Diastolic Blood Pressure


95% CI

Lower Upper

Normotensive 0.521 .479 1.854 1 .173 -1.591 .287


Male 2.396 .190 21.077 1 .000 .501 1.247

NW 0.477 .178 17.217 1 .000 -1.090 -.391

Supervisor/manager 1.722 .199 7.464 1 .006 .154 .934

Category 3 Stations 2.951 .367 8.682 1 .003 .362 1.802

No Body Pain 0.224 .492 9.242 1 .002 -2.460 -.531

Abbreviations: β = log odds ratio, S.E. = standard error, Wald = Wald chi-square, d.f. = degrees of freedom, sig = p value, C.I. = confidence interval, OR = odds ratio, NW = normal weight.

Working in category three station increased the risk of moving to the next highest

DBP category by a factor of 2.95.

Being a male increased the risk of moving to the next highest DBP category by a

factor of 2.40.

Being a supervisor/manager increased the risk of moving to the next highest DBP

category by a factor of 1.72.

Normal weight respondents had a decreased risk of moving to the next highest

DBP category by a factor of 0.52.

Those respondents who did not report bodily pain had a decreased risk of moving

to the next highest DBP category by 0.78.

Blood Pressure (Systolic/Diastolic) (BP)

A statistically significant model for blood pressure was developed using ordinal

logistic regression, [χ ² (7, n = 622) = 67.19, p < 0.001]. Predictor variables included

being a male, having normal weight and not having bodily pain, CVD and diabetes.

This model is shown in Table 4.3-28.

207


Table 4.3-28 Predictors of Blood Pressure (SBP/DBP)

OR Std. Error Wald d.f. Sig.

95% CI

Lower Upper

Normotensive 0.495 .683 1.062 1 .303 -2.042 .635


Male 3.068 .263 18.172 1 .000 .606 1.636

NW 0.509 .236 8.150 1 .004 -1.137 -.211

Body Pain 0.255 .581 5.527 1 .019 -2.504 -.227

CVD 0.485 .264 7.494 1 .006 -1.242 -.206

Diabetes 0.324 .459 6.029 1 .014 -2.028 -.227

Abbreviations: β = log odds ratio, S.E. = standard error, Wald = Wald chi-square, d.f. = degrees of freedom, sig = p value, C.I. = confidence interval, OR = odds ratio, NW = normal weight, CVD = cardiovascular disease.

Males have an increased risk of moving to the next highest category of BP by a

factor of 3.07.

Normal weight respondents had a decreased risk of moving to the next highest BP

category by a factor of 0.49.

Those respondents who did not report bodily pain had a decreased risk of moving

to the next highest BP category by a factor of 0.74.

Those respondents who reported CVD had an increased risk of moving to the next

highest category of BP by a factor of 0.48.

Those respondents who reported diabetes had an increased risk of moving to the

next highest BP category by a factor of 0.32.

Determinants of Exercise

Exercise hours were grouped into four categories (< 10, 10 – 19, 20 - 29, > 29) over a

fortnight and a statistically significant model was developed using ordinal logistic

regression, [χ ² (4, n = 196) = 18.08, p = 0.001]. Predictor variables in this model

208

Results

included being a current shift worker or a supervisor/manager and the number of

alcoholic drinks consumed in the last seven days. This model is shown in Table 4.3-

29. Current shift workers had an increased risk of moving to the next lowest exercise


For each one unit increase in alcohol consumed per week, respondents had an

increased risk of moving to the next lowest exercise group by a factor of 1.04.

Supervisor/managers had a decreased risk of moving to the next lowest exercise group by a factor of 0.62.

Table 4.3-29 Predictors of Exercise Hours


95% CI

Lower Upper

< 10 hours 0.422 .258 11.170 1 .001 -1.368 -.357

10-19 hours 4.464 .269 31.008 1 .000 .969 2.023

20-29 hours 11.138 .307 61.766 1 .000 1.809 3.011

Current SW 1.047 .015 9.324 1 .002 .016 .075

Alcoholic drinks/ wk 1.037 .014 6.584 1 .010 .009 .065

Supervisor/Manager 0.383 .455 4.442 1 .035 -1.851 -.067

Abbreviations: β = log odds ratio, S.E. = standard error, Wald = Wald chi-square, d.f. = degrees of freedom, sig = p value, C.I. = confidence interval, OR = odds ratio, wk = week, SW = shift worker.

Determinants of Sedentary Behaviour

Sedentary behaviour was determined by calculating the total number of self-

reported sitting hours in a typical workday. A statistically significant model was

developed using ordinal logistic regression, [χ ² (15, n = 628) = 126.13, p < 0.001].

Predictor variables included being an EMD, supervisor/manager, and 15-24-year age

group, male, working at category five stations, increased alcohol consumption over the

last year and normal weight (see Table 4.3-30).

209


Table 4.3-30 Predictors of Sedentary Behaviour


95% CI

Lower Upper

< 5 hours 0.064 .363 57.629 1 .000 -3.467 -2.044

10-14 hours 7.030 .334 34.025 1 .000 1.295 2.605

EMD 10.802 .286 69.275 1 .000 1.819 2.940

Supervisor/Manager 1.592 .201 5.330 1 .021 .070 .860

15-24 years 2.147 .376 4.135 1 .042 .028 1.501

Male 1.699 .179 8.751 1 .003 .179 .881

Category 5 Station 0.611 .191 6.651 1 .010 -.868 -.118

↑ Alcohol Consumption 1.639 .233 4.493 1 .034 .037 .950

Normal Weight 0.531 .170 13.928 1 .000 -.966 -.301

Abbreviations: β = log odds ratio, S.E. = standard error, Wald = Wald chi-square, d.f. = degrees of freedom, sig = p value, C.I. = confidence interval, OR = odds ratio.

EMDs had an increased risk of moving to the next highest sitting group by a

factor of 10.80.

The 15-24-year age group have an increased risk of moving into the next highest

sitting group by a factor of 2.15.

Males have an increased risk of moving into the next highest sitting group by a

factor of 1.70.

Respondents who had increased alcohol consumption over the last year have an

increased risk of moving into the next highest sitting group by a factor of 1.64.

Supervisor/managers have an increased risk of moving to the next highest sitting


Respondents who work in category five stations have a decreased risk of moving

into the next lowest sitting group by a factor of 0.39.

Normal weight respondents have a decreased risk of moving into the next highest

sitting group by a factor of 0.47.

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Results

Determinants of Diet

Serves of Vegetables per Day

A statistically significant model was developed for vegetable consumption using

ordinal logistic regression, [χ ² (6, n = 663) = 56.26, p < 0.001]. Statistically significant

predictor variables included gender, self-reported health and psychological distress

and are presented in Table 4.3-31.

Table 4.3-31 Predictors of Vegetable Consumption


95% CI

Lower Upper

One Serves 0.041 .376 72.555 1 .000 -3.941 -2.467

Two Serves 0.204 .360 19.477 1 .000 -2.295 -.883

Male 0.618 .146 10.757 1 .001 -.768 -.193

Poor/Fair Health 0.283 .217 33.918 1 .000 -1.687 -.837

Good Health 0.512 .157 18.141 1 .000 -.977 -.361

K10 - Mild 0.388 .350 7.321 1 .007 -1.633 -.261

K10 - Moderate 0.346 .391 7.366 1 .007 -1.827 -.295

Abbreviations: β = log odds ratio, S.E. = standard error, Wald = Wald chi-square, d.f. = degrees of freedom, sig = p value, C.I. = confidence interval, OR = odds ratio, K10 = Kessler psychological distress score.

Males had a decreased risk of moving to the next highest level of vegetable

consumption by a factor of 0.38.

Respondents with poor/fair self-reported health had an increased risk of moving to

the next highest level of vegetable consumption by a factor of 0.28.

Respondents with good self-reported health had an increased risk of moving to the

next highest level of vegetable consumption by a factor of 0.51.

Respondents with mild psychological distress had an increased risk of moving to

the next highest level of vegetable consumption by a factor of 0.39.

Respondents with moderate psychological distress had a decreased risk of moving

to the next highest level of vegetable consumption by a factor of 0.65.

211


Serves of Fruit per Day

A statistically significant model was developed for fruit consumption using

ordinal logistic regression, [χ ² (9, n = 628) = 48.56, p < 0.001]. Statistically significant

predictor variables included self-reported health, decreased alcohol consumption over

the last year, age and BMI, and these are described in Table 4.3-32.

Table 4.3-32 Predictors of Fruit Consumption


95% CI

Lower Upper

One serve 0.367 .265 14.301 1 .000 -1.520 -.482

Two Serves 2.325 .264 10.223 1 .001 .327 1.361

Three Serves 10.393 .296 62.587 1 .000 1.761 2.921

Poor SR Health 0.438 .228 13.151 1 .000 -1.271 -.379

Good SR Health 0.683 .168 5.155 1 .023 -.709 -.052

Decreased Alcohol 1.502 .175 5.376 1 .020 .063 .751

25-34 age 0.581 .277 3.838 1 .050 -1.087 .000

35-44 age 0.475 .276 7.264 1 .007 -1.285 -.203

Normal Weight 1.673 .166 9.572 1 .002 .189 .841

Abbreviations: S.E. = standard error, Wald = Wald chi-square, d.f. = degrees of freedom, sig = p value, C.I. = confidence interval, OR = odds ratio, SR = self-reported

Respondents who self-reported poor/fair health have an increased potential of

moving to the next highest level of fruit consumption by a factor of 0.44.

Respondents who self-reported good health have an increased potential of moving

to the next highest level of fruit consumption by a factor of 0.68.

Respondents who have decreased alcohol consumption over the last year have an

increased potential of moving to the next highest level of fruit consumption by a

factor of 1.50.

Respondents who were in the 25-34 age group have a decreased potential of


212

Results

Respondents who were in the 35-44 age group have an increased potential of


Respondents who were of normal weight have an increased potential of moving to

the next highest level of fruit consumption by a factor of 1.67.

4.3.6 Conclusion

This analysis has been presented in a number of sections using descriptive,

inferential and modelling techniques. It is believed that this is the first attempt at

predicting health status, chronic disease, organisational symptomology, caring for self

and risk factors for AOP. Whilst age was not used as an outcome (dependant) variable

the statistically significant relationship between age and other variables has been

presented in Table 4.3-33 and shows a succinct view of the relationships other

variables have with age.

Table 4.3-33 Age as a Predictor Variable for Dependent Variables

Dependent Variable OR Sig 95% CI

SR Health Status 1.017 0.028 0.002 0.033

Disability 1.016 0.029 1.002 1.031

Cancer 1.053 0.006 1.015 1.093

Diabetes 1.077 0.002 1.027 1.129

Three or more CD 1.03 <0.001 0.017 0.044

Job Satisfaction 0.981 0.005 -0.032 -0.006

Thoughts of Leaving 0.977 0.005 0.962 0.993

Fatigue 0.983 0.200 -0.032 -0.003

BtE-Lack of Time 0.975 0.014 0.955 0.995

BtE-Lack of Energy 0.962 0.007 0.947 0.978

Abbreviations: SR = self-reported, OR = odds ratio, Sig = significant, CI = confidence interval, CD = chronic disease, BtE = barriers to exercise.

213


This chapter, together with the literature review, informs the discussion and

contributes to the conceptual framework for understanding the health of AOP, a policy

map for ambulance services and recommendations for future research.

4.4 Stage Three - Thematic Analysis

A thematic analysis (Bryman, 2016) was conducted on the data from the semi-

structured interviews to further explore and find meaning behind the quantitative

findings and the three dimensions of context, people and interaction. It was also hoped

this analysis would inform the development of strategies to improve the health of AOP.

Gaining insight into how others see the health of AOP, and the organisational and

individual context was particularly important, especially after having the quantitative

results presented to the nine personnel who were interviewed using a semi-structured

approach (Bryman, 2006). The results of this analysis exposed a lack of perception by

most interviewees and over optimistic expectations by the researcher for this

component of the research.

A six-step approach, as described in (Braun & Clarke, 2006) was used in this

thematic analysis.

4.4.1 Step One: Becoming familiar with the data

The first step was transcribing the data from the recorded interview, reviewing

the transcriptions to ensure accuracy and rereading the transcriptions several times,

with a time gap for consolidation between each reading. Rereading is important as it

helps to understand the complexities and comprehension beyond the words, to

understand what an interviewee is saying and appreciate the details and build critical

arguments through analysis.

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Results

4.4.2 Step 2: Generating Codes

From a theoretical perspective, a more inductive method was utilised with

themes which emerged from the data (Patton, 1990). This inductive method was

diversified with a semantic style that provided a detailed and nuanced account of the

data based on pre-existing categories determined through the literature review and

analysis of quantitative data. A coding methodology, as described by Strauss, (1987)

was employed, which resulted in words and phrases being assembled and grouped into

categories and reviewed for similarities and differences. Coding categories were

developed based on the headings in the literature review and the qualitative results

section. It was thought this would assist in linking the interviewees data with the

development of themes and the explanatory processes They included health status

measures, health improvement strategies, Health influencing factors, work

environment, demographics, occupational health and safety, health status and presence

at work. This process is shown in Table 4.4-1 and the results are described in Table

4.4-2 and Figure 4.12.

Table 4.4-1 Frequency and Proportion of Categories

Category Code n %

Health Influencing Factors c 30 37%

Work Environment e 19 23%

Presence at work h 10 12%

OHS f 10 12%

Demographics a 8 10%

Measures of Health Status b 4 5%

Health Status g 1 1%

Health Improvement Strategies d 0 0%

Total - 82 100%

Abbreviations: OHS = occupational health and safety.

215


Table 4.4-2 Coding Tree - Words, Phrases, Categories and Codes

First Form Categories Code First Form Categories Code

Age Demographics a BMI HSM b

Education Mental Health

Feminisation Chronic conditions

Superannuation

Skill 0 HIS d

YIA

Rest Breaks WE e

Alcohol HIF c Deskilling

Anxiety ER

Core strength Jobs available

Cynicism Connectedness

Diet Culture

Exercise Surveys

Fatigue Hours worked

Job satisfaction Respect

Knowing M’gers Shift length

M’gers advocate Shift work

Physical health Staffing

Priority One

Resilience Fitness for duty OHS f

Sleep Health promotion

SC Pre-& post testing

Stress Predicting injuries

Trust Recruitment

What to do? University Grads

Worker Injury

Health Status Health Status g

Abbreviations: HSM – Health Status Measures, HIS = Health Improvement Strategies, HIF = Health Influencing Factors, WE = Work Environment, ER = Employer Responsibility, O = Organisational, M’gers = Managers, Grads = Graduates, YIA = Years in Ambulance, SC = Socially Connected.

Absenteeism Presence at Work h

Avoidant coping

Consider leaving

Employee value

Sick leave

Presenteeism

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Results

Figure 4-12. Proportion of Categories

4.4.3 Step three: Searching for themes

Whilst categories had already been identified and the data fitted to these

categories, themes were characterised by looking beyond the data. Initial themes were

developed using the visualisation technique of mind mapping (Greenwood, Kendrick,

Davies, & Gill, 2017). Some of these codes that formed categories, clearly fitted into

themes. For instance, there were several categories developed by codes that fitted into

a theme on the perspective of respondent’s views. From this, four themes were

developed and are described in Figure 4.13 and related to the coding in Table 4.4-1

and Table 4.4-2.

0%

1%

5%

10%

12%

12%

23%

37%

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4

Health Improvement Strategies

Health Status

Measures of Health Status

Demographics

OHS

Presence at work

Work Environment

Health Influencing Factors

Percentage of Categories

Categories

217


Figure 4-13. Initial Thematic Map - Four Draft Themes

4.4.4 Step 4: Review themes

The data in relation to each theme was considered in terms of whether it really

supported the theme. This resulted in a discussion that used self-asked questions to try

and develop a coherent theme (Maguire, M. & Delahunt, 2017). These questions

included:

Do the themes make sense?

Does the data support the themes?

Am I trying to fit too much into the theme?

If themes overlap, are they really separate themes?

Are there sub-themes?

Are there other themes within the data?

The need for health improvement programs

Despite much probing, there was minimal discussion/input by participants about the

need for health improvement programs and the strategies that could be implemented

to improve the health of AOP. The following quote came from an executive who had

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Results

worked over many years to improve the health of AOP. It was the first interview

conducted and showed some cynicism. The interviewee, who was not at the end of his

career, disclosed he may be near the end of his life as he was suffering from a life-

threatening illness (Broadbent et al., 2015). The researcher wondered if the

interviewee’s personal circumstances affected his perceptions. Nevertheless, this

interviewee spoke clearly and forcefully, “A lot of people are not interested in health

and wellbeing programs even with incentives. QAS spends 80% reacting to injuries

and little or no proactive interventions. Staff are not interested and issues around

people’s health are whole of life issues – so what – if the Commissioner reads your

work, and there are lots of issues and areas for improvement and potential risks to the

organisation by the virtue of the fact they are our employees, to what extent should we

get involved in incentivising health, whose responsible for what and how much money

should be invested, and what happens if there is no effect”?

It was put to this interviewee that, despite his viewpoint, this analysis showed an

interconnectedness between the organisation of the work, the work and organisational

environment and therefore what should be done about these issue that are affecting the

health of AOP (Ilmarinen, Tuomi, & Seitsamo, 2005). The response was concerning

and whilst it may not reflect the view of the organisation, it is likely to have an impact,

as the interviewee was an executive who has influence with the organisation. “The

improvement in health is important in private enterprise, but I see in government, that

there is lip service paid to these things. There is plenty more where they came from –

e.g. if paramedics start dropping like flies – we will just recruit more”. Whilst this

viewpoint seemed to be extreme, the other executive and manager interviewees were

subtler in their response and supportive of the organisational approach to this matter.

For instance, “job satisfaction rates have improved” despite the results of this research

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showing worse job satisfaction results than the ‘Working for Queensland Survey’

(QAS, 2004 and 2006) and demonstrating an interconnectedness between job

satisfaction and fatigue, anxiety, employment type, family stressors, age, self-reported

health and education.

Health status

Nevertheless, there was no surprise expressed by the interviewees that the health

of AOP was worse than the Australian population. However, the question was asked

as to whom the responsibility for the health of an individual belonged, the organisation

or the individual. Interviewee responses ranged from “as their responsibility” through

to “we already provide support for the mental health of our people as it has been

recognised that mental health can be affected by the work undertaken by AOP”.

However, this showed a lack of understanding or listening to the results that had been

presented in relation to mental health disorders and psychological distress showing a

relationship to anxiety, some chronic diseases, self-reported health and job

satisfaction. These thoughts are influenced by the majority of the literature on

paramedics and EMDs, which reviews their health status in terms of mental health

(Petrie, Milligan-Saville, et al., 2018) and reflects a lack of understanding of the

overall health impacts on AOP by the work they do, the organisation of the work, and

the work and organisational environment (Sterud, Ekeberg, & Hem, 2006; Studnek, J.

R., Bentley, Crawford, & Fernandez, 2010).

How does working for an ambulance service affect health?

Aggressive questioning of the interviewer by one interviewee, in regards the

need for the “organisation to develop a parent/child” relationship by taking

responsibility for the health of the worker was an indication of the lack of perception

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Results

and understanding about how the health (both mental and physical) can be affected by

the work of AOP. It should be noted that people closest to the operational work who

were interviewed, (the paramedic, EMDs and Operations Centre supervisor) did not

raise this issue. This may reflect a different perspective and a lack of understanding

about how their work affected their health. This viewpoint was also expressed in terms

of OHS, where it was felt that the organisation “was developing a good approach to a

health and well-being framework together with the Public Service Commission, which

includes a fitness for duty policy and executive health assessments”. However, it is

known that the ambulance service organises OHS on a reactive model of trying to

improve safety culture and reacting to injuries after they occur (Queensland, 2013).

The busy and often uncontrolled environment in which paramedics work, often

precludes the reporting of hazards but imposes a “if it is not safe don’t go there or do

it approach”. However, this fails to recognise that there are many instances where

there may be limited choice and that AOP have to assess the risks and will often act

despite there being a risk to their health and safety. For example, a patient collapsed

in a toilet with an inward opening door, leaves no room for subtlety or safe lifting.

Risk factors that contributed to presenteeism included overweight, poor diet,

lack of exercise, high stress and poor relations with co-workers and management

(Pohling et al., 2016). These risk factors hold serious implications for any organisation

and for AOP are linked to absenteeism, creating a cycle of presenteeism and

absenteeism that is difficult to control. For instance, obesity has been related to poor

CPR. Working parents and dual responsibility for children has resulted in more parents

wanting to work part time or taking accrued leave or sick leave to care for children

(Bolzendahl & Pierski, 2016). This creates reduction in financial support for a family

and may force parents to come to work when ill, because of little if any access to paid

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leave. This research demonstrated interconnectedness between the mental and physical

health (Harris, 2018) of AOP and a relationship to the organisation of the work and

the work environment (Moreno et al., 2019) via an assessment of organisational

symptomology (job satisfaction, caring for one’s self, work health culture and thoughts

of leaving) and other variables such as chronic disease. There was further discrepancy

between the two groups with non-operational personnel suggesting ambulance

organisations were becoming more flexible in terms of providing notice for absence

which was not necessarily sickness absence. “QAS has taken a conscious decision to

try and accommodate unplanned absenteeism. For instance, Time off in Lieu (TOIL)

was often difficult to get, so people take sick leave, so we have made it easier to get

TOIL with a couple of weeks’ notice”. However, it was pointed out by the operational

personnel that this does not address the fundamental problem of absenteeism and

presenteeism (Jensen, Andersen, & Holten, 2017). For instance, the following

comments point to a discrepancy in what is perceived to be happening versus what is

actually happening and adds to the view that there is a disconnect between operational

and non-operational personnel:

1. “We have a problem and what are the QAS going to do about it rather than

ignore it. I have been helped, but that is running out and the QAS can no longer

help with family friendly rosters – the processes and access have been

exhausted”. (Interview paramedic)

2. “The use of family friendly or flexible rosters often helped to manage children

and family issues but did not address short term immediate absence needs or

reduced pay and superannuation and the relationship to poor health associated

with under-employment”. (Interview paramedic)

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Results

Regular breaks

This issue is further evidenced when the interviews turned to regular breaks.

“Doesn’t know why crews don’t consider they have had a break – not urgent patients,

don’t require two people to treat, they are at a facility where they can sit down and

eat and have a cup of coffee”. The majority of ACPs and some EMDs’ indicated that

they were not getting regular rest breaks, even though EMDs have these rest breaks

built into their roster. Non-operational interviewees were concerned that there was a

negative relationship between an inadequate number of rest breaks, health status and

job satisfaction among some of the respondents and suggested that additional rest

breaks at hospitals and built into their roster were already implemented. This raises the

issue of what is a regular rest break and why and how it is judged differently by

managers and workers. The literature describes a regular rest break as one where the

worker will not be required to go back to work for a period of time and may include a

short nap (Healy et al., 2008; Zacher et al., 2014). The consequences of irregular rest

breaks in ambulance work have been related to increased risk of musculoskeletal

injuries (Courtney, J. A., 2010). Although it may not be possible in the short term to

give AOP regular rest breaks, especially paramedics, it should be noted that especially

on night shifts, regular rest breaks associated with naps and where the worker was not

required to return to work, have been associated with decreased injuries and accidents

at work (Tucker, 2003). Although the reason for irregular rest breaks for EMDs was

not evaluated in the AHS 2015 survey it may be related to workload and staffing levels

in smaller operations centres particularly at the supervisor level, which may not a have

a relief option.

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Representative nature of respondents and job satisfaction

It was explained that the respondent population was representative of the

ambulance population in terms of gender, age and employment type and that the

analysis showed no statistically significant difference. Despite being very specific and

showing the interviewees that the proportion of respondents in the 20 to 29-year age

group was higher for the respondents to the AHS 2015 than the proportion in the

ambulance service, the viewpoint that the respondent population was not

representative of the ambulance population persisted and was raised by several

participants, “Younger staff did not bother doing the survey”. It was raised by the

interviewees, as one reason some of the AHS 2015 results were quite different from

that experienced by ambulance organisational surveys (QAS, 2004 and 2006). For

instance, 70% of participants in a recent a staff satisfaction survey described their job

as okay or better than okay, whilst in the AHS 2015, only 50% had this opinion. This

may reflect the outcome differences described in the literature between an

organisational survey and anonymous independent survey often due to poor design,

differences among stakeholders, poor communication, conflict and understanding

(Palmer, 2002). However, it may also reflect a lack of understanding of these issues.

For instance, 51% of respondents had ‘thoughts of leaving’ the ambulance service, yet

the turnover rate is less than 4%. This showed a lack of understanding that the

associations with these two elements are different. Thoughts of leaving are predicted

by job satisfaction, psychological distress, cancer and long-term conditions (Lee,

Hom, Eberly, Li, & Mitchell, 2017) “I’ve had constant sinusitis whilst in ambulance”

whilst turnover rate has an economic element to it (Mitchell, Holtom, Lee, Sablynski,

& Erez, 2001; Lee et al., 2017) “but a lot of that is due to it being hard to find a job,

superannuation and people are looking at can they afford to retire”. It was

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Results

disconcerting to hear comments such as “There is plenty more where they came from

– e.g. if paramedics start dropping like flies, we will just recruit more” and “The

organisation may invest more money if there was increases in mental health injuries

etc. but the real reaction is that the organisation would recruit more people”. This

was a revealing moment in the interviews, and the question was asked, “Is this the

attitude of the ambulance service”. The answer was as blunt as the previous comments,

“may not be the attitude but that is the result”. This adds to not only the lack of

perception and understanding between operational and non-operational personnel

(Mikkelsen, Jacobsen, & Andersen, 2017), but it suggests that what the ambulance

service says (e.g. the health of our staff is a top priority) is not actually happening at

the root level. As an example, a paramedic who had been away from her station made

this comment about going back to her station, “I saw people I haven’t seen for two

years and didn’t recognise them – people had put on weight, were tired, poor morale,

saddened, they are almost downtrodden”.

Organisational change

Interviewees were not surprised that sleep hours had decreased in the last 15

years with one of the major issues being shift type and length, “2003 went from 10/14

to 10/10 shifts and in 2007 went to 12/12s. Twelve hour shifts are the worst thing that

ever happened”. Many factors have changed in ambulance services over this period,

including longer day shifts, respondents were less likely to get sleep on a night shift,

increased gender diversity and casualisation of the workforce, organisational

restructures, a pre-employment model with undergraduate degrees and increased

clinical complexity and governance. It was implied that a comparison of sleep hours

in an ambulance service may be inaccurate or not significant because of these changes.

Whilst the impact of the changed context may have some relevance, the number and

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complexity of the changes may be related to increased organisation and ambulance

specific stressors (Sterud et al., 2008b), poor health (Sterud et al., 2008a; Donnelly, E.

& Siebert, 2009; Hansen et al., 2012) and risk factors for poor health. This raises the

perspective of an organisation that is evidence based in its clinical practices, but not

having current knowledge of the humanistic aspects of organisational change in

regards work, the organisation of the work and the work environment, on personnel

(Vakola & Wilson, 2004). That change is constant is not in doubt, but its impact on an

ambulance workforce is not understood, there is little research on this area and the

impact on the health of AOP is unknown. Whilst this is not a call to slow improvement

and efficiency through change, it is an appeal to find how this affects the health and

work-life balance of AOP. As the organisation changes, individuals who lead,

supervise and manage, may themselves change to keep the focus on clients and

enhance the focus on the health of AOP (McFarlane, Enriquez, Schroeder, & Dew,

2011).

Injuries, health and the relationship to work and the environment

The AHS 2015 found that anxiety and stress were inversely related to self-

reported health. Intent to leave, obesity, poor work-related health culture and sleep

hours had an inverse association with self-reported health and job satisfaction. A

positive relationship was described by the AHS 2015 between respondents who

exercised more, had better self-reported health, slept more and were not obese with

organisational and work factors. In addition, the literature does report that

organisational stressors and culture are related to the health status of ambulance

personnel (van der Ploeg & Kleber, 2003; Sterud et al., 2011). There was little

understanding of the relationship between the organisational and health related factors

by the interviewees. The interviewer was given the impression that the individual was

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Results

responsible rather than the relationships mentioned above in relation to the

organisational factors and health. As an example, “Continue to have an increase in

soft tissue injuries (backs and shoulders), 40-45 age group with shoulder injuries even

with new manual handling equipment (this is only Powerlift stretchers), as they try to

use it in a manner it was not designed for. There are less injuries in younger people

which include rolled ankles with females getting out of big trucks. Age group greater

than 40 years of age who did not grow up with electronic stretchers are universally

overweight, have poor core strength and become repeat injurers”. This line of

reasoning suggests a lack of an evidence base in determining the risk factors associated

with of injuries (Vallmuur et al., 2016). Additionally, it implies AOP are not careful

enough, there is a need for workplace wellness programs (Lerner et al., 2013) and an

obligation to develop strategies for improving health and reducing incidents and

hazards that cause injuries of AOP.

Workplace wellness programs

Interview participants believed that workplace-based wellness programs would

not be effective or necessary (Caperchione, Reid, Sharp, & Stehmeier, 2016) (e.g.

“Queensland Fire and Emergency Services (QFES) have gyms but their health is just

as bad”). This reflects an organisational attitude of we will provide the facilities and

the AOP will be responsible for using them. However, a more appropriate attitude may

be that we will provide the facilities and encourage and assist AOP to make their use

part of their work-life balance. Whilst the evidence about the benefits of workplace-

based wellness programs was inconclusive (Lerner et al., 2013) and non-existent in

ambulance services, the cost of unplanned absences, presenteeism, workplace

accidents and injuries, recruitment and sub-optimal performance must be a

consideration in endeavouring to improve the health of operational personnel. There

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was a sense of looking for solutions rather than being advised about what the problems

were, as there was a sense that these issues were already known. Comments such as

“there are lots of issues and areas for improvement and potential risks to organisation

by the virtue of the fact they are our employees and to what extent should we get

involved in incentivising health, who is responsible for what and how much money

should be invested and what happens if there is no effect”. This may reflect the

expectations of AOP personnel, who are used to seeing immediate results from clinical

interventions, such as pain relief. It also reflects a lack of understanding by non-

operational personnel that the results of interventions may not be immediately visible

from an organisational perspective (White et al., 2016).

4.4.5 Step 5: Defining themes

This is the final refinement of the themes and identifies the essence of the

themes. The final themes are shown in Figure 4.14.

Final theme one: Understanding how the health of AOP is affected by work,

organisation of the work and the working and organisational environment is

lacking.

AOP were clear and consistent about this issue and agreed with the analysis of

the quantitative data. They felt that managers did not understand their issues and

challenges and how current practices could be improved. In addition, they suggested

that the health of AOP was a combined responsibility of both and individual and the

organisation and that it would be more effective to change the organisation of the work

and the working environment. The following reflects the view of the organisation.

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Results

“QAS is developing a good approach to a health and well-being framework

together with the Public Service Commission, which includes a fitness for duty policy

and executive health assessments” (Director).

Final Theme two: The evidence base for improving the health of AOP is deficient

Both AOP and managers agreed that this was the case, but AOP were more likely

to raise the issue of finding a solution than managers.

“What makes people exercise, watch what they eat and be stress free, why are some

people like that and others are not – figure this out as it is the crux of the problem in

ambulance” (Interview ‐ paramedic)

“A lot of people not interested in health and wellbeing programs even with incentives

need to speak to MD re this in recover from injury and illness. QAS spends 80%

reacting to injuries and little or no proactive interventions. Staff are not interested

and do you extend your responsibilities beyond that of an employer to a parent

relationship – issue around people’s health are whole of life issues – so what – if

commissioner reads it and there are lots of issues and areas for improvement and

potential risks to organisation by the virtue of the fact they are our employees to

what extent should we get involved in incentivising health, whose responsible for

what and how much money should be invested and what happens if there is no

effect” (Interview - Executive Director).

Final theme three: Difference in perspective of the causes of and responsibility for health between AOP and non-AOP.

This difference in relation to who is responsible for an individual’s health

between AOP and non-AOP was striking and disturbing. AOP understood the

relationship between the organisation of the work, the working and organisational

229


environment and that it affected their health whist non-AOP did not seem to

understand some basic principles e.g. what is a break?

“We have a problem and what are the QAS going to do about it rather than ignore it.

Has been helped with QAS but that is running out and can no longer help with family

friendly rosters – the processes and access have been exhausted. Talk of the boys

club about 8 years ago – known as certain group of people managed in a certain way

and it was punitive. It is really turned around from there, but people are still gun shy

– men and women were disciplined and put on performance plans for being sick.

Managers need to advocate for their staff not just follow the processes. Described

being told they wanted her job after having a bowel collapse operation – very

inappropriate”. (Interview – paramedic)

“The improvement if health is important in private enterprise but I see in government

that there is lip service paid to these things. There is plenty more where they came

from – e.g. if paramedics start dropping like flies – we will just recruit more”.

(Interview – Executive Director).

Final Theme Four: Limited understanding that the health status of AOP is worse

than the Australian population and leadership is need in creating change.

Whilst most of those interviewed agreed that their understanding is limited, there

was different view from AOP and non-AOP about leadership and how that change

should happen. Except for one, non-AOP considered the mental health aspect priority

and that overall health was secondary and the responsibility of the AOP.

“wants access to people for dieticians, physiologists etc. and wants people to

understand the intensity of work and shift length and how that affects them – they

have lots of time for doing something as it is not high intensity. However, people

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Results

cling to award conditions as there is a disincentive to not take a break. Twelve hour

day shifts are the worst for diet, family, exercise – they are atrocious. Discussion on

length of shifts – and a further study to determine causes. Are trying to do a wellness

thing and everyone talks about wellness incentives and not interested. Need someone

to bring it to their face e.g. wellness unit and associated with Priority one and not

run by WHS or Priority one – however their structures are important in terms of

physical health – the peer element is important. Opportunities with leaders and

managers”. (Interview with Assistant Commissioner)

“need a fundamental change in the way business is done? The people who do the

work have to come first not the executives and managers – everyone should be

saying the ambulance service cares for me. Demand is increasing, workload is

increasing, and paramedics are going to increasing number of chronic conditions

and minor injuries. So, the need to continue with non-urgent interventions in

ambulance. Not enough fun and too much pressure – people don’t want to be there

and want the big job. People are eating the wrong stuff and are getting late jobs

which affects family”. (Interview with paramedic)

Final theme five: Whilst ambulance services have policies and statements that

indicate the health of personnel is a priority. The focus is on the individual changing

not the workplace or environment.

The focus of the ambulance service is on selecting the right person, managing

workplace health and safety, reducing absenteeism and providing assistance for those

with psychological distress and mental health disorders. Some of the non-AOP

comment suggests that finding help is the responsibility of the AOP and that programs

are available if people look for them. The point made by AOP is that the responsibility

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is passed back to AOP with consideration of the impacts of the organisation of the

work and the working and organisational environment. It goes back to the Hierarchy

of Controls where it is considered more effective to change the organisation of the

work than change the habits of individuals.

“When people get injured or sick, process could be made so much easier by

empowering people rather than disempowering them. For example, if she does her

back, the manager should say what are you interested in and trying to find the things

that they are interested in and good at and will contribute rather than pushing paper.

We focus on the injury or illness rather than the holistic approach e.g. counselling

and psychological approach to health. However, it has improved over the last 10

years” (Interview EMD)

“The biggest issue is culture and bridging the gap between central office and those

working on the road. Do people have the time to do it? Good OIC will listen, make it

happen are reasonable and make a difference”. (Interview paramedic)

“Different in different government departments so why some people are regarded as

more valuable than QAS people”? (Interview – operational manger)

“Sick leave is probably generational where people see sick leave as an entitlement

rather than like the older generation who save sick leave for when they get sick.

Generational bias in current staff numbers – exponential growth in QAS employees in

the last 10 or so years – from 1800 – 4200 now”. (Interview – Assistant

Commissioner)

“Drug and alcohol are being looked at by a Fitness for Duty group - what does

fitness for duty mean (health, diet etc.) – join with AV and QAS to establish a policy

position and framework. Working group Sonic do preemployment, psychometric

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Results

testing. Qld health has a range of resources available online Health Promotion –

online via QLD Health site – lots of resources for all staff. However, there is some

doubt as to those that know are available – they only have to access it. E.g. Free

QUIT program so awareness may be a problem”. (Interview – manager)

Figure 4-14. Final Thematic Map – Five Themes

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4.5 Conclusion

This chapter was presented in three sections:

1. Stage One – Associations, effects and odds ratios.

2. Stage Two – Regression modelling.

3. Stage Three – Thematic analysis of qualitative data.

The first section was large and complicated with almost 1200 pages of output

from SPSS and whilst it informed further analysis, it introduced the spectre of Type I

(false positive) and Type II (false negative) errors. Due to the scale of the data analysis,

many tables have been developed to describe the results. However, they have been

largely included in Appendix C and cross referenced in this chapter. This section did

however inform regression modelling and the qualitative component of the research

(together with the literature review) which then led to Stage Three of the results – the

‘Thematic Analysis’ of the qualitative data gathered through a semi-structured

interview process.

Regression analysis provided significant insight into a complex and little

understood issue by clarifying relationships, the strength and direction of that

relationship between dependent and independent variables. This confirmed the

majority of the analysis in Stage One and predicted the value of the dependant variable

(e.g. fatigue) based upon the values of the independent variables (e.g. job satisfaction,

anxiety, employment type, family stressors, age, self-reported health and highest

schooling).

The final stage was the thematic analysis which concluded with the

development of five themes and importantly, the researchers epistemological and other

assumptions, ‘why’ and ‘how’, were made explicit in the analysis.

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Discussion

5 Discussion

5.1 Introduction

The aim of the research was to explore the health status of AOP and to develop

a conceptual understanding that may inform policy development, management and

future research. In doing so, this research sought to describe, identify and evaluate,

using a mixed methods approach, the individual, organisational and environmental

factors that may influence health status, and to identify and evaluate strategies that

may improve the health status of AOP.

This chapter integrates the findings of the AHS 2015, the literature review and

outcomes of key stakeholder interviews with the current evidence. The context

explores how the findings of this research may extend the grasp of these issues and

inform the development of a conceptual framework of understanding. It seeks to

identify similarities between these findings and what was previously known, identify

new knowledge, theoretical models and explore options for practical application. This

Chapter is organised around the original objectives:

1) To describe the health status of ambulance operational personnel.

2) To identify the individual, organisational and environmental factors that impact on

health status.

3) To develop a conceptual framework for understanding the relationships between


4) To identify strategies that may lead to the improvement of health.

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Finally, the Chapter brings this discussion together around a conceptual

framework for understanding the ways in which these factors may influence each other

and how the work life balance of AOP may be improved to increase their health and

wellbeing.

5.2 The Health of AOP

Making comparisons between the health status of Australian paramedics and

those of other countries was difficult, as the fundamental structure of ambulance

services was often very different. In addition, this research reflects a profession in

transition to gender balance and university qualifications, whereas in the U.S. the

gender balance has changed little over time and university qualifications amongst

paramedics was on average 5% (Bentley, Shoben, & Levine, 2016). Table 5.2-1 is a

summary of key findings that have been reported in results, for the three employment

categories and assist in putting this discussion into context. In terms of the regression

analysis, the only employment type that was predictive of any dependent variable was

supervisor/managers. Being a supervisor manager was predictive of increased job

satisfaction, overweightness, diastolic blood pressure and sedentary behaviour and

decreased rest breaks and exercise. This makes intuitive sense as supervisor managers

are older than paramedics and EMDs, their work has a high level of sedentary

behaviour associated with it (not as high as EMDs) and we know that increased blood

pressure is associated with obesity (Jordan et al., 2012). The implications for this are

concerning as there is evidence that supervisor support is a key mechanism in

improving worker health (Hämmig, 2017).

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Discussion

Table 5.2-1 Characteristics of Respondents by Employment Category

Category Paramedic Supervisor

Manager

EMD Overall

Male/Female % 64/36 81/19 29/71 64/36

Age (mean) 37 years 46 years 37 years 39 years

Tertiary Qualifications 68% 13% 17% 58%

Operational (% yes) 78% 79% 80% 86%

Years in Ambulance 11 years 21 years 6 years 12 years

Years of shift work 12 years 19 years 8 years 13 years

In relationship 70% 89% 83% 83%

Do other work 56% 56% 43% 54%

Considered leaving 53% 44% 56% 52%

Positive WR health culture 36% 56% 27% 39%

It is a good job 37% 57% 23% 38%

Smokes 7% 11% 18% 9%

SR ‘very good’ health 44% 42% 29% 44%

BMI - overweight 55% 78% 72% 61%

BP normotensive 81% 67% 81% 78%

K10 – severe distress 61% 63% 59% 61%

Experiences family stressors 49% 49% 57% 50%

Experiences personal problems 20% 13% 15% 18%

Anxiety (frequent-constant) 23% 25% 32% 24%

Disability 39% 41% 51% 41%

Chronic disease 25% 45% 30% 30%

Back problems 36% 32% 32% 35%

Sit >10 hrs per day 60% 70% 95% 67%

Fatigue (Frequent – constant) 48% 44% 31% 46%

Abbreviations: SR = self-reported, BMI = body mass index, BP = blood pressure, K10 = psychological distress score, WR = work-related.

One of the questions asked in the work-related health culture component of the

AHS 22015 was ‘My immediate supervisor supports efforts to adopt healthier lifestyle

practices’ (70% did not agree). Another standout from Table 5-1 is that EMDs report

237


less ’very good health’, have higher rates of ‘non-clinical anxiety’ and ‘disability’ than

other employment categories and 95% sit for greater than 10 hours per day. Obesity

rates are 72%, however only 30% report chronic disease, which is higher than

paramedics but lower than supervisor/managers. This is contradictory to the literature

(Booth, 2001), but may be partially accounted for by younger age. On the other hand,

paramedics have higher rates of back injuries and personal problems, but lower rates

of anxiety and chronic disease which may be accounted for partially by an increasingly

younger workforce

5.2.1 Occupational Health and Safety

Although the evidence that ambulance personnel experience higher rates of

injuries (including fatal injuries) than other workers is clear (Maguire, B. J. et al.,

2005; Roberts, Sim, Black, & Smith, 2015; Reichard et al., 2017), the evidence in

relation to the causes of these injuries in ambulance personnel was mixed. In Turkey,

traffic accidents, needle stick and body fluid splashes account for the majority of

injuries (Yilmaz et al., 2016), whilst in the U.S., it was traffic accidents, manual

handling and occupational violence (Reichard et al., 2017). In Australia on the other

hand, the predominant cause of occupational injuries was lifting, carrying and pulling

(Maguire, B.J. et al., 2014). Similarly, those factors that may contribute to injuries,

such as health status, risk factors for poor health and health behaviours are not clear

(Dropkin et al., 2015). The AHS 2015 did not seek to contribute to an understanding

of rates of injury, but rather focussed on the general health status of AOP. For this

reason, the factors that impact on the health of AOP sometimes form the basis of OHS

claims but also act as co-morbidities (including mental health, psychological distress

and chronic disease risk factors such as obesity), which may predispose individuals to

injury or complicate recovery. The AHS 2015 found that 50.1% of respondents had

238

Discussion

‘thoughts of leaving’ and 54.3% did other work. Sixty seven percent of those who did

other work specified that work as either home duties or voluntary work, and only 13%

performed paid work, which included owning their own business. Whilst the question

wasn’t asked about why AOP did other work, it appears this is not because AOP were

poorly paid, but as a necessity (home duties - 23.5%) and giving back to society

(voluntary work - 12.8%). However, when compared to the Queensland population,

70.5% completed unpaid domestic work and 18.8% did voluntary work. Initially the

AHS 2015 results for home duties and voluntary work appeared to be high but is low

compared to the Queensland population and may be explained by the high rates of

fatigue and lower hours of sleep for AOP.

Back injuries

The AHS 2015 asked two questions about back injuries, which are the majority

of musculoskeletal injuries experienced in ambulance work in Australia (Studnek,

Jonathan R., Crawford, Wilkins, & Pennell, 2010; Maguire, Brian J. & Smith, 2013).

This research suggests a link between back injuries and working in an ambulance

service, especially for paramedics and supervisor/managers. Thirty-four-point eight

percent of respondents identified a back problem compared with 16% of the Australian

population (AIHW, 2017). The AHS 2015 found the risk of a back problem amongst

males was slightly higher than females, which was dissimilar to a previous study of

ambulance officers in Sweden (Aasa, Brulin, et al., 2005). This may relate to the time

difference in the studies and the changes that have happened in the Australian

ambulance service in relation to reducing incidents and hazards, such as self-loading

stretchers, assistive lifting devices and an improved focus on safety culture. It may also

relate to females in the Australian ambulance service knowing their limits, following

risk management strategies in place and calling for assistance, whilst males have a

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more stoic approach, similar to that described in monitoring their own health

(Wiitavaara, et al., 2007). Punnet, et al., 2005 suggested the burden of lower back pain

is related to the higher participation of males in the labour workforce. The proportion

of males in the ambulance services was higher than that of females when the AHS data

was collected, and as male/female ratio approaches 50% it is expected that the current

picture will change, and females will report more back injuries than males (Berg, Hem,

Lau, & Ekeberg, 2006; Maguire, Brian J. & Smith, 2013). This research found a similar

association between musculoskeletal issues, anxiety, a mental health disorder and low

job satisfaction from what was described by Aasa et al. (2005).

Whilst causes were not assessed, hearing issues were three times greater for

those who had back problems. This relationship has not been previously described in

the literature and may well relate to those who work in busier stations reporting more

hearing loss and back injuries. Hearing loss is known to increase with age (Yang,

Schrepfer, & Schacht, 2015) and as AOP aged, they were also likely to report hearing

problems. Lifetime exposure to noise was not assessed, however hearing is a critical

element for AOP. With EMD’s, understanding and passing on locations of incidents

is important and with paramedics, understanding what a patient is relaying is a

diagnostic element in determining an appropriate clinical approach. The AHS 2015

results demonstrate a confusing picture of hearing loss and its associations in an

ambulance service. For instance, it is known that hearing loss is related to aging and

has been associated with poor communication, lowered health related quality of life

and decreased cognitive and physical functioning (Bainbridge & Wallhagen, 2014).

However, in firefighting and ambulance populations, the evidence for hearing loss was

mixed with some research indicating that hearing loss decreases faster in these

occupations (Ide, 2011) and other research suggesting that emergency service workers

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Discussion

were not at risk for occupational noise induced hearing loss (Clark & Bohl, 2005).

Whilst there is no evidence to suggest overuse, one such explanation of this association

may be related to the use of codeine-based products and paracetamol, where it is

known that overuse of these medications may be associated with sensorineural hearing

loss (Freidman et al., 2000). This needs further investigation as those who report

hearing, report back injuries and work in higher workload locations and may self-

medicate for back pain.

5.2.2 Chronic Disease

The Australian Institute for Health and Welfare (AIHW) describes eight diseases

(arthritis, asthma, back pain, cancer, CVD, chronic obstructive pulmonary disease,

diabetes and mental health) as part of the chronic disease profile of the Australian

population and that chronic diseases were the leading cause of ill health in the

Australian population (AIHW, 2016b).

Chronic disease

Some chronic diseases are known to be related to shift work (cardiovascular and

cancer especially) (Mosendane & Raal, 2008; Stevens et al., 2011), although there has

been little research on these associations in AOP. The evidence in relation to chronic

disease is mixed, with AOP in this research reporting higher rates of some chronic

diseases than the Australian population. The rate of chronic disease in U.S. based EMS

workers was shown be similar to the U.S. population (Drew-Nord, Hong, & Froelicher,

2009; Banes, 2014). The AHS 2015, found that 64.3% percent of respondents reported

at least one chronic disease compared with 50% of the Australian population. Males

reported more chronic disease than females and those who had three or more chronic

diseases tended to be older or work in rural ambulance stations. Seven-point six

percent of respondents reported three chronic diseases compared with 4.8% in the

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Australian population. The regression modelling showed BMI, age, and psychological

distress are statistically significant predictor variables of three or more chronic

diseases in the respondent population. We know that the determinants of ill-health in

the Australian working age population have many causes (AIHW, 2018). Table 4.3-33

shows a comparison of the first and second leading causes of ill health in the Australian

population as against similar data from the AHS 2015. This shows a significantly

higher rate of mental health diagnosis and back injuries in the AOP as compared to the

Australian population.

There are significantly worse rates of mental health disorders, coronary heart

disease and back problems in the respondent population compared to the Australian

population. This should be seen as a wakeup call for any ambulance service in

Australia, especially since this research has shown that psychological distress and

chronic disease are predictors of mental health disorders, sleep is a predictor of

cardiovascular disease and back disorders are predicted by mental health disorders,

personnel and family stressors and job satisfaction. Psychological distress and mental

health disorders are known to be high in ambulance services (Asbury et al., 2018;

Petrie et al., 2018) and sleep is known to be poor (Schernhammer, 2016; Neufeld,

Carney, Dolezal, Boland, & Cooper, 2017). Cardiovascular disease and back problems

have been shown by this research to be significantly higher than the Australian

population and job satisfaction have been shown to be worse than that described by

the ambulance service.

It should be noted that within the ambulance population the male rate of chronic

disease was three times greater and the female’s rate of chronic disease twice that of

the Australian population. Whilst this relates to the previous discussion on three or

more chronic diseases, four percent of females have one or more of the eight selected

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Discussion

chronic diseases in the Australian population (AIHW, 2018). The differences between

what was described in the respondent population and the Australian population may

be accounted for by women living longer than men (Austad & Fischer, 2016) and that

in the respondent population female participation in the ambulance workforce declines

at a greater rate than male participation over the age of 45 years (Krause & Sawhill,

2017). This could have a number of relationships with what is known about work

ability in females decreasing at a greater rate than that of males as they age, especially

where job demands do not decrease (Pranjic, Gonzales, & Cvejanov-Kezunović,

2019).

Arthritis

There was little difference between the rates of arthritic conditions in the

Australian population (16.1%) and the AHS 2015 population (15.6%). Additionally,

this research has shown that psychological distress, bodily pain and chronic conditions

are higher in the respondent population than the Australian population and was further

supported by the following. Arthritis was seen to increase as years of shift work and

years in ambulance increased. Arthritis is known to be associated with chronic pain

(AIHW, 2016b) as is obesity (Okifuji & Hare, 2015) and these relationships were

confirmed for the respondent population. The links between bodily/chronic pain and

obesity were thought to include structural factors such as lifting demands associated

with manual handling, inflammatory markers, depression, sleep and lifestyle (Okifuji

& Hare, 2015). Inflammatory markers and lifestyle were not able to be assessed in the

AHS 2015, and back injuries, depression and sleep (which was included in the

modelling) showed no relationship with the respondent population. Additionally, the

regression model showed BMI and bodily pain to be statistically significant predictors

of Arthritis in the respondent population. It was not known why the rates of Arthritis

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in the respondent population differs little from the Australian population, as all

indications were that the associations and predictors with and of Arthritis were worse

than that of the Australian population. One explanation may lie in the help seeking

behaviours of AOP, which was known to be less than that of the general population

(Sterud, Hem, Ekeberg, & Lau, 2008) and the differences in help seeking behaviour

between males and females (Juvrud & Rennels, 2017).

Cancer

Cancer is known to be responsible for almost one fifth of the burden of disease

in Australia and is associated with increasing age (AIHW, 2016b). There was no

suggestion in the literature than cancer was associated with being an AOP. However,

there are suggestions that there was an association between cancer and shift work

(Shariat et al., 2015). The AHS 2015 showed that shift work and age were associated

with a diagnosis of cancer, with rates of cancer for AOP slightly higher than that of

the Australian population (1.8 vs.1.3%). The regression model confirmed age and shift

work were associated with cancer in AOP. However, the greatest effect associated with

cancer in the respondent population was shown to be frequent to constant anxiety.

Stress and anxiety are controversial in determining the risk for cancer and it is thought

that any link may be due to secondary affects such as tobacco smoking, alcohol and

overeating and that stress may be a by-product rather than a risk factor for cancer

(Heikkilä et al., 2013). However, this is the first time that these associations and

predictors have been demonstrated in AOP. That is, this research has shown an

association between being an AOP and cancer, primarily through high rates of anxiety.

It is clear that ambulance services have to act to reduce anxiety that is associated with

work, the organisation of the work and the working and organisational environment

for AOP.

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Discussion

Cardiovascular disease (CVD)

As with cancer, CVD is linked to shift work especially night shift workers

(Mosendane & Raal, 2008). It was also linked to adverse risk factors such as obesity,

diet, tobacco smoking and poor sleep (Ramin et al., 2015). There is evidence that CVD

may be linked to ambulance work, most likely through risk factors associated with

chronic and acute organisational and ambulance specific risk factors (Sterud et al.,

2011; Hegg-Deloye, Sandrine et al., 2013). The AHS 2015 added to this evidence,

finding respondents with a rate of CVD 2.5 times that of the Australian population.

Supervisor/managers were more likely to report CVD, which may reflect ambulance

stressors, age and years of shift work. Those with moderate and severe psychological

distress, a disability, fair to poor self-reported health, asthma, a cancer diagnosis,

diabetes, arthritis, feeling depressed, overweightness, low job satisfaction, sleep < 6

hours and poor to very poor sleep quality on rostered days off were all more likely to

report CVD.

Whilst variables that were shown to be associated with the CVD in the

respondents were included in the regression model based on their effect size or were

contextually significant, only one variable was statistically significant in the final

model. Very poor, poor and fair sleep quality on rostered days off was shown to

slightly increase the risk of having CVD. The AHS 2015 collection of sleep hours were

categorised into those on rostered days off that were ≥ 7 hours and ≤ 6hours. There is

evidence, based on this categorisation of sleep hours that people who sleep ≤ 6 hours

per night have a relationship to CVD (Grandner et al., 2010) and mortality (Hublin,

Partinen, Koskenvuo, & Kaprio, 2007). In a meta-analysis of sleeping problems,

PTSD, obesity and CVD in paramedics, it was shown that sleep problems were

prevalent in this group of workers secondary to acute and chronic stressors that can

245


lead to the development of CVD (Hegg-Deloye, Sandrine et al., 2013). It should be no

surprise that CVD was worse in the respondent population with their reported decrease

in sleep hours, high rates of psychological distress and acute and chronic stressors and

increased fatigue. Patterson, Buysse, Weaver, Callaway, and Yealy (2015) reported

that sleep quality was related to CVD through secondary measures such as a weaker

recovery between shifts and the same relationship is very likely in this research. What

needs to be looked further is whether AOP have enough time to recover between shifts

or blocks of shifts, especially given the length of shifts, irregular breaks and extended

shift hours. However, Sofianopoulos et al. (2012) suggested there was a lack of

literature showing the effects of poor sleep quality, low sleep hours and shift work in

AOP. Lastly, sleep was also related to self-reported health and overweight in the

regression modelling. So, why is sleep poor? This is discussed in the section 5.2.3.

Sleep is a complex issue made more so by being an AOP who works shift work, with

high rates of chronic disease and overweightness which were predictors of poor sleep.

The strategies for improving sleep will be controversial. Based on current evidence,

they may include pre-employment testing for shift work disorder, circadian principles

in designing rosters, changing shift patterns, pharmacological interventions, napping

strategies for work (this will be difficult to incorporate into an ambulance operational

environment) and home and bright light exposure on night shift (but this has been

implicated in cancer).

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Discussion

Diabetes

Risk factors for diabetes are known to include insufficient physical activity,

saturated fat intake, obesity and tobacco smoking (Myint, Luben, Wareham, Bingham,

& Khaw, 2009). There is evidence to link shift work, particularly rotating shift workers

to diabetes (Bannai et al., 2016; Claus, Schuster, Oberlinner, & Webendörfer, 2017).

It would be logical to suggest that diabetes was a risk for AOP with factors such as

shift work, sedentary behaviour, obesity, stress and sleep problems shown to be high.

The rate of diabetes in the respondent population (4.4%) was slightly higher than

the Australian population (4.0%) and increased with age. Males report more diabetes

than females. EMDs and supervisor/managers were more likely to report diabetes as

were paramedics who had severe psychological distress, poor to fair self-reported

health, a disability, CVD, a back problem, a diagnosed mental health condition, a

cancer diagnosis, personal and family stressors, overweightness, hypertension, slept <

6 hours per night on rostered days off, or had poor to very poor-quality sleep. However,

the regression model showed a different picture, with three statistically significant

predictors of diabetes that included BMI, fatigue and age. These three predictors were

also linked to diabetes in the literature review (Studnek, J. R., Bentley, et al., 2010;

Heraclides, Chandola, Witte, & Brunner, 2012; Zahra, Lee, Sun, & Park, 2015; Claus,

Schuster, Oberlinner, & Webendörfer, 2017). However, each of the variables

associated with diabetes for paramedics had an association with BMI, fatigue and age.

For instance, independent variables such as gender, age, being a supervisor/manager

and psychological distress were all predictors of being overweight. Apart from the

obvious strategies for reducing the risk of diabetes such as weight reduction, improved

diet and increased exercise, there will need to be consideration given to monitoring

work hours. Long work hours were associated with an increased risk of diabetes which

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may be associated with secondary factors such as fatigue. This research showed that

38.4% of respondents worked ≥ 45 hours per week. This is the point at which Bannai

et al. (2016) described shift workers experiencing greater risk of diabetes. From the

perspective of AOP, long hours work was related to higher income, but from an

organisational perspective, long work hours were associated with increased injury and

illness. Controlling working hours may mean controlling involuntary and voluntary

overtime and lead to increased cost with the need for more AOP. It may also lead to

increased numbers of AOP seeking other employment on a casual or full-time basis to

increase income. This is an interesting discussion, as it was raised in the semi-

structured interviews as to what point does an employer take responsibility for the

health of employees, if at all. However, if employers allow employees to work longer

hours (e.g. involuntary overtime and overtime shifts), and health status is associated

with longer hours, it follows that employers should take partial responsibility for the

health of employees.

Obesity

Obesity was shown to be linked to diabetes in the Whitehall II study (Heraclides

et al., 2012) and to Danish health care workers (Poulsen, Cleal, Clausen, & Andersen,

2014), which did not include ambulance personnel. The Whitehall II study indicated

that diabetes was associated with work stress which was recognised as being high in

ambulance services and includes acute and chronic stressors as a result of the work,

the working and organisational environment (Wong et al., 2012; Hegg-Deloye,

Sandrine et al., 2013; Mirhaghi et al., 2016). Fatigue, which is known as a symptom

of diabetes may now be a risk factor for diabetes with a relationship shown to extreme

sleepiness, depression and obesity (Gangwisch et al., 2007). Similarly, age has also

been shown to be a risk factor for diabetes (Petit et al., 2001). Obesity was one of the

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Discussion

individual factors that can potentially be controlled and may be reduced with lifestyle,

organisational and work changes that are predictors of obesity. Obesity was one of the

more important outcomes of this research as it has been related to increased injuries at

work, poor performance and increased morbidity of patients (Finkelstein, 2010; Hegg-

Deloye et al., 2013). It may be that changing the organisation of the work and

improving the work and organistional environment may be more effective than

introducing controversial measures that decrease the income of AOP and increase

organisational costs.

Asthma

AOP reported (27.3%) which was greater than the Australian population.

Asthma has not previously been reported in AOP. Weight gain and higher BMI have

been implicated in asthma incidence and severity, although the mechanisms of which

are not understood (Chipps et al., 2012). Those in the ambulance population who

reported asthma were more likely to have emphysema, hay fever, allergic rhinitis,

bronchitis, sinusitis and other allergic conditions. The regression model developed in

this research confirmed that BMI and hay fever were predictors of asthma in this

respondent population. Even though BMI was found to be not statistically significant,

it was included because it was contextually significant. This raises the question of

whether an ambulance service should exclude employing a person because they have

a history of emphysema, hay fever, allergic rhinitis, bronchitis, sinusitis and other

allergic conditions? It follows, that ambulance services would need to look at all the

risk factors for chronic disease, susceptibility to obesity, long term conditions,

disability and musculoskeletal injuries and could potentially exclude most applicants.

Excluding potential employees based on some of these risk factors is discriminatory

(Bennington & Wein, 2000). For instance, should a person who has Type One Diabetes

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that is well controlled and poses no risk to that person, patients or the ambulance

service be excluded from employment? If such an approach was taken it could amount

to discrimination against not only potential employees but those AOP who are already

employed and develop a chronic disease. This is similar to an AOP who is tested

functionally, physically and psychologically and is employed as an AOP in an

ambulance service, but over time is unable to meet these standards. It may be one of

the reasons that ambulance services do not enforce physical, functional and

psychological health standards for AOP. These standards are implicit in the

employment contract for an AOP, but an ambulance service would not know, if for

instance, a person had PTSD and used private health care providers to assist in coping

with this mental health disorder.

Mental Health

There has been considerable focus on the mental health and resilience of AOP

(Courtney, J., Francis, & Paxton, 2010; Wild et al., 2018). Experiences of mental

health range from feelings of stress through a continuum of anxiety and distress to

overt mental illness. Five-point one percent (5.1%) of respondents to the AHS 2015

reported severe psychological distress in the four weeks before taking the survey. This

does not necessarily translate into mental illness, as the rate of diagnosed mental illness

was 11.3% compared with 13.6% in the Australian population. However, the

ambulance population is a working population, whilst the Australian population

includes those who do not and cannot work. This was contrary to research from

Norway into police and ambulance personnel (Berg, A. et al., 2006; Sterud et al.,

2008a) and in Australia (Petrie et al., 2018) where the opposite was shown. This lower

mental health rate in the ambulance service being researched, may reflect the

psychological support program, which includes education, prevention, counselling and

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Discussion

a peer support component and focuses on the issues that keep an AOP mentally healthy

and post traumatic growth. Additionally, it has been suggested that the majority of

AOP will grow psychologically from their experiences (Scully, 2011). Using the

Kessler psychological distress scale (K10), only 1.8% of the respondent population

did not report feeling depressed in the four weeks before the AHS was completed by

respondents and of those 78.4% reported feeling depressed ‘most of the time’ to ‘all

of the time’. It is unknown why; nevertheless, it is an important issue, as it contradicts

these research findings of 7.5% with a diagnosed depressive disorder. There could be

a number of reasons for this which could be include a fear of speaking up about a

mental health disorder as this could affect promotion prospects, moving to another

clinical level, or being moved to another position and fear of job insecurity. This lower

rate of diagnosed depressive disorders could be partly a result of psychometric testing

and employing those who will grow as a result of their experiences and a focus by the

ambulance service being studied on post traumatic growth.

These research findings in regards depression are contradicted by (Courtney, J.

et al., 2010; Courtney, James A. et al., 2013) who report that paramedic shift workers

were at high risk of depression. This adds further evidence to what is becoming a more

complex issue with every paragraph. The regression modelling showed that poor self-

reported health, chronic disease and job satisfaction were all predictors of a mental

health diagnosis and psychological distress. It once again points to not only lifestyle

factors but organisational issues impacting on the health of AOP and the need for

programs that consider all modes of health improvement, not just those that improve

mental health.

A regression model was developed for psychological distress and included final

predictor variables of self-reported health, cancer, and job satisfaction which was

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linked to mental health disorders. Job satisfaction, anxiety, poor self-reported health,

cancer and asthma are now linked to a mental health disorder and psychological

distress in AOP. There has been a suggestion that length of service may make an AOP

more susceptible to post traumatic stress disorder and troubling thoughts (Bennett et

al., 2005). This research found little variation in the K10 score and self-reported health

associated with years of service or total shift work experience. This may be a result of

survivor bias where AOP who are unhealthy, leave work earlier and consequently

accrue less exposure compared to their healthier counterparts (Buckley, Keil,

McGrath, & Edwards, 2015). However, years of service were associated with an

increase in mental health diagnoses including depression and PTSD. When the

statistically significant regression model was finalised, it contained three predicator

variables of anxiety, including asthma and psychological distress (K10). Interestingly,

K10 was included as an independent variable in the regression modelling for mental

health disorders, as these two variables were not statistically correlated. These

variables all predict statistically significant increases in mental health disorders

associated with being an AOP. Anxiety and psychological distress have been shown

to be associated with mental health disorders in AOP (Petrie et al., 2018), however

asthma has not. In contrast, this relationship between a mental health disorder and

asthma has been shown to exist in the general population. Relative to adults without

asthma, respondents who had a mental health disorder had an OR = 1.6 95% CI (1.4-

1.8) for depressive disorders and an OR = 1.5 95% CI (1.4-1.7) for anxiety disorders

(Scott, K. M. et al., 2007). This association may be related to secondary issues

associated with mental health disorders such as tobacco smoking, allergic rhinitis and

lack of exercise (Kim, Han, & Kim, 2016). Anxiety and psychological distress have

been shown to have a relationship with AOP’s mental health (Sterud et al., 2006;

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Discussion

Sterud et al., 2008a; Sterud et al., 2011), which was exacerbated by fatigue and sleep

problems (Courtney, J. et al., 2010).

5.2.3 Factors Influencing the Health of AOP

The literature review identified three broad categories of factors associated with

the health and wellbeing of ambulance operational personnel. These include the

characteristics of individuals, the working environment and the organisational

environment. Age, employment type and gender were included in all regression

modelling as independent variables (as they were potential confounders), and

qualifications and station category were included where there was a significant effect

size or contextual significance.

Individual characteristics

This section is an important component of the research as it gives an indication

that some aspects of work design in the ambulance service can affect caring for one’s

self. For instance, receiving formal rest breaks or the timing of those rest breaks was

not a choice for operational personnel, as operational demands take precedent. Gender,

barriers to exercise, being involved in wellness programs and hours worked all have a

component that was affected by the organisation of the work and subsequently impact

on an employee’s ability to care for one’s self.

Demographic Characteristics

The AHS 2015 confirmed that the demographic characteristics of AOP influence

their health status.

Age

Age was primarily associated with chronic disease in the cross-tabulation

analysis. However, the regression modelling provided a very diverse delineation of

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age, which is shown Table 4.3-33. Age in AOP was a statistically significant predictor

of self-reported health, disability, cancer, diabetes, three of more chronic diseases, job

satisfaction, thoughts of leaving, fatigue and lack of time and energy for exercise in

the respondent population (see Figure 4.1). After the age of 45, there is a decline in the

proportion of female AOP who are employed. It certainly isn’t because of policies that

reduce aged employees as they would be regarded as discriminatory, but the results of

this research do indicate a need for action in this group of AOP in relation to their

health. Similar to Australian firefighters (Walker, Driller, Argus, Cooke, & Rattray,

2014), older AOP have demonstrated poorer health status. These changes need to be

considered in terms of employment type and work ability changes. Health promotion,

fitness, dietary, work type, rostering arrangements, and psychological support

programs will need to be considered in terms of improving the health of this group.

The regression modelling showed age as a predictor variable for multiple dependent

variables, some of which were positive in nature. For instance, as AOP aged job

satisfaction improved and thoughts of leaving declined.

Gender

In considering gender, the literature has described that females may be more

susceptible to musculoskeletal injuries (Maguire, Brian J. & Smith, 2013). The AHS

2015 found AOP to be on average younger than the general population and

increasingly female. The ratio of males to females varied considerably within the

different employment groups: EMDs (29:71), Advanced Care Paramedics (62:38),

Critical Care Paramedics (83:17) and supervisor/managers (81:19). This may reflect

recruitment trends or lifestyle choices or lower rates of female participation in the

ambulance workforce previous to this research. In addition, the permanent part time

female workforce was three times that of their male counterparts and they were more

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Discussion

likely to rate their health as poor or fair and be overweight than their male colleagues.

There were several gender based statistically significant relationships discovered in

the cross-tabulation analysis and these were included in the regression modelling.

However, gender was found to be a predictor of only one dependent variable. Males

were found to have a decreased risk of participating in a formal rest break, which may

be related to the ratio of males to females in the supervisor/management group who

were also found to be a predictor of not having a rest break. Whilst this research did

not find causes, the argument that males were less likely than females to have a regular

rest break seems to be weak. There was no difference in getting formal rest breaks in

the younger age groups between females and males. The over 45-year age group was

slightly lower in reporting not getting regular rest breaks and may relate to this group

of males having worked in the ambulance service for longer and having different

generational work-life expectations. However, generally the distribution of rest breaks

was evenly distributed between age groups except that the proportion of those < 45

years of age was nearly twice that of those ≥ 45 years of age.

Qualifications of AOP

This is the first known research that seeks to relate health status to qualifications

of ambulance personnel. The AHS 2015 did not distinguish between graduate and non-

graduate paramedics and it was difficult to do so by looking at those who have an

undergraduate degree, as many non-graduate paramedics have completed a degree

after entering the service. However, those less than 34 years of age were more likely

to be graduate paramedics and there was no evidence to indicate that those in the 15-

34 year age group had worse self-reported health, chronic disease or stress. There was

only one statistically significant relationship found in the regression modelling for

education/qualifications. Those who had more than a year 10 high school education

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had an increased risk of a poor job satisfaction score. This is a noteworthy result, as

there was evidence to indicate that nurses with higher education levels have less

organisational commitment (Alexander, Weiss, Braude, Ernst, & Fullerton-Gleason,

2009) and job satisfaction (Lu et al., 2012). In addition, the AHS 2015 found that those

with tertiary qualifications were the group most likely to consider leaving the

ambulance service. There is a need to further explore the factors associated with more

highly educated paramedics in relation to job satisfaction and intent to leave as the

evidence is contradictory.

Service Location

There was some inconsistency in the literature about the impact of service

location (rural vs. urban) on health status of paramedics. Courtney, James A. et al.

(2013) concluded the health status of rural paramedics was the same as that of

Metropolitan paramedics. Pyper and Paterson (2016) suggest normal levels of stress

in rural paramedics and that this may have been mitigated by working with known

people in the rural communities. The AHS 2015 results add to the contradictory nature

of the evidence. Those respondents who worked in rural stations were more likely to

report severe psychological stress than those who worked in urban stations. This is a

very important point, as it may mean that those who work in rural stations have less

psychosocial support and may need increased psychological interventions and physical

support than their urban colleagues. Isolation, limited workload, deskilling and limited

social opportunities may all contribute (Terry, Lê, Nguyen, & Hoang, 2015;

Humphreys, Wakerman, Pashen, & Buykx, 2017). However, there was no difference

in station category for mild psychological stress or for those who were well. Service

location was determined by using the ambulance service station categorisation system.

(e.g. a category five station is a busy station situated in an urban environment and

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Discussion

provides multiple ambulance vehicles 24 hours per day with two paramedics on each

car). Regression analysis provided a dissimilar picture and determined only one

variable that was statistically significant in relation to service location. Those who

worked in category five stations had an increased risk of developing a disability. This

was intuitively sensible, as fatigue has previously been related to disability in

Australian paramedics (Paterson, Sofianopoulos, & Williams, 2014) and the AHS

2015 found fatigue to be higher in category five stations, which are known to be busier.

The AHS 2015 also found that as paramedics’ age, they tend to move away from

category five stations. This suggests the need for a system that considers workload

amongst paramedics in terms of reducing the burden of disability and fatigue that was

shouldered by those who work in busier stations and the impact of working with and

knowing their own community

Diet

Diet impacts on health status and is known to be disturbed by shift work

(Atkinson et al., 2008). In the AHS 2015, those who had one serve of fruit or less per

day were more likely to have moderate to severe psychological distress and were in

turn more likely to have CVD, diabetes, asthma, or a disability compared to those

respondents who consume more than one serve of fruit per day. This was consistent

with a study by Regehr, C., Goldberg, Glancy, et al. (2002). Diet was determined by

asking respondents about the number of serves of vegetables and fruit they consumed

in a day and was included in many of the regression models. However, the only

statistically significant predictor for diet was found to be serves of vegetables in

relation to lack of energy in relation to barriers to exercise. Eating quickly and eating

until full, has been reported in the literature as contributing to obesity (Maruyama et

al., 2008). One aspect of being a paramedic that fosters eating quickly is the unknown

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nature of when and where the next case will arise and the lack of scheduled rest breaks.

In all categories of employment, those who do not get regular rest breaks tended to be

overweight. Female EMDs were more likely to be overweight compared to their male

counterparts. Overall, supervisor/managers were more likely to be overweight than

EMDs and paramedics (this was the group of respondents that has a decreased risk of

having a rest break). Whilst there are many ways in which this may be improved,

scheduled and formal rest breaks and constant reinforcement of the importance of diet

through education and health promotion that may change the choices AOP make in

relation to diet.

Obesity

Obesity has been associated with chronic disease, disability, fatigue and sleep

problems (Kopelman, 2007) and was one of the major biomedical risk factors linked

to the onset of chronic disease. Obesity was measured in the AHS 2015 using BMI and

waist/hip ratio and was included in many of the regression models as an independent

variable. Obesity was found to be a statistically significant predictor of self-reported

health, asthma, diabetes, arthritis, three or more chronic diseases and sleep hours.

Respondents to the AHS 2015 who were overweight, reported chronic disease, CVD,

back problems and high levels of fatigue. They also reported lower good quality sleep

on rostered days off. There was also some suggestion that psychological stress

commonly associated with ambulance work may be associated with obesity (Svedin,

Norrlander, & Fläckman, 2012). What is not known was whether this stress was a

causative factor in obesity or vice versa. Overweight AOP were more likely to report

negatively in relation to feeling positive at work, having good friends at work, feeling

recognised and appreciated, respecting the work of peers and their supervisor caring

about them as a person. Overweight respondents were more likely to report the job

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Discussion

they were in was depressing and have thoughts of leaving the ambulance service.

Obesity has been shown to be related to paramedics (Hegg-Deloye, Sandrine et al.,

2013) and EMDs (Lilly et al., 2016) and as a risk factor for CVD (Hegg-Deloye, S. et

al., 2015) and it was thought that this was related to PTSD, occupational stress, alcohol

abuse and depression.

Exercise

Courtney, J. et al. (2010) suggested paramedics did less exercise than the general

community and had greater levels of fatigue, anxiety, stress and poor sleep. Low self-

reported health of paramedics (Sterud et al., 2006; Suzuki, Yoshioka, Ito, & Naito,

2016) and nurses (Malinauskiene et al., 2011) was related to low physical activity.

Those in the AHS 2015 who reported exercising more, had a decreased risk of poor

health, a cancer diagnosis, asthma, diabetes, CVD, a disability and back problems.

Sitting hours were not correlated with exercise hours and were therefore considered

separately in this discussion.

This added to the contradictory evidence as to whether physical fitness was

protective towards musculoskeletal injuries in paramedics (Broniecki, M. et al. (2010);

Jenkins et al. (2016). There is generalised support for workplace physical activity

programs, but the evidence as to their effectiveness is limited or inconclusive (Proper

et al., 2003). Additionally, Fennell, Gerhart, Seo, Hauge, and Glickman (2016)

reported no improvement in physical activity or health related outcomes with

incentive-based exercise programs. In contrast, the 45 and Up Study of Australian

adults (Rosenkranz, Duncan, Rosenkranz, & Kolt, 2013) indicated that increased

physical activity and lowered sitting time were associated with excellent health and

quality of life. Marques, Santos, Martins, Matos, and Valeiro (2018) reported that

physical activity was associated with lower risk of chronic disease and this was also

259


reflected in the AHS 2015 data analysis. Intuitively, lack of time may be related to

lower exercise hours. The regression modelling indicated that lower exercise hours

could predict lack of time and energy as a barrier to exercise. Other predictors for both

lack of time and energy as a barrier to exercise include age, diet and thoughts of

leaving. For lack of energy, predictors not already mentioned include alcohol

consumption, rest breaks and being an EMD, whilst for lack of time the remaining

predictor was psychological distress. There needs to be more research on the physical

work demands and whether this has an incidental exercise component and if AOP are

fit for the task. This one point raises a range of social, industrial, lifestyle and work

related issues that are complex and needy of further research.

In addition, the effect of exercise on the timing and content of food intake and

shift work was not known. However, exercise in shift workers has been shown to have

some favourable impacts on fatigue (Atkinson et al., 2008) and workplace based

physical activity programs may provide some improvement whilst further studies are

completed. The AHS 2015 demonstrated no statistically significant association

between low levels of exercise and chronic disease. This was a disappointing result as

there was ample evidence in the literature to indicate that physical activity has a

protective affect with non-communicable disease (Lee et al., 2012). However, caution

should be exercised when considering this result of the AHS 2015, as only 223

respondents answered all question that allowed an accurate summation of exercise

hours, as the questions required respondents to think clearly about their exercise over

the last two weeks in comparison to the Australian health survey where exercise was

judged on pedometer readings which included formal and incidental. In addition, it is

known that there is a social desirability bias associated with self-reported exercise

(Brenner & DeLamater, 2014; Brenner & DeLamater, 2016). Exercise is clearly an

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Discussion

important issue in the health of an individual, and ambulance services need to, in the

first instance, action initiatives that reduce the factors known to be associated with lack

of time and energy to exercise. That is, targeting the factors that are known to be

associated with physical activity (e.g. age, sex, health status, self-efficacy and

motivation) (Bauman et al., 2012) may be a more effective method of assisting AOP

to improve health than providing facilities and programs of exercise.

Sitting

In relation to sitting, those who sit for longer hours (sedentary behaviour) are

known to be linked to increasing obesity, Type 2 Diabetes and CVD (Kolt et al., 2017).

The average sitting time of the respondents was 11.5 hours on a working day, with

supervisor/managers and paramedics more likely to sit from 10-14 hours on a working

day. EMDs sat for greater than 14 hours on a working day (this was the total sitting

time in a working day, at work and in other activities such as sitting whilst driving,

eating and watching television which may have been at home). This was an important

finding, as Banes (2014) described long periods of inactivity interspersed with high

physical demands in firefighters leading to high rates of obesity and CVD.

The AHS 2015 showed respondents with increased sitting hours had an

association with CVD, obesity and diabetes in the cross-tabulation analysis. The

regression modelling provided further insight into the effects of increased sitting hours

which were a statistically significant predictor of long term conditions such as hay

fever, sinusitis, allergies and bronchitis. The predictors of sitting hours included being

an EMD or supervisor/manager, younger age group, gender, station category, alcohol

consumption and weight. Increased sitting time was a statistically significant predictor

of increased chances of having a rest break. This poses a dilemma as those with the

greatest sitting hours (EMDs) have a rest break built into their roster, but

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supervisor/managers who had the second highest sitting hours were the group who had

the highest risk of not getting regular rest breaks. It is clear that having a regular rest

break is of critically significance for the health of AOP and ambulance services need

to consider developing policies that ensure formal rest breaks are available to all AOP.

This should be in the context of what a regular rest break is known to be rather than

what the ambulance service or policy makers think it should be. There is no room in

this issue for an individual interpretation of what is a regular rest break.

Sleep

Kukowski et al. (2016) described sleep as having a mitigating effect on stress in

paramedics (Manoharan & Jothipriya, 2016). Caution should be taken in interpreting

these results as there have been many changes, one of which was a move from mainly

10 hours shifts towards 12-hour shifts in the ensuing 13 years. However, this needs to

be framed contextually. The ambulance environment is unpredictable and working

more hours than a designated shift length is a common issue. In this example, 13 years

ago, paramedics especially, may have worked up to 12 hours on a shift (base shift of

10 hours) and now can potentially work up to 14 hours on a shift (base shift of 12

hours). A comparison of sleep hours from 2003 and 2015 is provided in Figure 4.5 and

has significant safety implications such as increased rates of AOP injury, medical

errors and safety compromising behaviour.

Sleep Hours

The AHS 2015 showed a positive association between sleep hours and job

satisfaction and an inverse association between sleep hours and work-related health

culture, personal and family stressors and anxiety. A positive association was shown

between sleep and fatigue in the AHS 2015. In summary, sleep ≥ 7 hours per night on

rostered days off predicted decreased stress, family stressors and anxiety and an

262

Discussion

improved work-related health culture and job satisfaction. The respondents who

indicated improved health status with increased sleep (> 7 hours on rostered days off

also showed a reduced likelihood of a mental health disorder, a lower K10 score and

chronic disease.

Sleep quality

Poor sleep quality in paramedics has been linked to health, occupational duties

and personal relationships and associated factors such as CVD, lifestyle behaviours,

job conditions and psychopathology (Hegg-Deloye, Sandrine et al., 2013). Courtney,

James A. et al. (2013) indicated that the rural cohort of paramedics was more likely to

have a poorer sleep quality. The explanation may lie in the on-call component of some

rural paramedics. The AHS 2015 showed that paramedics in category one stations

were more likely to sleep less than six hours per day on rostered days off. This is an

incongruent result, as category one stations are likely to be the least busy work areas.

Category one and four station respondents report poor sleep quality as did

Supervisor/Managers and EMDs. Regression modelling provided further evidence that

sleep has a protective effect. Fair sleep quality was shown to reduce the risk of

developing a long-term condition, but not so with CVD. Respondents who reported

fair and poor sleep quality were shown to have a slightly increased risk of developing

CVD. Increased shift work years and BMI predicted an increased risk of sleeping ≤ 7

hours and good and excellent self-reported health showed a decreased risk of sleeping

≥ 7 hours on rostered days off. Whilst ambulance services spend time educating

employees of the importance of sleep and how to manage sleep, shift work and stress,

especially for shift workers, up to 31% of ambulance employees may have shift work

disorder. This is a clinically recognised condition that may make a shift working AOP

incompatible with shift work (Wright et al., 2013). Fatigue and poor sleep have

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enormous safety implications for individual AOP, patients, the community and the

ambulance services. The rest break issues are significant and are less than the

prescribed breaks from driving in the transport industry with far greater safety

consequences (NHVR, 2019). There are no known education strategies that are

delivered to AOP, nor is the condition widely known amongst this group of employees.

It is imperative that consideration be given to testing for shift work disorder in AOP

and that strategies be developed to assist this group of workers with reduced exposure,

as not only can it affect worker wellbeing but has the potential to increase fatigue and

decrease performance and impact on patient morbidity and mortality (Brachet, David,

& Drechsler, 2012) .

Alcohol

Despite the latest evidence in relation to there being no safe limit of alcohol

consumption in relation to the risk of cancer (Connor, 2017), the AHS 2015 did not

find any link, either through the cross-tabulation or regression analysis to cancer. Six

percent of respondents drank alcohol daily compared to 8% in the Australian

population (NHMRC, 2009). Respondents had a mean weekly consumption of alcohol

of one drink per day and 8.3% of respondents drank at a high-risk level compared to

18.2% in the Australian population (AIHW, 2016c). This figure could be higher as

there is some evidence indicating alcohol use may be underreported by as much as

22% (Livingston & Callinan, 2015). Managers drink more often than other

employment types and EMDs drink less than other employment types. However,

regression modelling showed that the amount of alcohol consumed in one week was a

predictor of reporting lack of time as a barrier to exercise. Consuming five or more

drinks at least once per week was also shown to be a predictor of an increased risk of

being fatigued. Whilst generally, alcohol consumption in AOP’s is lower than the

264

Discussion

Australian population, it is likely that there are some AOP reporting to work with a

blood alcohol concentration higher than zero, which is the level required by the

ambulance service for AOP. This is especially important; given 8.3% of AOP are high

risk drinkers. Whilst there is no known regular testing of blood alcohol content in the

ambulance service, this is done in other public driving related services such as rail,

tram and bus. If it were to be introduced then testing for fatigue should also be

considered given the relationship between fatigue and simulated blood alcohol levels

(Williamson, Feyer, Friswell, & Finlay-Brown, 2000) There is a need to develop a

reasonable approach to this issue to protect patients and AOP, especially in the high-

risk activity of driving ambulance vehicles in emergencies.

Tobacco smoking

Tobacco Smoking has been reported in the literature in relation to paramedics in

the U.S. at 12% (Hegg-Deloye, S. et al., 2015) and 19% (Barrett et al., 2014). Tobacco

smoking rates for AHS 2015 respondents were 8.9%. Those respondents who smoked

tobacco were more likely to report poor health, severe psychological distress, sit for

greater than 14 hours per day, have had or currently have cancer, CVD, arthritis,

diabetes, hay fever, bronchitis, emphysema, overweightness, hypertension or two

chronic diseases. However, the regression modelling showed that tobacco smoking

was not a predictor for any of the dependent variables in the health status, chronic

disease, caring for self, organisational symptomology or risk factor categories.

Respondents who smoked tobacco were more likely to sleep less than six hours per

day on rostered days off and report poor sleep. Tobacco smoking rates were highest in

the 35-44 age group, and of those who smoked tobacco, EMDs smoked the most

(18.3%). There is some evidence that tobacco smoking can improve immediate

performance (Myers, 2010), yet has detrimental health affects if continued over time.

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There is a need for focused anti-smoking campaigns in the ambulance service,

especially for EMDs who have tobacco smoking rates equivalent to the Australian

population.

5.2.4 The Working Environment

AOP function in a difficult and unpredictable environment. They work long

hours and are shift workers and may be exposed to infectious diseases, emotional

stress, fatigue, occupational violence, injury and vehicle crashes. They are facing

increased workload and high work demands which have been associated with

increased anxiety and physical illness (Aasa, Brulin, et al., 2005). The AHS 2015

described respondents who worked at the busier stations, EMDs and supervisor

/managers having moderate psychological distress and increased rates of disability.

There may be other factors that contribute to this issue, such as occupational violence,

exposure to distressing life events, shift work, a high job demand and low control

environment (Cropley, Steptoe, & Joekes, 1999), lack of exposure to critical incidents

and the high expectations of graduate paramedics especially, not being met.

Occupational violence

Up to 90% of ambulance personnel have reported violence, both physical and

verbal, and its effects have been known to effect social support, job satisfaction,

psychological well-being and lead to increased levels of anxiety (Brough, 2005b).

Workplace violence was not assessed in the AHS 2015, however evidence from the

literature (Maguire, Brian J et al.(a), 2018, Maguire et al (b)., 2018) and the ambulance

service where this research was undertaken (QAS, 2016a), indicates that occupational

violence is increasing. The need for high quality national and international research in

this area is increasing and should be conducted with universities that have high quality

researchers, an interest in occupational violence and strong links with ambulance

266

Discussion

services. The research needs to be focused on the causes, prevention, protection and

solutions to the issue of occupational violence (Maguire, Brian J et al., 2018). It needs

to build on previous research, not just indicate that further research is needed – that is

obvious. One way of focusing this research would be to create a consortium of

universities that have an investment in paramedicine and the state ambulance services

in Australia to form a Center for the Prevention of Occupational Violence in

Emergency Services.

Formal rest breaks

Irregular meal timings have been implicated in weight gain (Jakubowicz et al.,

2012) and an increased likelihood of sustaining a musculoskeletal injury in both

Australian (Broniecki, Monica et al., 2012) and U.S. (Dropkin et al., 2015) ambulance

services. The AHS 2015 found irregular rest breaks were associated with poor health,

back problems, psychological distress, bodily pain, thoughts of leaving and increased

sitting hours. Fatigue and subsequent risk of injury has also been implicated in

irregular rest breaks (Weaver et al., 2015a). The AHS 2015 showed regular rest breaks

were associated with decreased fatigue and barriers to exercise, increased job

satisfaction, performance, sleep quality and a more positive workplace. Not having

regular rest breaks and being an EMD was shown by regression analysis as a predictor

of lack of time to exercise. An insight into the different employment groups and why

they do not or cannot take regular rest breaks is provided in Table 4.2-25.

. Thirty nine percent of supervisor/managers and 40% of paramedics indicated

they do not take regular rest breaks because of work pressure. A slightly smaller

proportion indicated they do not take regular rest breaks, because they eat on the run.

A model of predictors of rest breaks was developed using binary logistic

regression and indicated that males, supervisor managers, paramedics, respondents

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who had frequent to constant fatigue showed a decreased risk of having a regular break.

Counter intuitively, those who showed an increase in sitting time (primarily EMDs)

had an increased risk of having a regular break. In 2000 the Australian Council of

Trade Unions published a paper on health and safety guidelines for shift work and

extended working hours (ACTU, 2000). One of the measures that was recommended

was formal breaks during shifts.

The true meaning of a break is where a worker cannot work or have to worry

about going back to work and may include a napping strategy (Garbarino et al., 2004;

Brooks, A. & Lack, 2006; Thorpy, 2010). Whilst there are different strategies, lengths

and conditions governing rest breaks throughout the world, Work Smart U.K. (2017)

has a succinct definition: “A rest break is an uninterrupted period of at least 20 minutes

during which work should not be undertaken. A period of downtime when you are

allowed to stop working but must stay in contact with your employer is not a rest break,

even if it turns out at the end of the break that it was uninterrupted”. Eighteen years

after this paper was published, apart from EMDs who have regular rest breaks built

into their shift, this issue has not been resolved in the ambulance service where the

research took place. It should be noted that even though EMDs have regular rest breaks

built into their shift, they have reported through the AHS 2015 that they sometimes do

not get a regular rest break. Both paramedics and EMDs work shift work schedules

and if regular breaks can be built into one shift work schedule for EMDs, they can be

built into a shift work schedule for paramedics. Both paramedics and

supervisor/managers have the same two top reasons for not having breaks: pressure to

get work done and eating on the run There was a positive correlation between these

two variables, with pressure to get work done associated with eating on the run. It is

logical to suggest that reducing pressure to get work done, which is not necessarily

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Discussion

urgent clinical work, will reduce the temptation to eat on the run. Therefore, an

organisation that insists and schedule breaks for all AOP, is critical in improving the

health status of that group of workers (Chastin, Egerton, Leask, & Stamatakis, 2015).

Shift work

There was a difference between shift work disorder (SWD) and the normal

disruptions to work-life that shift work entails. There was some evidence that shift

work may be a causative agent in reported levels of distress, fatigue and health related

conditions in ambulance personnel (Sofianopoulos et al., 2012; Patterson et al., 2015).

One Australian ambulance study used shift work as the dependant variable and

reported links to fatigue, poor sleep quality and performance at work (Archer, 2012).

The AHS 2015 results for current shift workers report psychological stress and fatigue

and that respondents were less likely to report their performance as high against a non-

shift worker. Sleep hours and quality on rostered days off did not appear to be affected

by shift work.

Shift work has been reported to be associated with overweightness in ambulance

personnel (Patterson, Suffoletto, et al., 2010) and whilst the mechanisms were not

known, it was assumed that it relates to diet, sedentary behaviour, fatigue, decreased

physical activity and anxiety. In the AHS 2015, male and female overweightness

increased with increasing years of shift work and regression modelling showed a

statistically significant relationship between increasing years of shift work and

increased chances of cancer. The ambulance service operates a 24/24 shift work

environment, and this will not change. What can change however is the construction

of rosters that follow circadian principles and variations in rosters that allow multiple

choices that may best suit work-balance options for multiple AOP (Bird, J., 2004;

Abendroth & Dulk, 2011). To do this will need an assurance that remuneration will

269


not decline and one way of doing this would be to provide a composite rate of

remuneration so that moves from one roster to another will not result in a loss of pay.

Whilst a more complex system to manage and potentially more costly to maintain,

improvements in sleep and reduction in fatigue would benefit AOP, patients and

organisational performance.

Performance

Chronic health conditions have been related to reduction in performance through

work impairment (Collins et al., 2005). In this research, participants who reported high

psychological distress, a disability, bodily pain, poor self-reported health, asthma, a

cancer diagnosis, CVD or diabetes were all less likely to report their performance as

high. Those with back problems indicated their overall performance was high for only

35% of the time. Most respondents who were overweight were less likely to report

their performance as high, and those who exercised more thought their performance

was higher most of the time. These are important findings for any ambulance service

that seeks efficiencies in their operations. It appears, that if the health issues of AOP

are resolved, not only are personnel healthier, fatigue and anxiety decrease, job

satisfaction and sleep improve, but ‘at work performance’ will improve, which may

translate to decreased morbidity and mortality for patients. There was no relationship

found between performance and sleep even though it was documented in the literature

(Patterson, Suffoletto, et al., 2010). Regular rest breaks were also linked to

performance, with respondents who did not get regular rest breaks reporting they were

less likely to regard their performance as high and more likely to regard their

performance as average. In reality, even though these solutions may be complex

operational issues to resolve, they are simple measures that could have a dramatic

effect for AOP and ambulance services.

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Discussion

Fatigue

Fatigue is a known risk factor for accidents and has been related to bodily pain,

low motivation, sleepiness and many factors that make an individual susceptible to

poor decision making and slowed reflexes (Aritake et al., 2015). It is known to be

affected by poor sleep, stress and shift length and can manifest itself as depression

(Archer, 2012), and affect the safety of ambulance personnel (Patterson, Weaver,

Frank, et al., 2012). Long shift lengths were implicated with fatigue but can have

positive and negative effects (Knauth, 2007). Respondents to the AHS 2015 who

worked greater than 181 hours per month were more likely to report fatigue and

describe that fatigue as high. Respondents with constant or high fatigue were more

likely to describe their health as poor and report not getting regular rest breaks.

Fatigued respondents were more likely to have poor job satisfaction and to consider

the work-related health culture in a negative sense than those respondents who were

not fatigued. Performance can be affected by fatigue (Berg, T. I. J. v. d., Elders, Zwart,

& Burdorf, 2009). Respondents, who reported constant fatigue were less likely to

report their performance as high, and more likely to rate their overall performance as

average. Those who experienced increased fatigue when working involuntary

overtime were more likely to do less work than most other workers in the job. Given

the above, ambulance services cannot afford to continue operations that are proven to

contribute to the development of fatigue that may lead to increased incidents and

hazards that cause injuries, poor operational performance and significant risk to those

in need of ambulance services.

There was some evidence that physical exercise can reduce fatigue (Aasa, U.,

Angquist, & Barnekow-Bergkvist, 2008). Those who exercised for less than 10 hours

per fortnight were more likely to report fatigue as did those who sit for 10-14 hours on

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a work day. The respondents reported two barriers to exercise which were ‘no time’

and ‘no energy’. This section describes the fatigue quandary that AOP confront and is

shown in Figure 5-1.

Figure 5-1. The AOP Fatigue Quandary.

Whilst AOP will make personal choices about what they eat, if and for how long

they exercise, drink and smoke, there were a range of factors that will influence these

choices. A regression model showed that job satisfaction, age, alcohol and disability were

statistically significant predictors of fatigue. Poor job satisfaction predicted high fatigue

and high job satisfactions scores predicted less fatigue. Fatigue increased when alcohol

was consumed at a rate of five drinks in one session at least once per week, and with age

and disability. Therefore, the fatigue quandary was influenced by more than barriers to

exercise and is shown in Figure 5-2.

Fat

igue

Fatig

ue

↑sitting & work hour exposure ↓exercise

↓sitting, ↑exercise

No energy No time

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Discussion

Figure 5-2. The Modified AOP Fatigue Quandary.

Additionally, the work of AOP is one of high demand and low control (Regehr,

C., LeBlanc, Jelley, Barath, & Daciuk, 2007). Where they work and the type of work

they do is not a choice that they are able to influence. Other factors such as income,

education, conditions of employment, influence and social support can enhance or

undermine the health of individuals and the work environment. Both job satisfaction

and work-related health culture climate can in part, influence the social aspects of the

work environment. Van der Ploeg and Kleber (2003) hypothesised that lack of social

support at work and poor communication were the main risk factors associated with

health symptoms. It was hypothesised that workplace based social support was a

perceptive concept and includes the actuality that a person was cared for, had

assistance from other work mates and supervisor/managers and most importantly, the

organisation that employs personnel was regarded as a supportive social network. This

research provides evidence to support this hypothesis with job satisfaction being

predicted by fatigue, anxiety, job type, family stressors, age, education and self-

reported health. The work-related health culture was predicted by the thoughts of

leaving, psychological distress and having a disability. The key that links these two

elements together was thoughts of leaving, which was predicted by age, psychological

Fat

igue

Fat

igue

↑sitting & work hour exposure, ↓exercise ↑alcohol, age & disability, ↓ job satisfaction

No energy No time

↓sitting, ↑exercise

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distress, long term conditions and job satisfaction. Critical to this is how organisations

regard their purpose, which in the case of ambulance was to care for the community.

It is suggested that it should change to one of caring for those who care for the

community to create positive workplace culture impacts such as improving job

satisfaction and organisational performance and decreasing thoughts of leaving

(Braithwaite, Herkes, Ludlow, Testa, & Lamprell, 2017).

Work related health culture

Lu et al. (2012) identified that nurses with tertiary education had lower rates of

job satisfaction, which may be related to the increased expectations of nurses not being

met. Sellgren et al. (2008) showed that work unit climate was strongly associated with

job satisfaction. Respondents to the AHS 2015 with an undergraduate degree, were

less likely to report a positive work-related health culture than those with a diploma or

lower qualification. There was an association between poor work-related health

culture and obesity, arthritis, cancer, CVD, blood pressure, alcohol consumption and

diet in the cross-tabulation analysis. A model of work-related health culture was

developed using logistic regression analysis and showed that thoughts of leaving, a

high psychological distress score and a disability were predictors of a poor work-

related health culture. A high psychological distress score was in turn predicted by

a poor job satisfaction score, poor self-reported health and a cancer diagnosis, whereas

self-reported health could be predicted by diabetes, age and obesity.

Thoughts of leaving

Intent to leave or even the consideration of intent to leave (in the AHS 2015, this

was described as thoughts of leaving) can be seen as a reflection of job satisfaction

(Blau, Chapman, Gibson, & Bentley, 2011) and “job embeddedness” which was a

summation of three elements: 1) perceptions of their fit with a job, the organisation

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Discussion

and the community, 2) links to other employees, teams and groups and 3) what the

employee would say they would have to sacrifice if they left their job (Mitchell,

Holtom, Lee, Sablynski, & Erez, 2001). Thoughts of leaving were predicted by job

satisfaction, age, a high psychological distress score, a cancer diagnosis and long-term

conditions. There was interconnectedness between these variables that suggest a link

between the health of AOP and organisational symptomology, especially job

satisfaction and work-related health culture. Therefore, improving job satisfaction and

work-related health culture has the potential to improve the health of AOP.

Job satisfaction

Job satisfaction was found to be linked to personal and family stressors including

perception of a negative work-related health culture, and predicted by individual

characteristics including fatigue, anxiety and family stressors. These are significant

findings and job satisfaction was at the centre of these connections. It is reasonable to

suggest that if job satisfaction were to improve there would be a concomitant

improvement in work-related health culture and sleep, a decrease in thoughts of

leaving, rates of chronic disease, fatigue and anxiety. However, this is a difficult

association to predict because of the lack of knowledge of how happy an AOP is with

their private life. For instance, if an AOP was happy at home and individually, would

they feel happier with work or if work was better would they be happier at home, feel

less anxious and sleep better.

Self-reported health

The AHS 2015 showed that AOPs rates of mental health diagnoses and

psychological distress were lower than the general population. However, these results

should be viewed with caution, as the indications from the literature are increasingly

showing higher rates of mental health diagnoses and psychological distress in

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ambulance personnel in Australia (Varker et al., 2017; Petrie et al., 2018). In contrast,

Scully (2011) described post-traumatic growth in paramedics as result of their

experiences. These results match those from the ambulance service’s EAP and there

could be a number of reasons for this. One explanation for this lower rate of mental

health disorders in these paramedics was that the ambulance service involved in this

research has an Employee Assistance Program that includes prevention, education,

peer supporters and a range of mental health professionals state-wide that can provide

24-hour assistance to AOP and whilst available to all AOP, it has its origins in

supporting paramedics. It focuses on staying mentally healthy and post traumatic

growth. Additionally, there was no evidence in the AHS 2015 to support lower rates

of mental health disorders with EMDs or supervisor/managers. EMDs were more

likely to report severe to moderate psychological distress than supervisor/managers

and paramedics, and supervisor/managers were more likely to report a mental health

disorder than EMDs. This may reflect increased general, organisational and ambulance

specific stressors in those ambulance occupations that were little discussed in the

literature e.g. EMDs and manager/supervisors. Interestingly, regression modelling

showed that being a supervisor/manger was a predictor of poor job satisfaction, not

having regular rest breaks and working longer hours. A paramedic was a predictor of

not having regular rest breaks and an EMD was a predictor of lack of time to exercise.

This shows specific stressors were related to different employment types within an

ambulance service and adds emphasis to the need for individualised health

improvement programs that take into account the needs of different employment types.

The associations identified in the AHS 2015 between poor self-reported health

and job satisfaction support previous findings into this relationship (Faragher et al.,

2005). Job satisfaction has been identified as a significant component of worker well-

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Discussion

being with organisational support acknowledged as a major predictor of this well-

being. Other predictors of worker well-being in a U.K. study of ambulance officers

were work engagement (focused, dedicated and energetic) and psychological stress

(Soh et al., 2016). Respondents in the AHS 2015 who identified a negative job

satisfaction score were also more likely to report severe to moderate psychological

stress, poor self-reported health, back problems or disabilities.

Organisational environment

The organisational and work environment in ambulance services in Australia

(Courtney, James A. et al., 2013), New Zealand (Brough, 2005a) and the United

Kingdom (Mahony, 2001) have been shown to affect the health of paramedics. EMDs

in Australia (Shakespeare-Finch et al., 2015) have been shown to have high rates of

post-traumatic stress disorder and in the U.S., this has been shown to be related to

secondary issues such as tobacco smoking, alcohol consumption and obesity (Pierce

& Lilly, 2012). The AHS 2015 showed job satisfaction, work-related health culture,

thoughts of leaving and having a regular rest break affect the health or were affected

by the health of AOP. More specifically, these four symptomologies affected health

status, chronic disease and caring for one’s self and were exacerbated by fatigue,

barriers to exercise, sleep and in the case of supervisor/managers, hours worked.

Organisational communication is a component of job satisfaction and it has

been reported by previous researchers that 23.4% of job satisfaction was described by

communication practices within an ambulance organisation (Jules & Bourque, 2009).

Those respondents to the AHS 2015 who felt they were not informed were more likely

to consider job satisfaction in a negative sense.

Furthermore, it has been suggested that supervisors should recognise the

importance of their role in providing socioemotional support to employees in an

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attempt at reducing the impact of organisational stressors on paramedics (Arial et al.,

2011). Respondents with poor self-reported health, a mental health disorder and

psychological distress were less likely to agree their supervisor supports them and trust

the leadership team. Van der Ploeg and Kleber (2003) identified supervisor support as

one of the risk factors in predicting fatigue, PTSD and burnout at work. Importantly,

the AHS 2015 identified 42.8% of respondents reported their supervisor does not care

about them as a person, 35% reported their supervisor does not review their

performance, 68% identified their supervisor does not support a healthier lifestyle and

65% reported they do not trust the leadership team. These are critical elements in

improving the health of AOP and in providing further education to

supervisor/managers. In addition, leader support has been identified as affecting

psychological well-being, job satisfaction, work unit climate and work-life balance

(Sellgren et al., 2008; Ghorbanian et al., 2012; Mattock, 2015), and was assessed in

the AHS 2015 using the above four variables. Supervisor fitness amongst others, has

been identified as one of the factors that may influence the health of firefighters

(Dobson et al., 2013) and in the AHS 2015 supervisor/managers reported twice the

rate of poor health than paramedics which was indicative of lower rates of exercise,

increased sedentary behaviour, higher rates of obesity and longer working hours.

Intuitively, this group of workers may have been in an ambulance service longer, be

older and have a greater number of shift work years, even though they may not

currently be shift workers, which may explain the higher rates of chronic disease.

Interestingly, supervisor/managers were the one group who were most likely to

not know what was expected of them at work when compared to paramedics and

EMDs. There may be a link here to what paramedics describe as a lack of emotional

support and the health of their supervisors who may be too busy coping with a high

278

Discussion

workload, poor physical and psychological health and trying to understand their role.

They may see emotional support as taking on more aspects to an already damaged

psyche. This raises the spectre of inconsistent application and education of

supervisor/managers in relation to legislation, regulation, policy and clinical practice

guidelines or alternatively it points to supervisor/managers being so busy and having

constant demands on their time, that they were not able to pay due attention to any one

issue.

Supervisor/managers are assumed to be relaying information and caring for other

AOP as they have been trained to do. However, each supervisor has their own

perspective on policy, different clinical backgrounds, level of training and

interpretations on key performance indicators as well as desire for promotion.

However, there is a positive side to this aspect of interpretation by

supervisor/managers that allows for individual circumstances with AOP. Nevertheless,

time may be the critical issue where supervisor/managers are the group most likely to

not know what is expected of them. Pressure to get work done was also explored when

discussing irregular breaks and may also relate to AOP perspective of not being cared

for. This may lead to a loss of perspective of what their role was in their environment.

Further, work-related health culture was found to be negative overall, more so for

paramedics and EMDs and generally positive for supervisor/managers. This integrates

with theme one and three from the thematic analysis where it has been suggested that

there was lack of understanding of how the work environment impacts on AOP and

that there was a different perspective between supervisor/managers, paramedics and

EMDs. How leaders regarded their health can have a flow on impact on work-related

health culture. There is a contradiction in the AHS 2015 results where

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supervisor/managers generally report a positive work-related health culture but are

more likely to be overweight than paramedics.

Leadership

It was recognised that the quality and nature of leadership capabilities and health

of managers and supervisors can have a significant impact on workers (Sellgren,

Ekvall, & Tomson, 2008; Ghorbanian, Bahadori, & Nejati, 2012; Mattock, 2015). In

the semi-structured interviews, interviewees where given a description of

supervisor/managers that was found in the AHS2015. These descriptions included

working the longest hours, having the worst self-described health, not take regular rest

breaks, poor job satisfaction rates, were more overweight and had greater rates of

chronic disease and psychological distress. Interestingly, the non-operational group

counselling rates for managers and supervisors had increased by 300% since 2012,

which coincided with the last organisational restructure18. Supervisor/managers were

the group that were least likely to know what was expected of them by the organisation.

The response of interviewees was interpreted by the researcher as a ‘so what’ moment,

as though it was an expectation that this was what they do and how they are. The poor

health of ambulance supervisors/managers has not previously been reported in the

literature.

Intent to leave

The research from the U.S. (Blau, Chapman, Pred, & Lopez, 2009; Chapman et

al., 2009; Blau & Gibson, 2011) has related intent to leave or stay with job satisfaction,

opportunities for advancement, pay and benefits. In the AHS 2015 there was a

difference between what respondents say was consideration of intent to leave and the

18 This information was supplied by two people in separate semi-structured interviews.

280

Discussion

actual turnover rate of the ambulance service. This was supported in the literature and

in addition it was suggested the variables associated with each concept for intent to

leave and actual turnover were very different (Cohen, G., Blake, & Goodman, 2016).

There was a difference in the demographics of U.S. and Australian ambulance

services, which may account for the differences in intent to leave. For example, the

AHS 2015 showed graduate paramedics to be the single largest group who expressed

their intent to leave the ambulance service, which was not reported in the literature. A

negative job satisfaction, poor work-related health culture and high rates of

psychological distress score were related to intent to leave. Given the results of the

AHS 2015 and the relationships with intent to leave, it suggests the need for increased

attention on improving job satisfaction, work-related health culture and reducing

psychological distress as one way of improving the health status of AOP.

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5.3 Strategies to Improve AOP Health Status

A fundamental aim of our healthcare system is to prevent disease, reduce injuries

and risk so that people remain as healthy as possible with a reasonable quality of life

(QoL) (AIHW, 2018). Several challenges, such as demand growth that is faster than

population growth, having a professionally staffed ambulance service with few

volunteers and an aging population were likely to translate into increased job strain

and subsequent workplace stress (APSC, 2018). Additionally, these challenges can

lead to an expectation of higher performance of ambulance personnel, which can lead

to greater effect on employee’s health and well-being (Ogbonnaya, Daniels, Connolly,

& van Veldhoven, 2017).

Nielsen et al. (2017) identified four elements that when considered and acted

upon could lead to improved employee well-being and performance. These include job

satisfaction and shaping at an individual level, social support among colleagues at the

group level, quality relationships between leaders and employees at the leadership

level and autonomy of practice and flexible human resources practices that meet the

needs of a particular employment group at the organisational level. Job satisfaction has

been identified as low in the AHS 2015 and was linked to fatigue, anxiety, stressors

and poor self-reported health all of which have been identified as high (Hosseinabadi,

Karampourian, Beiranvand, & Pournia, 2013; Jimmieson, Tucker, & Walsh, 2016).

Job satisfaction was also linked to lower education levels and an aging worker.

Resilience was known to be built by better work cultures, healthier workers and work

redesign (Luthar, Cicchetti, & Becker, 2000). A low control high demand environment

and a younger workforce with limited experience limits the ability of an ambulance

service to allow autonomy of practice. In addition, human resource practices are tied

to policies and procedures that allow little room for flexibility and no one solution is

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Discussion

going to improve the health of AOP. Additionally, employers now have a clear

deliniation of what are the issues with AOP health. Focusing on the associations and

predictors is one way of developing strategies to improve health status in AOP.

The semi-structured interviews were designed to gather thoughts on strategies

that would help improve health status. Whilst the interviewer produced evidence about

the effects of AOP’s work on their health from both the quantitative analysis and the

literature review, the opinions of interviewees were firmly grounded in their own

perspectives and experiences which may have changed as they were promoted or

moved to different positions in the organisation. However, it was disappointing to note

this change from the non-operational personnel who all had previous operational

experience. The researcher had an expectation that the interviewees previous

operational exposure would help them understand the perspective of current

operational personnel. Their previous perspective may have changed, based on future

leanings and what this means to the individual through necessary developmental

changes that confuse organisational, operational, social and personal responsibility.

Individuals must make a choice if they want to advance in an organisation and this

progress may affect perceptions. Organisations make cultural and operational changes

to improve and flourish and some do so without considering the effect on people, work

life balance and health status (Sanderson, 2012). It is important for an ambulance

service to have people in management with previous operational experience to inform

their work as managers and maintain current exposure to operations (Parker, R. et al.,

2008; Rahati, Sotudeh-Arani, Adib-Hajbaghery, & Rostami, 2015).

An example of this was how operational and non-operational personnel think

differently about absenteeism and presenteeism. Operational personnel described the

ambulance organisation as inflexible in meeting their needs and absenteeism was

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described by this group as maladaptive coping behaviour resulting from fatigue, stress,

long shifts, lack of rest breaks and job dissatisfaction. There are multiple references to

this in the research on AOP which describes absenteeism as a product of work

attitudes, thoughts of leaving, burnout and stress from ambulance specific,

organisational, family and personal stressors, manager characteristics, human resource

management practices, worker characteristics and the work and job environment (van

der Ploeg & Kleber, 2003; Wegge, Schmidt, Parkes, & Van Dick, 2007; Sterud et al.,

2008b; Sterud et al., 2011). On the other hand, comments by non-operational personnel

suggested absenteeism in younger workers was generational and seen as an

entitlement. In doing so, it was pointed out that older workers including supervisors,

managers and executives were retiring with large aggregates of accrued sick leave.

This reflects presenteeism, which has been recognised as costlier to an organisation

than absenteeism (Gosselin et al., 2013) and may mirror the expectations of the

organisation in terms of attendance pressure factors and male role norms (Aronsson &

Gustafsson, 2005; Hansen et al., 2011). Programs including organisational leadership,

health risk screening (it is known that this is now happening for executives and senior

managers in the ambulance service), individual health improvement programs and a

supportive workplace-based culture are known to reduce presenteeism (Anderson, L.

M. et al., 2009; Anyadike-Danes, 2017) and all need to be considered as part of the

health improvement strategy. It is important that this research be used to inform

management strategies to improve AOP health and develop an evidence base that

further inform health improvement strategies.

Health prevention and promotion such as anti-smoking, workplace health and

safety, worker wellness programs and an improved safety culture should assist in

achieving a healthier environment in which people feel safe, secure and can carry out

284

Discussion

their work without fear of injury or illness (Parks & Steelman, 2008; McCleary et al.,

2017; Reed, J. L. et al., 2017). The Australian population is experiencing increasing

levels of chronic illness as are AOP. However, AOP are experiencing rates of chronic

illness that are greater than the Australian population. Strategies are needed that

promote a sense of personal accomplishment and improvement in sleep and fatigue as

a way of reducing stress and improving the health of ambulance personnel. Nielsen et

al. (2017) identified that making a change at any one of the four levels can make a

difference to employee well-being and performance and that no level was more

effective than any other.

5.3.1 Improving the Health and Safety of AOP

When health and safety is mentioned to AOP and organisations, it is thought of

in a reactive sense. This research recognises the established nature of health and safety

but argues its value and affect is limited and that a new approach to this area is needed.

From an organisational perspective, the costs associated with health improvement of

operational personal must be demonstrated as having a positive return on investment,

through improvements in productivity, job satisfaction and reductions in injuries and

absenteeism. From the perspective of the individual worker the improvement of work-

life balance, reduction in fatigue, chronic disease and the risk of musculoskeletal

injuries are important. Addressing ambulance specific and general organisational

stressors, improving the working environment and demonstrating that the employer

genuinely cares for the worker are all issues that could be considered (Lu, Barriball,

Zhang, & While, 2012). A good example of this is the concern and research that is

being done to reduce occupational violence towards AOP (QAS, 2016a; Maguire,

Brian J et al., 2018)

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Whilst these issues seem to be dissimilar, they are analogous. The principal

strategies that have been found to improve the working environment, have focused on

safety culture, leadership and engagement, risk and injury management and improving

operational performance by reducing incidents and hazards, enhancing operational

capability, reducing Lost Time Injury Frequency Rate (LTIFR) and workers

compensation claims. Despite Nielsen et al. (2017) suggesting that making change at

any level of his model is likely to lead to improvement, DeGroot & Kiker (2003),

Jenkins et al. (2016) and Anyadike-Danes (2017) suggest that single interventions

alone do not work in improving the health of employees. In addition, caution should

be exercised when looking at U.S. based programs as they were built on reducing

health insurance costs as workplaces in the US. provide private health insurance for

employees (Emanuel, Glickman, & Johnson, 2017). Strategies must be

multidimensional including training, design, exercise and assistive devices. However,

Wiitavaara et al. (2007) suggest an even more comprehensive approach that involves

an increased understanding of the construct of illness and health in a work and social

context.

Some elements that contribute to the poor health status of AOP may be

unavoidable (e.g. exposure to human tragedy) as they are an inherent part of the work.

Health promotion programs should focus on building psychological, physical and

psychosocial resilience of individuals to mitigate the risk and impact. It has been

recognised that peer involvement plays a critical role in staff psychological wellness

(Scully, 2011) and it would be logical to suggest that this approach would assist in

improving overall health of AOP. This research indicates that as an industry, AOP

have increased chronic illness, obesity, sleep issues, anxiety and fatigue. The same

principles that apply to psychological wellness could be considered in improving

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Discussion

physical and psychosocial wellness. A peer support program which can sustain health

promotion activities at the forefront of AOP, provide support, advice and referral to a

range of health providers may be an appropriate approach.

In terms of workplace health and safety, it has been acknowledged that by returning

workers back to work as early as possible, it is less likely they will have long term

disability and morbidity (Seing, MacEachen, Ekberg, & Ståhl, 2015; van Vilsteren et al.,

2015). However, a holistic approach to this issue should be considered, that focuses on the

circumstances of the injury or illness (not just the physical aspects) which informs

understanding, improvement and prevention. Additionally, concept mapping, group model

building, conjoint analysis and intervention mapping would be appropriate strategies for

developing and implementing health improvement programs for ambulance personnel

(Powell et al., 2017). An example of this approach based on the research findings showing

the complexity involved, has been developed for mental health disorders and is shown in

Figure 5.3. This model demonstrates that mental health is interconnected with multiple

variables that are related to health status, chronic disease, organisational symptomologies,

caring for self and risk factors. It is hypothesised that this complexity will be similar for

any risk factors. The model follows the relationship of mental health to multiple risk

factors and indicates a more complex interaction that just the work that AOP do. For

example, the model shows that anxiety is related to disability, where a person works, age,

poor self-reported health, work related health culture, thoughts of leaving, psychological

distress, cancer, long term conditions and job satisfaction.

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Figure 5-3. An Interconnectedness Model for Mental Health Disorders

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Discussion

5.3.2 Improving Shift Work

Shift work is a major component of the work environment in ambulance services

and it is known that some individuals may be intolerant of shift work (Wright et al.,

2013) and that it is a isk factor for some ill health conditions (Tucker, Marqui, Folkard,

Ansiau, & Esquirol, 2012; Courtney, J. A., Francis, & Paxton, 2013). Protective factors

for shift work include younger age, male gender, high scores on flexibility,

extraversion and internal locus of control and low scores on languidity, morningness,

and neuroticism and a genetic disposition (Saksvik, Bjorvatn, Hetland, Sandal, &

Pallesen, 2011). These factors can determine tolerance to shift work with reasonable

accuracy, efficiency and in a timely manner (Aeschbach, 2013). In contrast to this,

tolerance to shift work is not tested in the recruitment of AOP. On the other hand,

strategies included in the education of AOP to cope with shift work were included in

initial induction processes. In addition, there are no strategies in ambulance that focus

on Shift Work Disorder (SWD). Whilst shift work disorder is a clinically diagnosed

condition, Flo et al. (2012) indicated that SWD can be adequately assessed using three

symptom-based questions and found significant associations between symptoms of

SWD and gender, age, night work, number of nights worked, working shifts separated

by less than 11 hours, languidity/flexibility, anxiety and insomnia in the adjusted

analysis. Whilst this may be an appropriate approach for an organisation, it may be

considered as discriminatory based on age and gender alone. In addition, there was

inconsistent and limited evidence that strategies currently used to assist AOP with shift

work issues do little to mitigate the effects of shift work (Hegg-Deloye, Sandrine et

al., 2013; Patterson et al., 2015).

Wright et al. (2013) outlined strategies that may be useful in managing the

effects of shift work and SWD which included education about vulnerable times of

289


performance. They also include an individual approach from an appropriately qualified

sleep clinician that incorporates sleep promotion strategies, good sleep behaviours,

sleep napping strategies, promoting wakefulness, decreasing work hours and shift

length, bright light exposure at night (however this has been implicated in risk of

cancer) and reduced caffeine use. The concern with these strategies and particularly

reducing shift length and sleep napping strategies was that they may be discordant with

the AOP’s lifestyle and increasing workload. Longer shift lengths are preferred by

AOP, as they were seen to give extended periods of time away from work. However,

it was clear that longer shifts are detrimental to an AOPs’ health (Weaver et al., 2015b;

Patterson et al., 2016) and it was unclear if the time away from work after longer shifts

in a short period, provide enough time to recover fully before recommencing work and

reducing the impact on work competency. Conversely, compressing a working week

may improve AOP work-life balance with a low risk of adverse health effects (Bambra,

Whitehead, Sowden, Akers, & Petticrew, 2008). However, more research needs to be

conducted on this aspect of shift work for AOP, to determine the health effects of a

compressed working week.

Introducing healthy, flexible and family friendly rosters that improve

psychosocial support, reduce hours worked and include mandatory rest breaks in a

suitable environment may be methods that can provide some assistance. However,

these rosters can have unintended negative impacts such as reduced employment

participation especially for women, reduced career opportunities and access to

preferred roles. Skinner and Chapman (2013) suggests these types of rosters will only

be successful if the workers do not experience economic, social or career penalties.

Aiming for the roster that had the most evidence to show the least damaging effects on

an individual (e.g. working only one type of shift roster e.g. day shift) may be a strategy

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Discussion

worth considering. However, this needs to be considered also in terms of work ability

as AOP age. Sallinen and Kecklund (2010) reported that despite the shift system, night

and early morning shifts and quick returns to work are associated with short sleep

durations and increased sleepiness (Sallinen & Kecklund, 2010). The balance is very

difficult to manage in meeting the needs of AOP and the organisation. It was

recognised that these will be difficult to manage in a 24-hour service and would most

likely require increased staffing levels, changes to employment type (e.g. a larger

number of permanent part time AOP) and shared rosters that allow a person to move

between types of rosters and workload to suit an individual’s personal needs. Rather

than a blanket method of improving rostering through organisational change it may be

more appropriate to include a step-wise approach, which includes education of workers

to recognise the aspect of their work that may be affecting their health, which in turn

can lead to AOP involvement in rostering (Skinner & Chapman, 2013). Creating some

work locations that are built on flexible rostering, rather than all locations working the

equivalent system, may assist in providing options for shift working personnel.

Including formal rest breaks and restricting end of shift overtime and offering ‘on time

finishes’ may also be considered.

5.3.3 Worksite Wellness Programs

Wellness programs are increasingly being used by organisations to improve the

health of employees, reduce unplanned absenteeism and improve productivity even

though the evidence is limited as to their economic effectiveness (Lerner, Rodday,

Cohen, & Rogers, 2013). This section of the AHS 2015 was categorised into three

elements: respondents who be involved in health programs if there were incentives,

those who would use wellness facilities if available and the time during a day when

health programs would most likely be completed. The evidence for workplace wellness

291


programs was not clear. The lack of ‘down time’ in ambulance operations in urban

areas and the issues of fatigue and shift work introduce additional elements that

complicate any type of shift. AOP, especially in an urban environment, have limited

time to exercise at work. The type of work varies and can provoke stressors that are

different to those for whom the volume and intensity was high and consistent.

Interventions in the workplace to improve health need to be targeted at individuals

after having measured where their health lies, rather than at an organisational level

(Scott et al., 2003; Blake & Lloyd, 2008). Subsequently, the measurement of AOP’s

health was critical, as it had been shown that health risk appraisals (Chih-Wen et al.,

2009) can improve the health of workers. This was a significant element, as there is a

need to appreciate the health of all AOP may change over time as will work ability and

to be able to develop concepts of health improvement in this industry based on


Occupational risk or work disability for paramedics is known to be higher than

in the general population (Maguire, B.J. et al., 2014; Reichard et al., 2017). Combined

with the results of the AHS 2015 that show the health of AOP was related to the

working and organisational environment; a more comprehensive approach to risk

reduction and health improvement could be considered. Using the work ability index

may be one such approach that considers how long AOP are able to work and to what

extent this depends on work content and job demands (Ilmarinen, 2009). The work

ability index is a validated instrument (Ilmarinen, 2007) to assess the work ability of

workers. The WAI questionnaire covers the following dimensions of individuals:

Their current work ability compared with their lifetime best.

Their work ability in relation to the demands of the job.

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Discussion

The number of diagnosed illnesses or limiting conditions from which they suffer.

Their estimated impairment owing to diseases/illnesses or limiting conditions.

The amount of sick leave they have taken during the last year.

Their own prognosis of their work ability in 2 years’ time.

The work ability index has never been tested in an ambulance service and it is

one method that should be considered in determining evidence-based concepts to

prevent work ability declining in AOP and maintaining wellbeing as AOP age.

A number of studies have been conducted on improving wellness through

physical exercise and these may have some applicability to ambulance services. Po-

Huang Chyou (2006) conducted a 20-week walking program with female employees

of a large medical practice and found that there was a statistically significant increase

in the amount of exercise and a decrease in mean BMI. Such a program could be

adapted to the Australian context as it is a simple and common-sense intervention for

AOP, by encouraging AOP to live close to where they work, by incorporating

incidental exercise in their daily routine and by using treadmills in every station and

operations centre. A wellness intervention in ambulance from a North Dublin Service

identified 97% with unhealthy lifestyle behaviours, of which 74% indicated they had

made positive lifestyle changes in relation to the maladaptive life behaviours

previously pursued (Devaney & Noone, 2008). Such programs may have long lasting

impacts. In a five year follow up of a one-year general physical fitness program for

home care personnel, (Pohjonen & Ranta, 2001) reported the positive effect obtained

in the original program had continued with body fat decreasing, dynamic muscle

performance and maximal oxygen consumption increasing and the work ability in the

control group decreasing three times faster than in the intervention group. This is

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another program that has adaptability to ambulance as it consisted of two exercise

sessions per week over one year and could possibly be conducted pre or post-shift or

in leisure time and involve family. What these programs did not consider was how to

manage the issues that may be causative of poor health status from an organisational

and lifestyle perspective. It is no use having these programs in place if workers are just

too fatigued to participate.

Only one study was found that attempted to address the cardiovascular and

physical health problems prevalent in EMS in the U.S. (Oglesbee et al., 2015). This

program used a relatively simple exercise, health promotion and assessment program

and showed improvements in diastolic blood pressure, heart rate, cholesterol, weight,

body fat and the number of push ups and sit ups that could be performed. What this

program did not address was worker’s time constraints. This is important, as the AHS

2015 survey identified lack of time and energy to exercise as the two major barriers to

exercise. The cost to employers should also be considered, and this could be

determined through a social cost benefit analysis that assesses the cost to employers of

introducing wellness programs for employees and considers not only organisational

performance improvements, but cost in terms of the risk factors, development of

disease, presenteeism and absenteeism and the psychosocial aspects of poor health.

Ambulance employers should note that benefits to the organisation may not be

immediately available (Berry, Mirabito, & Baun, 2010).

To develop sound strategies, some aspects of the AHS 2015 results should be

considered. When asked about barriers to exercise, lack of energy was reported by

72.7% and lack of time by 84.8% of respondents. This provides some evidence that

policies and programs reducing working hours, shift lengths and ensuring regular rest

breaks should be a priority for an ambulance service. The majority of respondents

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Discussion

indicated they would be involved in wellness programs if there were incentives and

would use wellness facilities if they were available. The availability of wellness

facilities on site may encourage more physical activity at work, before, after work or

on rostered days off. These facilities could include exercise equipment, access to health

professionals, exercise physiologists, personal trainers, psychologists, dieticians, sleep

physicians and health assessments and through these professionals, the development

of individual health improvement programs. Another approach may include the access

to personal trainers and/or exercise physiologists in situations where health conditions

(e.g. obesity, chronic disease or mental health) are comorbid with AOP. An example

of such a program comes from the Australian Department of Veteran Affairs, who

fund individual programs with an exercise physiologist and/or a dietitian for veterans,

aimed at improving heart and mental health (DVA, 2018).

In summary, a health improvement strategy in an ambulance service ought to

contain fitness screening and medical evaluations tailored to individual risk. Fitness

programs could include physical activity, cardiorespiratory, muscle and flexibility

training. Behavioural modification could include tobacco smoking cessation,

hypertension, cholesterol, diabetes and obesity reduction and nutritional strategies.

Initial and ongoing education and screening must include health ergonomics, coping

with shift work, sleep improvement, fatigue reduction, stress management, injury

prevention, resilience training, safety culture improvement and leadership in health

improvement.

Criteria to evaluate the effectiveness of these programs will be needed. Whilst

there will be overarching strategies and policies, the programs should have an AOP

centred approach and ambulance organisations should commit, declare and

demonstrate that not only the safety, but the health of AOP is the highest priority. This

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is a critical element in developing strategies and policies in regards the health of AOP.

Considering all these aspects, a complex and interlocking model for improving health

related risk factors and safety is provided in Figure 5.4.

Figure 5-4. Improving Health Related Risk Factors and Safety

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Discussion

This model is a summary of this section and show the complexity and

interconnectedness of each element. That is, all elements need to be addressed in an

overarching plan. For instance, to look at just picking the right people, will solve no

issues if the safety culture doesn’t change, resilience training is not introduced and if

there is no evidence base for picking the right people. This framework allows people

to differentiate the perspectives and place them in the appropriate context, and to react

to the context in the proper way. In the absence of contextual differentiation, people

will do what is comfortable rather than what is appropriate.

5.4 A Conceptual Framework of Understanding

There is no such thing as an “average” operational person. Their roles differ as

does the operational and organisational context within which they operate. Thus, there

was unlikely to be a consistent picture of the “average” AOP, if such a concept was

useful or even relevant. There are five known studies (Boreham et al., 1994; Sterud et

al., 2006; Studnek, J. R., Bentley, et al., 2010; Pék et al., 2013; Betlehem et al., 2014)

that describe the health status of ambulance personnel and they discuss paramedics

only. There are no studies that describe the health status of EMDs or

supervisor/managers. In previous studies general health issues were less clearly linked

to the nature of the work or to the environment in which the work was undertaken (Soh

et al., 2016). Of interest, most respondents to a U.S. national longitudinal study of

health described their involvement in EMS as ‘not a primary career path’ and therefore

found it difficult to segment work related health issues from personal and other sources

of pathology. The major difference is that being employed by an Australian ambulance

service is a primary career path and associations with thoughts of leaving are related

to organisational symptomology and actually leaving is an economic or retirement

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decision (Bentley et al., 2016). Turnover rates are low and despite rumours to the

contrary, there is no evidence that graduate paramedics are leaving in higher number

than their non-graduate colleagues.

This research is the first that has developed a predictive model for back injuries

in AOP, which indicated that a mental health disorder, being a current shift worker,

family and personal stressors and job satisfaction influenced the risk of incurring a

back injury in ambulance service work. Importantly, the issues of employment type

(e.g. paramedic vs. an EMD), age, CVD, arthritis and respiratory conditions (AIHW,

2016a) were not part of the model, nor was gender (Aasa, Barnekow-Bergkvist, et al.,

2005) and self-reported health status (Studnek, J. R. & Crawford, 2007). This

conceptual model of understanding links health status, risk factors and organisational

symptomology to the chances of incurring a back injury and adds to the body of

evidence that organisational and psychosocial exposure is a diverse component of

incurring a back injury, more so than just physical exposure (Dropkin et al., 2015).

This research has shown that mental health disorders are a predictor of back injuries,

both of which have been shown to be significantly higher in the respondent population

than the Australian population. Age has been shown to be related to multiple

organisational symptoms, risk factors, disability and health status in the respondent

population. There are complex interactions between variables that are not as yet fully

understood.

The significance of these findings in relation to chronic disease should not be

underestimated. Whilst the causes of these high rates of chronic disease remain

unknown, the results show a group of workers who have substantially higher rates of

chronic disease than the Australian population. The results on chronic disease were a

disturbing trend and efforts to reduce chronic disease in AOP should be considered in

298

Discussion

the same context as mental health. If such an approach was taken and encompassed all

health considerations using a similar model to the ambulance service EAP (Scully,

2011) it is likely that there would be improvements in the health status of AOP. The

interconnectedness between mental health, chronic disease the work that AOP do, the

environment in which they work (especially paramedics) and the organisational

environment contribute to an increased rate of chronic disease. The models developed

in the regression analysis now allow prediction of chronic disease in this group of

workers. However, the latter three elements and the impact they have are fixed, and

further research is needed that links causation leading to a reduction in risk factors and

action to improve health at all levels of an ambulance service.

The issue of whether being an AOP is a young person’s role is going to be a

necessary component of future proofing this group of workers and if so, what role does

an ambulance service have in assisting these employees to develop other employment

opportunities in both an ambulance service and beyond. In addition and given that aged

workers in an ambulance service are known to be more susceptible to chronic disease,

fatigue, overweightness, poor exercise habits, all of which will lead into a less healthy

retirement, the question needs to be asked if there is a need for age-based recruitment

and continuing employment and fitness standards. However, some elements of the last

two comments are discriminatory and reducing and controlling hazards may be the

only short-term solution available. This approach is part of the current workplace

health and state system and cannot be considered unilaterally.

The regression modelling in the AHS 2015 did not find obesity to be related to

a mental health diagnosis or psychological distress but did find that overweight

respondents had an increased risk of sleeping less hours. Obesity is related to poor diet

and sedentary behaviour which were the result of environmental and psychosocial

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changes that were associated with lack of supportive policies in industry (WHO, 2016).

The AHS 2015 showed that body mass index for AOP was the same as the Australian

population and that waist-hip rates were 10% greater than the Australian population.

Waist-hip measures central adiposity, which is known as an enhanced measure of

cardio-metabolic risk (Ashwell, Gunn, & Gibson, 2012). This may be one of the

factors that have contributed to the high rates of CVD, three or more chronic diseases

and asthma in the respondent population. More studies are needed to determine

causation especially in relation to shift work, the working environment and

organisational commitment to the health of AOP which can lead to a heightened focus

on policies to improve health.

The model presented in Figure 5.5, unfolded as the research progressed and

presents the major factors that contribute to the health of the AOP over their working

lifetime. These actions should continue over a working lifetime and emphasise

individual worker health and ability. These elements are interrelated and taking a

positive step in one element should subsequently result in a chain reaction of improved

progression in an individual’s health. This model delays or negates negative

consequences, such as morbidity and possibly mortality, disability, performance and

absence and may lead to improved quality of life in retirement. Whilst this model may

have the appearance of a generic approach, it should be considered in the context of

working and organisational environment and the nature of the work in an ambulance

service, which was clearly outlined in Chapter Two. This conceptual model of

understanding has multiple components and was made even more complex with the

involvement of three employment types who experience distinctly different work

profiles and have multiple and different risks associated with their work. Its

complexity is significant, as demonstrated by the interconnectedness model developed

300

Discussion

for mental health disorders (see Figure 5.3). The complexity has been reduced by

grouping multiple variables into 13 elements and further classifying these into three

groups which are described in more detail below.

Group one includes social networks and psychosocial factors, demographics,

individual characteristics risk management and work-life balance, the work

environment and the nature of the job. Whilst it may be considered as discriminatory,

employers need to ask the question in regards picking the right people for a position.

However, this needs to be considered in terms of reducing the risk of an inherent task

requirement and creating organisational solutions. For example, knowing someone

was incompatible with shift work may impact on patients and safety. Group two

includes health monitoring and promotion, education and health activities. Group three

includes health friendly organisational and leader characteristics and a safety culture

for work-life.

A conceptual framework of health improvement has also been developed that

considers determinants, participation and policy proposals in more detail. This model

is described in Figure 5.6. There are only two structural determinants of gender and

occupation considered in this model, which was particularly important, as occupation

reflects standing, social networks, work stress, control and autonomy, work

environment, tasks and exposure (e.g. physical demands or sedentary behaviour). The

other structural determinants of health were not considered in this model as there was

an assumption that all respondents in similar employment groups have similar

determinants such as income, education, and social class. There were several next level

determinants that have been considered in developing a conceptual model for health

improvement such as the work environment, psychosocial factors (work, personal and

family stressors, social support, lifestyle and living circumstances), behavioural and

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biological factors, which include nutrition, physical activity, tobacco smoking and

alcohol and predisposition to some chronic diseases. These factors have been identified

as being negatively associated with AOP health status through the AHS 2015.

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Discussion

Figure 5-5. A Conceptual Model of Understanding the Health of AOP.

Health ‐mental and physical

(EMDs, Supervisors, Paramedics)

Psychosocial factors Work

environment & nature of the

job

Health promotion

Health friendly organisational

& leader characteristics

Demographics e.g. age,

gender, YIA, job type

Staff & peer support‐

psychological and physical

Health activities

Safety culture for work‐life

Education

Health monitoring

Social networks

Risk management &

work‐life balance

Individual characteristics

Education

Involving

Informing

Collaborating

Empowering

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Figure 5-6. A Conceptual Framework for Health Improvement of AOP

•Gender

•Occupation

Structural Determinants:

•Work environment

•Psychosocial factors

•work, personal, family & organisational stressors

•Living circumstances ‐ renting, morgatge, children, relationship, debt

•Biological ‐ genetic

•Behaviour ‐ smoking, alcohol, obesity, other risk factors

•Predisposition to disease

Next Level Determinants

•Informing ‐ problem, objective, altenatives, solution

•Consulting ‐ feedback

•Involving ‐ ensure concerns, aspirations, opinions are considered

•Collaborate ‐ partner with employment groups, funding, assessors and providers to develop alternatives and preferred solutions

•Empower ‐ individuals and groups ‐ they have ultimate control over decisions that affect their well‐being

Particpation and Empowernment of Employment Groups & individuals

•Reduce inequalities based on gender and occupation

•Job security, opportunities and alternatives based on age , chronic disease without making workers downwardly mobile

•Improved work environments with alternatives for gender, single parents, aged and workers with dependants

•Work overload to reduce disability

•Working hours, shift length, regular rest breaks, napping opportunities

•Health, safety and culture

•Health promotion, healthy lifestyles, obesity, sedentary behaviour, physical activity

•Health facilities, access, providers, health assessments, health programs and peer support

•Responsibility of inidviduals, groups, the organisation

•Funding

•Education ‐ determinants of health , risk, sleep , exercise, strength , flexibility , fatigue, mental and physical health, wellness

•Healthy Leader development

•Workforce reintegration post illness or injury and workers who are incapacitated including social and income protection and job alternatives

•Preemployment fitness and functional assessments

•Continuing fitness for purpose

•Assistance for those who care for dependants

Policies

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Discussion

Conclusion

The task of constructing the model that will guide policy will be difficult, as it

will be developed at an organisational level, however aimed at an individual worker.

The health status of the AOP has been identified as complex and will require a

multifaceted approach to an issue that is not fully understood. Several purposes for a

conceptual model of understanding have already been discussed in this research and

include the determinants of AOP health status and the inequalities between the three

employment types. In addition, the determinants and risk factors were shown how they

relate to each other and create inequalities in health status between employment

groups.

Integrating training facilities amongst emergency service providers may reduce

cost but maintaining ambulance employees as advisors and supporters to AOP is a

sensible approach. Funding, motivation, mandatory components, incentives, liability

and well-defined purposes will need careful attention as will areas with heavy

workload and more remote areas, hours worked, shift length, irregular rest breaks and

overtime to reduce time constraints for health improvement.

Increased collaboration between all emergency service organisations in regards

health and wellness challenges and initiatives will create opportunities for

improvement. Research collaboration with universities would also assist in providing

an evidence base for programs and enhancing the knowledge base (especially

concerning causation) in regards the health of the ambulance workforce.

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6 Conclusions

This is the first research that attempts to develop knowledge and understanding

of the health of AOP in Australia. This research subsequently demonstrated

associations between the various elements of the organisation of the work, the working

and organisational environment, the prevalence of risk factors and chronic disease,

predictive models and the demographics of AOP. The final section of chapter five

developed a conceptual model of understanding the issue, and a framework for

improving the health of AOP. This research is the most significant that has been

completed on the health of AOP in Australia and should underpin an increased

understanding and action to improve the health of this group of workers throughout

Australia, via direct action by ambulance services and AOP.

The research question sought to add to the body of knowledge on AOP health

status, risk factors and how that may be mitigated in the future. This research sought

to answer the following sub questions:

What are the characteristics of the physical and mental health of ambulance

operational personnel?

What are the individual and work-related factors that influence this health?

What strategies may lead to the improvement of health?

Study 1 was a systematic literature review associated with the health status of

AOP including the influence of organisational and lifestyle factors in achieving a

positive work life balance.

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Conclusion

Study 2 was a survey and data analysis on the health status of a sample of AOP

and a thematic analysis of the outcomes from semi-structured interviews. The

Ambulance Health Survey 2015 (AHS 2015) was the principal primary data source of

the research. The AHS 2015 results contributed to a systems analysis by utilising a

convenience sample of stakeholders from managers, supervisors, paramedics and

EMDs to identify and evaluate proposals for health and wellbeing interventions.

6.1 Implications for Policy Makers

This section is informed by a six point policy map (see figure 6.1) that has many

connections with that which has been described thus far. The most important elements

are explained below and form the basis for any ambulance organisation developing

policy in improving the health of AOP.

Working conditions

Fact based policy

Research reporting & feedback

Health programs

AOP health & wellbeing

Data collection & analysis

Health promotion

Shift workLong shiftsBreaks

Work demands

AuthorityMeasuresPrinciples

Strategies associated with improving healath

Physical and psychological resilience

PreventionSafety culture

FacilitiesExpert supportPeer support

Individual programs

CAAReaction & response

UniversitiesFunding

Workforce dataHealth data

Organisational data

Work based wellness programsWork ability

Aging

Job satisfactionSleepFatigue

Chronic diseaseInjuries

Figure 6-1. A six point policy map

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Psychological distress

This research found that paramedics have a higher injury rate than average and

were susceptible to psychological distress associated with the demands of the

organisation of the work, organisational and environmental issues. Whilst this is

contradictory, the rate of mental illness and psychological distress was statistically

significantly lower than the Australian population and that reported in the literature.

Furthermore, good quality psychological support programs that included a peer

component, education and prevention strategies and promote good mental health and

post traumatic growth, may be part of the solution in reducing mental health issues and

psychological distress. However, even with these programs in place, 78.4% of

respondents to the AHS 2015 reported experiencing depression ‘most of the time’ ‘to

all of the time’ in the four weeks leading up to the survey. This may or may not

represent a chronic depressive issue or it could be an acute episode based on an event

in that individual’s personal life or work in that four weeks.

Injuries and Occupational Health and Safety

In some countries, transport related incidents in comparative populations

accounted for most of the injuries whilst in Australia most injuries were related to

lifting, pulling or carrying. Poor sleep, fatigue and obesity have been implicated in this

injury rate (Sofianopoulos, Williams, & Archer, 2012). Injury rates have been shown

to be high and related to obesity, psychological distress and mental health disorders.

This suggests that a reactive approach to OHS in an ambulance service has limited

effectiveness in improving worker health and consideration should be given a number

of elements that have the potential to improve health and subsequently reduce injuries.

These should include an improved safety culture approach, safety champions, regular

health checks which include consultation with a psychologist, dietitian, exercise

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Conclusion

physiologist and an occupational physician. As incidents and hazards occur, then not

only does the injured AOP have case management, but an approach that considers how

the risks joined to form an incident that led to an injury and what can be done to

overcome these issues. Generalities should no longer be allowed, and investigations

have to provide specific actions that can be implemented as preventative measures or

controls whilst maintaining the privacy of the individual.

There should be a thorough review of safety and lifting equipment that can be

used in ambulance services and they should invest in finding and developing

engineering solutions to reduce injuries. It is up to policy makers, regulators and

technical designers to find solutions. Injured AOP should be brought back to the

workplace in worthwhile employment related to the employment type, as early as

possible with no loss of wages. Those that are no longer capable of working as an

AOP, must be comprehensively supported to find suitable employment. This will

require policy and structural changes to government level responses that apply to

whole of government organisations, recognising the significant factors that impact

upon the OHS of AOP. For instance, The Queensland Work and Safety Act and

Regulation 2011, Codes of practice which include fitness for duty requirements for

individual that are related to the components of the work that this research has

identified (WorkCover Queensland, 2019).

The ambulance service already promotes the health and safety of AOP.

However, the model is based on a reactionary approach of improving safety culture

and case managing people who were injured at work. There is now evidence of an

association between the physical health of AOP, job satisfaction, work environment,

sleep, fatigue, psychological distress, family stressors and ambulance specific stressors

such as leader support and work-related health culture. Furthermore, the literature

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raises evidence of a link between an AOP’s health and injuries at work. The workforce

is rapidly casualising and moving towards a gender balance, with the literature

reporting increasing injuries in ambulance work related to female workers. In addition,

there was a link between improving physical health and mental health, obesity and

work performance, including CPR and absenteeism. The challenge for organisations

is creating policy of pronounced sagacity, raising awareness of these links from an

organisational and individual worker sense and finding solutions that meet the needs

of both parties and OHS regulators.

Stress

It is known from other studies that low self-reported health is associated with

higher rates of mental health issues, low physical activity rates and obesity (Scott, Lim,

Al-Hamzawi, & et al., 2016). Psychological distress has been related to ambulance

work, such as acute stressors (e.g. dead children) and whilst it is recognised that some

AOP will be affected, this stress is more often being related to organisational (e.g. loss

of control and poor leadership) and environmental factors (e.g. the social aspects of

work) in the ambulance industry (Rybojad, Aftyka, Baran, & Rzońca, 2016; Asbury

et al., 2018). Stress is known to have a relationship with some mental and physical risk

factors such as diet and obesity and whilst it had been suggested that resilience of the

individual is related to stress, that resilience can be affected by work engagement such

as lack of communication and long shifts which can be exacerbated by fatigue, anxiety

and poor sleep. Job satisfaction, intent to leave and absenteeism were known to be

affected by work engagement, organisational and ambulance specific stressors. It is

known that sleep hours had decreased, fatigue and anxiety had increased and were

impacted by multiple factors such as sedentary behaviour, alcohol, age and disability,

job satisfaction, work -related health culture and where thoughts of leaving are high.

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Conclusion

These have been shown to be associated with the organisation of the work. This means

that policy makers are going have to either decrease the lengths of shifts, which will

be an unpopular choice, or have shifts finish on time unless there is a critical

emergency. Smaller length shifts could be developed that ensure coverage at the end

of longer shifts and in rest break times. This is likely to be difficult, complex and

industrially charged.

Aging workers

Developing policies based on age is discriminatory, therefore there will need to

more effort placed on aged workers maintaining fitness for duty, reducing obesity,

improving diet and alternative rostering arrangements that are known to be less

fatiguing. The difficulty with rostering however, is that different rosters incur different

rates of pay and until a composite rate is developed and implemented, that allows AOP

to move between rosters without financial loss, it will be a complicated issue to

resolve.

Fatigue

Although lifestyle choices have been linked to obesity, diet and physical

exercise, it was not known how this relates to ambulance work and whether this work

was a causative agent of lifestyle choices (Rosenkranz et al., 2013). Fatigue had been

described as high and implicated in long shifts and irregular rest breaks and has been

associated with musculoskeletal injuries in paramedics (Sluiter et al., 2003; Tucker,

2003; Patterson, Weaver, Hostler, et al., 2012). This research associated fatigue with

job satisfaction, alcohol consumption, age and having a disability. Occupational

violence is known to be increasing and was described as being higher for female

paramedics and has been implicated in job satisfaction (Maguire, Brian J et al., 2018).

Solutions must be sought for fatigue and these should include improving job

311


satisfaction and reducing disabilities and back injuries. Fatigue must be addressed as

not only is it associated with increased rates of injuries in AOP (Patterson, P. Daniel,

Weaver, Hostler, et al., 2012) but sleepiness (Patterson, P. D., Weaver, Frank, et al.,

2012; Courtney, J. A. et al., 2013) and patient mortality (Brachet, David, & Duseja,

2010). Fatigue is one of the major hazards that can be modified by a detailed review

of the organisation of the work.

Shift work

Shift work has physiological, psychological and social effects because of the

disruption to normal sleep-wake cycle (Sofianopoulos et al., 2012; Aeschbach, 2013).

In addition, shift work was known to affect social and quality of life and that these

shift workers were more likely to have extra accidents and sickness absence

(Schernhammer, 2016). Continuing this theme, shift work in AOP in Australia has

been associated with increased risk of fatigue, depression and poor-quality sleep

excessive sleepiness, nodding off whilst driving and poor sleep quality (Archer, 2012).

This research showed that shift work increased your chances of cancer, poor sleep, and

a back injury and reduced rates of exercise. The risk of delayed sleep onset and

maintenance issues was more prevalent amongst those AOP who worked longer shifts.

Finally, shift work disorder (a medically diagnosed condition) has been described as

incompatible with shift work and may influence 10 to 31% of AOP (Wright et al.,

2013). More effort will need to be placed on reducing the hazards associated with shift

work and shift work disorder. Although it could be considered as discriminatory,

selecting those who are more compatible with shift work and incompatible with shift

working disorder should be considered. This is really no different from psychometric

testing of applicants who are selected based on their results that indicate they are more

likely to grow from their experiences as an AOP. Initial and continuing education will

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Conclusion

need to be considered for this issue together with access to appropriately qualified

clinicians (e.g. sleep physicians) for evaluation and assistance. Different rosters could

be designed and are known to be in place in the ambulance service, but the issue of

financial loss will limit their effectiveness and the number of people who want to work

these rostering arrangements. Policy makers will have to find solutions to these issues.

Workplace based wellness programs

There was some evidence in other industries that workplace-based health

improvement strategies may be effective, especially for improving physical activity.

There were significant gaps in the literature, especially in Australia, on demographics,

health status, health risks, and the work-related health culture of AOP and ambulance

services and how these elements interact and influence each and the consequences.

Whilst most of the literature focuses on paramedics, there was little concerning EMDs

and no literature on supervisor/managers apart from that which describes the effect of

inadequate leadership on workers.

Job satisfaction

Of concern was the relationship of AOP to organisational symptoms such as job

satisfaction, work related health culture, thoughts of leaving and rest breaks. This

research has shown some common predictors of these four such as age, employment

type, fatigue, self-reported health and psychological distress. Although it was

recognised that these are complex problems which require evidence-based solutions,

this research provides some clues for a partial resolution. For instance, job satisfaction

has been related to fatigue and in turn to not getting formal rest breaks. It is intuitive,

that should all AOP get regular rest breaks that job satisfaction increases, fatigue

decreases and the known health impacts on AOP reduce. Job satisfaction has been

shown to be at the core of many of these issues in this research and policy makers will

313


have to find solutions that improve job satisfaction. A starting point is the predictors

of job satisfaction such as reducing anxiety, fatigue and poor health.

Supervisor/managers will need particular attention as poor job satisfaction in this

group has the potential to affect other AOP. Reducing disability and back injuries has

already been discussed in terms of OHS, however improving the work-related health

culture and decreasing psychological distress will be necessary for reducing anxiety.

Ensuring AOP get regular rest breaks and that shift times are not extended will be

critical in reducing fatigue, anxiety and subsequently barriers to exercise. Age has

already been discussed, but workload will need to be considered in terms of caseload

per week, month or year as this has been implicated in anxiety, fatigue and

performance. Therefore, workload policies that distribute work evenly amongst AOP

should be considered.

Work demands

Predicting the work demands that make individuals more susceptible to

musculoskeletal injuries and mental health issues was unclear. It was found that the

rate of back injuries in paramedics was twice that of the Australian population and

subsequent regression analysis found the predictors of back injuries included being a

current shift worker, a mental health disorder, family stressors, personal problems and

job satisfaction. These risk factors could be addressed. Twice as many males as

females had back problems, nonetheless when looked at as a proportion of each

gender; males had a slightly increased risk of having a back problem. Increasing back

problems were also associated with decreasing job satisfaction. As the workforce is

approaching a gender balance, consideration should be given to how an increase in

musculoskeletal injuries can be reduced, which is likely to increase as the proportion

of females increases. OHS issues in regards lifting equipment have already been

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Conclusion

discussed. There needs to be more emphasis on selecting the right people from a

psychological and physiological perspective without being discriminatory. Policy

makers will need to consider programs for the three levels of AOP that assist in

maintaining fitness for duty. These issues need to be considered at the start of a career.

This is an issue for policy makers at university level and it is suggested that the same

approach that is used to select candidates for medical undergraduate degrees, be

utilised in selecting candidates for paramedicine undergraduate degrees. For instance,

a first aid certificate is a prerequisite for entering the paramedic degree at Australian

Catholic University. Other necessary prerequisites, for instance mental health first aid,

could be similarly introduced.

Chronic disease

Of immediate concern was the rate of chronic disease and its corresponding

relationship with several factors such as BMI, age, sleep and psychological distress.

Higher rates of chronic diseases were reported by respondents to this research when

compared to the Australian population. Of significant concern was asthma which was

primarily in the younger age group, CVD which was higher in all age groups and

overall and the proportion of respondents with three or more chronic diseases which

was 2.5 times that of the Australian population. Males reported more chronic disease

than females and those who had three or more chronic diseases were more likely to be

older or work in rural ambulance stations. Mental health was found to be lower than

that of the Australian population and mental health disorders were more likely as years

in an ambulance service increased. It is known that the psychological support services

in the ambulance service were being consulted increasingly by younger AOP and

supervisor managers which suggest this system is working. Or, it could be that younger

AOP have been encouraged to share their feelings at an earlier age. However, it was

315


not known if the current psychological support approach of the ambulance service will

translate into reduced mental health issues in AOP later in life.

This is an important point, as if the current EAP works well, then a similar

program that introduces the other aspects of health discussed in this research, may also

work to improve the health of AOP. The complexity of the mental health issue in an

ambulance service is not understood. This research showed multiple links (see Figure

5.3) which included chronic disease, back injuries, psychological distress, anxiety,

organisational symptoms, workplace, employment type, health culture, caring for self,

family and personal stressors and fatigue. It is now incumbent on ambulance services

to expand mental health programs to include all aspects of these predictors to ensure

mental health issues are addressed in their totality. For example, one of the predictors

of mental health issues is anxiety, however reducing anxiety cannot be accomplished

unless its’ predictors are addressed. The complexity increases the further this chain of

predictors is followed. Ambulance services will need to improve employee assistance

programs to include multiple elements if they are to definitively address the mental

health issues of AOP. Depression may be much higher than formerly recognised and

related to a greater number of variables than previously thought.

Work ability

Ambulance services have much to consider, for instance, as a person’s work

ability changes throughout life and they are transitioned into other job types, this

should not result in loss of self-worth, economic loss or psychosocial support. In

addition, those who apply for a promotion and were found suitable but have individual

characteristics that were known to impose upon the individual a greater risk of poor

health status should be considered by policy makers. Health monitoring is a critical

component of health improvement and consideration needs to be given to how it can

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Conclusion

be matched against work ability and job requirements without invading the privacy of

this information. What are the implications for policy makers if a worker’s health

status does not improve? Who then is responsible and to what extent and if there were

increased risks associated with that decreasing health status, should the worker stay in

that position? There is a complex range of legislative, regulatory, policy and

discriminatory issues associated with these issues that need to be considered in a

considerate and sensible way. For instance, should someone who was overweight,

doesn’t exercise, has a poor diet and does not demonstrate a healthy role model be

selected as a supervisor/manager and to what extent should these issues be considered

in a selection process, if at all? This relates to fitness for inherent task requirements

and is a complex issue which needs careful consideration and would require non-

discriminatory performance measures, an evidence base and not be retrospective.

However, performance testing for fitness for duty is more often performed with

already employed individuals (Allen, Stein, & Miller, 1990) e.g. breath testing AOP

before they commerce duty. However, an approach of this nature will need to be tested

clinically as promoted AOP often work alone and are sent as a first responder to

critically ill and injured people.

Strategies for improving health

It was shown by this research that physical fitness, sleep, fatigue, alcohol,

tobacco smoking, work-related health culture, job satisfaction, supervisor support, the

nature of the work and intent to leave were associated with the health of the AOP.

Knowing these gives policy makers an opportunity to consider options to reduce

chronic disease with AOP. The argument put forward in the semi-structured

interviews, that an ambulance service is not responsible for the lifestyle choices of

AOP, no longer has validity, as chronic disease in AOP has been associated with the

317


organisation of the work, the working and organisational environment in an ambulance

service. The health department in the state where this research was conducted has

multiple programs available to promote public and health worker wellbeing. These

programs include cancer screening, diet and nutrition, fitness and exercise, alcohol,

tobacco smoking and drugs, mental health, men and woman’s mental health, sexual

health, environmental, sun and food safety, community programs and workplace

health and safety. These programs are available for specific groups within the

community as well as AOP. Whilst they have been promoted within the ambulance

service, they are not at the forefront of an AOP’s mind in a busy working environment

that includes shift work. A peer approach to increasing health promotion visibility

should be considered by ambulance services. There is evidence from this research that

a peer approach to reducing mental health disorders and psychological distress is a key

component of any organisations in reducing and controlling work related risk, which

may be applicable to other AOP support programs.

To promote health related quality of life and the management and reduction of

chronic diseases in AOP, mass media campaigns could be used to deliver information

about healthy eating, levels of physical exercise, coping with shift work, sleep and

fatigue and how these programs can be incorporated into the life of individuals. Health

promotion is a well-established discipline and is at the core of improving the health

status of AOP. However, it should be adapted to different employment types and the

risks associated with that employment, which is another complexity for policy makers

to consider. These programs need to be continuous over the life of an AOP. As an

example, and whilst people may choose where they live, encouraging workers to live

closer to their workplace and encouraging ambulance services to place workers close

to their place of residence can then assist in promoting a program that encourages

318

Conclusion

people to walk or cycle to and from work. This introduces complex issues, for instance,

the large proportion of graduate paramedics that live in urban areas yet must travel to

outlying areas to work.

A high demand and low control work environment has been implicated in

determining organisational stressors in ambulance service and other industries such as

nursing (Smulders & Nijhuis, 1999; Söderfeldt, Söderfeldt, Ohlson, Theorell, & Jones,

2000; Regehr, C. M., D, 2007). There may be little able to be achieved in terms of

changing this model in a highly technical, skilled and knowledgeable workforce such

as AOP, for reasons such as governance, patient outcomes, occupational violence and

harassment. However there does need to be consideration to finding ways of increasing

support for AOP, which may increase the sense that AOP skills and knowledge are

valued and their decisions and opinions are respected.

Travel to and from work adds a considerable burden on a worker in extending

an already lengthy shift that may have involved an extension of work hours. It can lead

to increased accidents and injuries at and away from work, adverse effects on health

and increased absenteeism (Knauth, 2007). This is particularly the case in ambulance

services where workers are exposed to occupational violence, high rates of injuries,

infectious diseases, distressing human suffering and patients affected by drugs, alcohol

and mental health disorders. AOP are often employed in areas where they are away

from family and friends and my work a long distance from their home. This research

has also reported higher rates of back injuries in those who work at busier stations.

This goes to the issue of fatigue and stress. Further research should be conducted, that

develops a body of evidence into the effects of these issues on AOP and policy makers

at an organisational and regulatory level need to consider changes that helps to modify

these effects.

319


Conclusion

The ambulance service involved in this research has been very successful at

reducing and containing mental health issues and psychological distress, promoting

post traumatic growth, resilience and good mental health in AOP, which was widely

recognised as related to ambulance work, culture and environment. A similar link has

now been shown to exist between physical health and ambulance work. The ambulance

service employee assistance program that includes access to psychologists, counsellors

and an extensive peer support system has been instrumental in providing psychological

support and reducing known adverse outcomes from exposure to ambulance work to

less than that of the Australian population. This approach has merit and with

modification or duplication to include health promotion, healthy eating and exercise,

may be successful in improving the physical health of AOP, which in turn may have a

further positive effect on the mental health of this population.

A review of health and wellness programs in Victorian Emergency Services (this

includes ambulance services) (Baum, 2011), found only two programs for the

ambulance service that were aimed at improving physical health and diet. Both were

health promotion activities. In the Ambulance Service where this research took place,

there is a multitude of health promotion and prevention programs available which may

not necessarily be known or accessed, despite information about these programs being

readily available and having been advertised. Bringing these programs to the attention

of AOP will require consistent education, promotion and peers who were willing to

assist in improving the holistic health of AOP. This section finishes with a succinct

description of the six point policy map described in Figure 6.1-1

1. Develop a constantly evolving evidence base for all aspects of health including

changes over time, assessment and preemployment data.

320

Conclusion

2. Improving research activities surrounding that health and using an evidence base

in determining policies and participation in improving organisational, work and

individual factors that improve health status, reduce risk factors, improve

performance and culture.

3. A policy approach that includes a whole of working life approach to health which

embraces position changes, work ability changes, culture, retirement, valid

instruments, measures and improving the health culture to reduce injuries,

disability and chronic disease.

4. Health promotion will require constant attention and peers may be an important

component in achieving this

5. Programs which include access to experts (dieticians, exercise physiologist, sleep

physicians), access to facilities and given the success of the Employee Assistance

Program in reducing mental health conditions & psychological distress, use this

same approach in developing a holistic wellness approach for AOP.

6. The collection of workforce, health and organisational data such as hours worked,

type of shift, location, estimated workload, factors influencing job satisfaction,

health culture and occupational violence that will help to identify any causative

factors that potentially impact on the health of the ambulance worker.

6.2 Strengths and Limitations

6.2.1 Strengths

This research has several strengths. First, the research was conducted in a

previously unstudied population. No research has been completed on the health of

AOP in any ambulance service in Australia that has such a broad focus. This research

321


has implicated the work of AOP, the organisation of the work and the working and

organisational environment with AOP health and provides further evidence that AOP

have increased risk factors and an increased prevalence of some health-related issues.

Second, the collection of data on the demographics, risk factors, self-reported

health and other factors related to ambulance work and AOP health is the most

comprehensive in Australia and has wide-ranging implications for ambulance services.

No research in Australia has gathered such a wide variety of data elements that impact

on overall health status of the three employment types. Additionally, the studies on

health status of AOP in Australia focus predominantly on mental health (Courtney, J.

A. et al., 2013; Roberts et al., 2015; Wild et al., 2018).

Third, whilst this data was collected in only one ambulance service, there were

many similarities with the clinical work, organisation of that work and the work and

organisational environment in ambulance services in other states of Australia,

especially those that have a very large population base. This research may have

implications for other ambulance services and may present an opportunity or

motivation for all Australian ambulance services to consider the implications of this

research for the health of AOP.

Fourth, whilst the survey tool comprised some elements of non-validated and

validated instruments, it was designed to make the questions relevant to AOP. This

survey instrument, whilst not validated, is the start of a process that may lead to a

validated survey tool or tools that will be accepted Australia wide as an appropriate

tool for assessing the health of AOP and how the work of this group affects their health.

Over time a validated survey tool will contribute to the development of knowledge of

causation of poor health in AOP. It is known that several ambulance services are

322

Conclusion

considering how they can assist in improving the health of the AOP and this research

provides further evidence of the increasing need.

Fifth, this research used a mixed methods approach which integrated quantitative

and qualitative research approaches. Using a sequential mixed methods design allowed

for a better understanding of the research question than either quantitative or

qualitative alone and provided more nuanced and contextualised research. In

interviewing the stakeholders, it showed a disparity between operational and non-

operational perceptions of the health status of AOP and the lack of evidence in policy

that can affect this health status.

Sixth, the respondent AOP is representative of the ambulance service AOP in

terms of age, gender and employment type.

Seventh, by using elements of the Australian Health Survey 2011-13, the

respondent data was able to be compared to the Australian population data.

6.2.2 Limitations

First, the cross-sectional design of the research does not allow the determination

of causality and the direction of associations cannot be determined. Care should be

exercised when looking at the nature of the associations between variables. For

instance, whilst psychological distress was shown to be less than the Australian

population, it was statistically significantly associated with job satisfaction. However,

the job satisfaction survey used in the AHS 2015 has never been previously validated

in any setting. Consequently, Cronbach’s Alpha was used to determine the

reliability of this component of the AHS 2015 which demonstrated strong

internal consistency. What was not known is whether health status affected

psychological distress or psychological distress affected health status. However, given

323


the constraint of time and resources, a cross-sectional design was very useful and

provides a level four evidence base (NHMRC, 2008). This survey could have been

exposed to question and questionnaire design bias, transcription bias, survivor bias,

cultural difference and respondent recall.

Second, this research used self-reported data, and from that perspective has

several limitations. For instance, the questions were predetermined and even though

they were tested for face validity, it forced respondents to answer what the researcher

wanted to find out rather than what they wanted to say, and respondents could ignore

a question completely. To reduce the size of an already large survey, limited

opportunity was given for respondents to add to their response. This can potentially

reduce the validity. However, using a mixed methods approach, provided opportunity

for stakeholders (albeit a limited number) to comment on the data.

Third, social desirability bias could have an impact in terms of under-reporting

variables that reflect poorly on the individual (e.g. alcohol) or over reporting those

variables that reflect on blameless behaviour (e.g. self-reported health). However, in

this survey no individual could be identified, and anonymity was assured. For example,

it was known that respondents may underreport alcohol consumption (Livingston &

Callinan, 2015). In addition, this survey may reflect the culture of the organisation in

that the AOP think of themselves as carers and that their health was invulnerable to

the vagaries of their work (Clompus & Albarran, 2016), which may be especially true

for the younger ambulance population. As such, this may reflect in a lowered response

rate.

Fourth, the AHS 2015 was large, with 154 questions and some questions having

multiple response parts. As such, it may lead to lower response rates. Some questions

may be misunderstood or may take more time to answer and require detailed thinking

324

Conclusion

about the response. As an example, the questions on exercise required respondents to

think about hours of exercise in three categories over the two weeks before the survey

took place. This may lower the reliability of the data.

Fifth, even though this questionnaire was pilot tested for face validity and

content and construct validity, the survey has not previously been used or validated in

another ambulance setting. This can potentially leave the research open to claims that

it does not measure up to its assertions.

Sixth, this research compared the health characteristics of AOP with that of the

Australian population and an older survey of these workers in regards sleep, fatigue

and anxiety (Parker, A. W. & Hubinger, 2003). In comparing the AHS 2015 results

with the Australian population it could potentially confound the results as there were

many differences between the average Australian (if there was even such a person) and

AOP, especially paramedics. For instance, the data from the AHS 2015 included

working paramedics, whilst the data for the Australian population included working

and non-working population. In making comparisons between current AOP and those

who were surveyed 15 year ago (sleep, fatigue and anxiety) (Parker, A. W. &

Hubinger, 2003), the results could be confounded as the organisation of the work and

the working environment have changed in the ambulance service, including a move

towards a systematic approach for EMDs, increased workload, a younger workforce,

increased responsibility of supervisors, increased requirements for clinical competence

and governance, a move to longer shifts, gender equality and casualisation of the

workforce and a university undergraduate degree in paramedicine as a prerequisite to

employment with an ambulance service.

Seventh, the survey data was self-reported and may differ from other surveys

that have different collection methods and that are conducted by ambulance

325


organisations in regards their employees. However, it is known that the ambulance

service in this research, has collected data from organisational surveys using the same

methodology as this research; that is, mail out questionnaires. Some of the AHS 2015

data may have quality issues, for instance alcohol, and exercise. Caution should be

taken when interpreting some results (e.g. only 223 respondents answered all questions

that allowed an accurate description of exercise hours). However, by using the same

elements from the Australian Health Survey, the AHS 2015 results provided useful

information (e.g. alcohol consumption) for a subset of the Australian population and

how actions change over time (e.g. alcohol consumption in the last 12 months).

Eighth, volunteer bias refers to a specific bias that can occur when respondents

who volunteered to provide their health data are different in some way from the general

population (Voide et al., 2012). The respondent population was shown to be

representative in terms of age, employment category and gender; however other

differences between respondents and population may exist. Similarly, population bias

should be considered and whilst these respondents were not selected they may have

volunteered their data because they know the researcher (FitzGerald & Hurst, 2017).

There was no way to control for this. The last bias to be considered is survivor bias.

This is where a less well AOP may reduce their exposure to work and unless health

status and exposure are measured over time it is not possible to measure (Austin &

Platt, 2010). This data collection was a point in time survey as part of a cross sectional

analysis.

6.3 Recommendations for Future Research

These recommendations focus on those associated with further research that may

be practically achievable and are based on the evidence presented in this study. The

researcher has no ability to influence recommendations for practice change within the

326

Conclusion

Ambulance Service, other than providing evidence based on the literature and the

results of this research and suggesting models that may assist in improving the health

of AOP.

6.3.1 Recommendation One

There is a need for valid and reliable survey instruments in EMS that assess

fatigue, sleep, organisational stressors, the nature of the work, health culture, health

and safety (which includes occupational violence), health status, risk factors, and

modifiable lifestyle factors.

Ten different EMS survey designs related to fatigue and four related to sleep

were identified. Altogether, 37 different studies have been published that observe the

reliability and validity of these instruments. A systematic review of these studies

reported there was limited evidence of the reliability of different surveys in Emergency

Medical Systems and that none of these studies reported on sensitivity and specificity

(Patterson et al., 2018).

Whilst there may be many valid instruments on these elements in the general

populations, it is suggested that they be modified to suit ambulance services by a group

of experts to provide a content validity assessment of the instruments. This could begin

with a group of experts from the Convention of Ambulance Authorities in Australia

and continue with other groups from Paramedics Australasia, The College of

Australian and New Zealand Paramedics and representatives of universities that

currently provide undergraduate paramedicine programs. This approach will increase

the possibility that these instruments will capture the construct better for the target

population.

327


6.3.2 Recommendation Two

Sedentary behaviour was reported to be high, poor sleep and fatigue are on the

rise in AOP, fruit and vegetable consumption was low and chronic diseases such as

cardiovascular and asthma are much higher than the Australian population. It is

recommended that further investigation of these factors be conducted so that lifestyle,

work and health status improvement can be initiated. Whilst ambulance specific

instruments are being developed, a longitudinal study could explore the causative

factors. An intervention trial could be conducted to test modifications to lifestyle,

workforce factors and to promote improved health status especially amongst AOP who

are working or have worked shift work.

6.3.3 Recommendation Three

Future longitudinal studies should include biomarker data not unlike that

collected in the Australian Health Survey. These should include biomarkers of CVD,

Type II diabetes, chronic kidney disease, liver function, nutrition status and other

factors such as serum cotinine to estimate the prevalence of active and passive

smoking. These studies should also include measurements such as blood pressure,

height, weight, hip and waist circumference, peak flow values, VO2 max, anaerobic

threshold, strength, flexibility and endurance. Whilst these measures should provide

important information on AOP, workforce data such as hours worked, type of shift,

location, estimated workload, factors influencing job satisfaction, work-related health

culture and occupational violence will help to identify any factors that potentially

impact on the health of AOP.

6.3.4 Recommendation Four

It is recommended that a feasibility study of how entry level medical data can be

obtained as a starting point in a longitudinal study, be investigated. A longitudinal

328

Conclusion

study of AOP health should include entry level medical data. As well as requiring

informed consent and ethics approval from multiple institutions, this will require

cooperation of ambulance services through the CAA. There is opportunity for a

university/s that are willing to create an Australian wide partnership with ambulance

services in applying for research funding and creating a centre for health excellence

for AOP.

6.3.5 Recommendation Five

The complex interactions between the models developed in this research

should be explored further using a technique such a Structural Equation Modelling

(SEM) to bring together the multiple models presented in this research to determine

which observed variables are reliable indicators of latent variables (e.g. the health

status of AOP). Binary and ordinal logistic regression analysis has created 25 models

from the AHS 2015 data. Separately, these models give an indication of issues that can

be addressed to potentially improve the health of ambulance operational personnel.

6.3.6 Summary

This research showed that AOP had worse self-reported health, lower rates of

psychological and mental health disorders, higher rates of disabilities, back injuries,

cancer, diabetes, CVD and asthma than the general Australian population. The number

of respondents who reported three or more chronic diseases was 2.5 times that of the

Australian population as was the rate of CVD, and asthma was three times that of the

Australian population. This is the first-time asthma has been reported to be associated

with AOP. Sedentary behaviour was high, and exercise, tobacco smoking and alcohol

consumption rates were low as were fruit and vegetable consumption. Central

adiposity rates were 10% higher than the Australian population. Job satisfaction was

poor, and thoughts of leaving were high.

329


Logistic regression analysis was used to determine predictors of dependent

variables such as health status, chronic disease, organisational symptomology, risk

factors and caring for self. Mental health was theorised as being related to more than

the work that AOP do as part of their employment. As a result, an interconnectedness

model was developed which showed that mental health was primarily influenced by

anxiety, psychological distress and asthma. Secondary influencers included job

satisfaction, systolic blood pressure, a disability, BMI, hay fever, poor self-reported

health and a cancer diagnosis, whilst third level influencers were age, gender (male),

employment type (supervisor/manager), shift work years, poor sleep, station category

(level 5), poor work-related health, thoughts of leaving, diabetes, CVD, alcohol

consumption, fatigue and family stressors. This is a critical finding as it has the

potential to change the way poor mental health of AOP is managed.

Cross tabulation of the AHS 2015 data demonstrated links between poor self-

reported health and workplace factors such as job satisfaction. There appeared to be a

complex link between sleep and fatigue, which was related to alcohol, age, disability,

quantity of shift work years, self-reported health, overweightness and job satisfaction.

The more sleep, the less likely respondents were to report negative associations with

the above variables. Working long hours (>181 hours per month) was associated with

increased fatigue which was in turn associated with poor job satisfaction, work-related

health culture and self-reported health, not getting regular rest breaks and greater levels

of disability. The literature supports a link between poor supervisor support and job

satisfaction. This research showed respondents with poor self-reported health, mental

health disorders and psychological distress were less likely to agree their supervisor

supports them and trust the leadership team. Critically, job satisfaction was linked to

330

Conclusion

fatigue, anxiety, employment type, family stressors, age, health status and the level of

high school completed.

These results and the outcome of the literature review were presented to nine

individuals from the ambulance service in semi-structured interviews and the

transcribed interviews were analysed using a thematic approach. These interview

results did not contribute to the development of health improvement strategies but did

show that: perceptions between operational and non-operational personnel were

different, the influencers on the health of AOP was little understood and the evidence

base for improving their health is deficient. A complex conceptual model for

understanding the health of AOP was developed followed by a framework that could

be used by an ambulance service for health improvement. The implications for

organisational and regulatory bodies are significant in the context that the

psychological, physiological and psychosocial health of AOP must be looked at as a

system, as this research has shown health issues, not just mental health, to be

significant and warranting immediate attention.

Five recommendations for future research were made which include future

longitudinal studies, the inclusion of biomarker and preemployment medical data in

future longitudinal studies, valid EMS health surveys and immediate attention to health

promotion activities. A peer support model was suggested to improve the advancement

of health promotion and the development of improved safety culture which has been

shown to be related to patient safety and leadership.

331

Understanding the Health of Operational Personnel in an Ambulance Service: A Mixed Methods Case Study

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8 Appendices

8.1 Appendix A: Ethics Approval, Recruitment

Information and Survey

Ethics application - approved - 1400000936

DELETE REPLY ALL FORWARD CONTINUE EDITING DISCARD Mark as unread

QUT Research Ethics Unit Wed 18/02/2015 10:57 AM Inbox To: Gerard Fitzgerald; Tony Parker; Michele Clark; Richard Galeano; ... Cc: Janette Lamb; ... Inbox

1 attachment

UHRECSTANDARDCONDITIONSOFAPPROVAL-HUMANRESEARCH.DOC 44 KB Dear Prof Gerard FitzGerald and Mr Richard Galeano Project Title: Evaluating the health and work-life balance of operational personnel in the Queensland Ambulance Service: Influences, associations and impacts Ethics Category: Human - Low Risk Approval Number: 1400000936 Approved Until: 18/02/2018 (subject to receipt of satisfactory progress reports) We are pleased to advise that your application has been reviewed and confirmed as meeting the requirements of the National Statement on Ethical Conduct in Human Research.

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I can therefore confirm that your application is APPROVED. If you require a formal approval certificate please advise via reply email. CONDITIONS OF APPROVAL Please ensure you and all other team members read through and understand all UHREC conditions of approval prior to commencing any data collection: > Standard: Please see attached or go to http://www.orei.qut.edu.au/human/stdconditions.jsp > Specific: Approval for Study 2 (QAS personnel survey) only Decisions related to low risk ethical review are subject to ratification at the next available UHREC meeting. You will only be contacted again in relation to this matter if UHREC raises any additional questions or concerns. Whilst the data collection of your project has received QUT ethical clearance, the decision to commence and authority to commence may be dependent on factors beyond the remit of the QUT ethics review process. For example, your research may need ethics clearance from other organisations or permissions from other organisations to access staff. Therefore the proposed data collection should not commence until you have satisfied these requirements. Please don't hesitate to contact us if you have any queries. We wish you all the best with your research. Kind regards Janette Lamb on behalf of Chair UHREC Office of Research Ethics & Integrity Level 4 | 88 Musk Avenue | Kelvin Grove p: +61 7 3138 5123 e: [email protected] w: http://www.orei.qut.edu.au

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University Human Research Ethics Committee (UHREC)

Standard Conditions of Approval – Human Research

All ethical approvals are granted subject to the following standard conditions

of approval. These are also available online at: http://www.orei.qut.edu.au/

General Conditions

The research team must:

1. Conduct the project in accordance with your UHREC approved protocol.

2. Conduct the project in accordance with QUT policy, NHMRC / AVCC guidelines and regulations, and the provisions of any relevant State / Territory or Commonwealth regulations or legislation.

3. Respond to the requests and instructions of the University Human Research Ethics Committee (UHREC).

4. Ensure all research participants are provided with the current Participant Information Sheet and Consent Form, unless otherwise approved by the Committee.

5. Report on the progress of the approved project at least annually, and at the completion of the project.

6. (Where the research is publicly or privately funded) publish the results of the project is such a way to permit scrutiny and contribute to public knowledge.

7. Ensure, wherever possible, that the results of the research are made available to the participants.

Concerns, Complaints, Adverse Events and Unexpected Outcomes

Follow Section 5.5.3 of the National Statement which states that: ‘Researchers have a significant responsibility in monitoring approved research as they are in the best position to observe any adverse events or unexpected outcomes. They should report such events or outcomes promptly to the relevant institution/s and ethical review body/ies and take prompt steps to deal with any unexpected risk’.

As such, the research team must, via the Concerns, Complaints and Adverse Events

form:

1. Immediately advise the Research Ethics Coordinator, if any complaints are made, or expressions of concern are raised, in relation to the project.

2. Suspend or modify the project if the risks to participants are found to be disproportionate to the benefits, and immediately advise the Research Ethics Coordinator of this action.

3. Stop any involvement of any participant if continuation of the research may be harmful to that person, and immediately advise the Research Ethics Coordinator of this action.

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4. Advise the Research Ethics Coordinator of any unforeseen development or events that might affect the continued ethical acceptability of the project.

Modifying your Ethical Clearance

The research team must:

1. Convey proposed changes to the Research Ethics Unit for appropriate review and approval, prior to implementation of any proposed change.

2. Submit requests for variations via the Variation Request Form; minor changes will be assessed on a case by case basis.

3. Note that major changes, depending upon the nature of the request, may require submission of a new application.

NOTE: The UHREC may apply additional specific conditions to your

approval. You will be notified of these in your approval email and certificate. You

must ensure all research team members also understand and comply with any specific

conditions of approval.

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Research Team: Mr Richard Galeano PhD student Professor Gerard FitzGerald Principal supervisor Professor Tony Parker Associate supervisor Professor Michele Clark Associate supervisor School of Public Health and Social Work, Faculty of Health, Queensland University of Technology (QUT)

8.1.1 Information Sheet for Participants

This research is being undertaken as part of a PhD at Queensland University of Technology (QUT) by Richard Galeano. QUT Ethics Approval Number 1400000936

The purpose is to develop a comprehensive picture of the health of operational personnel in the Queensland Ambulance Service (QAS), identify factors that influence their health, identify associations between work and lifestyle, and determine how work groups and ambulance organisations may impact positively on the overall health of operational personnel.

You have been invited to participate because you are an operational employee with Queensland Ambulance Service.

This Information Sheet will provide you with important information to assist you with your choice.

Participation Your participation in this research is entirely voluntary. If you do agree to participate, you can withdraw from the project at any time without comment or penalty. Your decision to participate or not participate will in no way impact upon your current or future relationship with QUT or QAS. Your participation will involve completing a survey instrument and a self‐measured body composition. The survey instrument will take 30 minutes of your time. The self‐measured body composition measures will take approximately 15 minutes of your time.

Support Whilst completing the survey, should you experience any distress in reliving your experiences or recognise that you have a health problem you are strongly advised to contact a health professional that may be able assist you with these issues. Support is available through QAS Priority One at 1800 805 980.

Ambulance Health

Survey

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Completing the survey and self‐measures is voluntary. You may choose whether or not to volunteer in this or any part of the AHS 2015. If you choose to participate you may withdraw at any time if you feel unable to continue.

Privacy and Confidentiality Comments and responses are non‐identifiable, governed by the Privacy Act 1988. Any data collected as part of this project is stored securely as per QUT’s management of research data policy.

What are the benefits of participating? Your answers to the survey questions and self‐measures may give you new insights into your health, or may be something you might like to discuss with your doctor. You will also be contributing information that will guide future healthcare policies, guidelines and public health programs that will be relevant to you and your family for years to come. Unidentified information from your responses will be combined with other respondents information to assess the health of operational employees in general – the more people participating, the more accurate this picture will be. This data will enable a wide range of research into the relationship between physical activity and health outcomes for everyone in the ambulance community. This information will be compared with the general population through the Australian Health Survey data and previous surveys on operational employee’s sleep and fatigue. I do not expect to be providing any comparisons and analyses of the combined data until late 2015. Data collections will be conducted in early 2015 with analysis starting mid 2015. Results and discussions will be freely available, published in scientific journals, presented at conferences and at organised station meetings throughout Queensland.

Self‐Measures I am asking you or someone close to you or a co‐worker to measure your height, weight, hip and waist circumference, systolic and diastolic blood pressure. These measures will take approximately 15 minutes to collect. These results can be recorded in the survey instrument where you will also find instructions on accurately recording the body composition data.

Survey The survey instrument is quite large but should only take approximately 30 minutes of your time to complete, and comprises four areas: 1. A health risk component.

2. Self‐measured body composition data.

3. A Personal Interest Component.

4. A Health Culture Audit.

You can access, complete and submit the online version of the survey via QUT’s Key Survey. You have been emailed via Key Survey which has generated an individual URL for each respondent (preferred response method).

OR

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Appendices

Electronically (it has also been emailed to you via the QAS email system) and to maintain de‐identification, if you choose to email your return please do so to: cedm‐[email protected].

OR

Manually – you can print the survey instrument, complete and return via post to:

Ambulance Health Survey 2015 Centre for Emergency and Disaster Management QUT Kelvin Grove Victoria Park Road Kelvin Grove Q 4059

Returns that are made online are unidentifiable and those returns made by email or post will be separated from the original email/post by an independent person at QUT’s Centre for Emergency and Disaster Management (CEDM), who is not connected to the research, and forwarded on to the researcher. In this way, all respondents remain unidentifiable. YOU SHOULD CONSIDER COMPLETING YOUR SELF‐MEASURES, AND IF A PARAMEDIC PLEASE

GATHER YOUR STATION WORKLOAD FROM YOUR OFFICER IN CHARGE (OIC) BEFORE ATTEMPTING TO COMPLETE THIS SURVEY.

What will happen to my survey response and recorded measures? After the data has been entered into a database and checked for accuracy, any paper results will be stored in a locked file cabinet at QUT for 15 years. Electronic responses will be recorded on a password protected laptop and on a secure QUT server (also password protected). This data will only be accessible by the researcher and a nominated person from QUT, at this stage, the Principal Supervisor.

You may withdraw your consent to participate at any time.

THIS SURVEY IS NOT INTENDED FOR THE PURPOSE OF DIAGNOSING OR TREATING ANY HEALTH PROBLEMS YOU MAY HAVE.

PARTICIPATION IN THIS SURVEY DOES NOT TAKE THE PLACE OF VISITS TO A DOCTOR OR OTHER HEALTH PROFESSIONALS.

Questions / further information about the project If have any questions or require any further information about the project please contact:

Mr Richard Galeano [email protected] 0437 729 735 Professor Gerard FitzGerald [email protected] 0731 383 935 Professor Tony Parker [email protected] 0731 386 173 Professor Michele Clark [email protected] 0731 383 519

Concerns or complaints regarding the conduct of this research The QAS and QUT are committed to research integrity and the ethical conduct of research projects. However, if you have any concerns or complaints about the ethical conduct of this research you may contact the QUT Human Research Ethics Unit on 0731 385 123 or email [email protected] . The QUT Human Research Ethics Unit is not connected with the research project and can facilitate a resolution to your concerns in an impartial manner.

Thank you for helping with this research project and please keep this sheet for your information.

375


Subject Title: Evaluating the Health and Work‐Life Balance Of Operational Personnel in The Queensland Ambulance Service: Influences, Associations and Impacts Dear Colleague I am writing to seek your assistance in a program to determine, promote and improve the health status of operational personnel in the Queensland Ambulance Service (QAS). The purpose is to provide a comprehensive picture of the health status and general wellbeing of operational personnel. I am conducting this research at Queensland University of Technology (QUT) as part of my PhD studies and the study has been approved by QUT Human Research Ethics Committee (Approval Number 1400000936). You have been invited to participate as you are an operational person working for the QAS. You have the opportunity in this study to do something that is original and will provide long lasting benefits to operational personnel. However, I do need your assistance by asking you to complete the survey and self‐measures. You can access, complete and submit the online version of the survey via QUT’s Key Survey. You have been emailed via Key Survey which has generated an individual URL for each respondent (preferred response method); or Electronically (it has also been emailed to you via the QAS email system) and to maintain de‐identification, if you choose to email your return please do so to: cedm‐[email protected]; or Manually – you can print the survey instrument, complete and return via post to:

Ambulance Health Survey 2015 Centre for Emergency and Disaster Management (CEDM) QUT Kelvin Grove KELVIN GROVE QLD 4059

Returns that are made online are unidentifiable and those returns made by email or post will be separated from the original email/post by an independent person at CEDM who is not connected to the research and forwarded on to me as the researcher. In this way, all respondents remain unidentifiable.

The information that you need to make an informed decision about your participation in this study is

contained in the attached Participant Information sheet.

Please feel free to ring or email me if you have any questions.

Yours sincerely

Richard Galeano

PhD Student

0437 729 735

[email protected]

Professor Gerard FitzGerald Principal Supervisor 07 3138 3935 [email protected] School of Public Health and Social Work, Faculty of Health Queensland University of Technology

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Appendices

Ambulance Health Survey 2015 Centre for Emergency and Disaster Management (CEDM) QUT Kelvin Grove KELVIN GROVE QLD 4059

EVALUATING THE HEALTH AND WORK‐LIFE BALANCE OF OPERATIONAL PERSONNEL IN THE

QUEENSLAND AMBULANCE SERVICE: INFLUENCES, ASSOCIATIONS AND IMPACTS

SEEKING YOUR ASSISTANCE Dear Colleague I am writing to seek your assistance in a program to determine, promote and improve the health status of operational personnel in the Queensland Ambulance Service (QAS). The purpose is to provide a comprehensive picture of the health status and general wellbeing of operational personnel. I am conducting this research at Queensland University of Technology (QUT) as part of my PhD studies and the study has been approved by QUT Human Research Ethics Committee (Approval Number 1400000936). You have been invited to participate as you are an operational person working for the QAS. You have the opportunity in this study to do something that is original and will provide long lasting benefits to operational personnel. However, I do need your assistance by asking you to complete the survey and self‐measures. You can access, complete and submit the online version of the survey via QUTs Key Survey. You have been emailed via Key Survey which has generated an individual URL for each respondent (preferred response method); or Electronically (it has also been emailed to you via the QAS email system) and to maintain de‐identification, if you choose to email your return please do so to: cedm‐[email protected]; or Manually – you can print the survey instrument, complete and return via post to:

Ambulance Health Survey 2015 Centre for Emergency and Disaster Management (CEDM) QUT Kelvin Grove Kelvin Grove Q 4059

Returns that are made online are unidentifiable and those returns made by email or post will be separated from the original email/post by an independent person at CEDM who is not connected to the research and forwarded on to me as the researcher. In this way, all respondents remain unidentifiable. The information that you need to make an informed decision about your participation in this study is contained in the attached Participant Information sheet. Please feel free to ring or email me if you have any questions.

Yours sincerely

Richard Galeano PhD Student 0437 729 735 [email protected]


Reminder

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Dear Colleague, In late May, I emailed and posted to you information on a study that I am conducting about the health status of operational personnel in the QAS. This is paramedic‐led research, supported by QAS, that I am undertaking for my doctoral studies. I wanted to provide an update and once again seek your assistance to help me undertake what I know to be the first comprehensive health review of Paramedics and Emergency Medical Dispatchers worldwide. Completion of the Ambulance Health Survey 2015 (AHS 2015) is central to my data collection ‐ I have re‐attached a copy of the survey with this email for your reference. Over 4000 operational personnel received the survey; so far the responses obtained equate to a 5.6% return rate, far short of the required response rate of approximately 30%. If you are yet to participate, you can complete and return the survey in one of three ways:

1. If you prefer to complete the survey on MS Word, open the attached document, complete the survey, save and email to cedm‐[email protected]

2. Alternatively you can print the survey, complete it by hand and post it to: Ambulance Health Survey 2015 Centre for Emergency and Disaster Management QUT Kelvin Grove Victoria Park Road Kelvin Grove Q 4059

3. For those that have not completed the web‐based version through KeySurvey, I will shortly re‐send the individual links so that you can access and submit the survey online. (If you have already submitted a response via email or post, please do not complete a second copy via KeySurvey) For those of you who have completed the AHS 2015, a BIG thank you! Can I please ask for your further support, by becoming a champion for the AHS 2015, and encouraging and helping your colleagues to respond? Your support and participation is greatly appreciated. Kind regards,

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Appendices

8.1.2 Ambulance Health Survey (AHS) 2015

This research is being undertaken as part of a PhD at Queensland University of Technology (QUT) by Richard Galeano. The purpose is to develop a comprehensive picture of the health of operational personnel in the Queensland Ambulance Service (QAS), identify factors that influence their health, identify associations between work and lifestyle, and determine how work groups and ambulance organisations may impact positively on the overall health of operational personnel. You have been invited to participate because you are an operational employee with QAS.

Please read the following which provides you with important information to assist you

with your choice to participate or not.

Please note this research has been approved by QUT Human Research Ethics Committee, approval number 1400000936.

Participation

Your participation in this research is entirely voluntary. If you do agree to participate, you can withdraw from the project at any time without comment or penalty. Your decision to participate or not participate will in no way impact upon your current or future relationship with QUT or the QAS. Your participation will involve completing a survey instrument and a self‐measured body composition. The survey instrument will take 30 minutes of your time. The self‐measured body composition measures will take approximately 15 minutes of your time.

Support

Whilst completing the survey, should you experience any distress in reliving your experiences or recognise that you have a health problem you are strongly advised to contact a health professional that may be able assist you with these issues. Support is available through QAS Priority One at 1800 805 980.

Completing the survey and self-measures is voluntary.

You may choose whether or not to volunteer in this or any part of the AHS 2015. If you choose to participate you may withdraw at any time if you feel unable to continue.

Confidentiality Comments and responses are non‐identifiable, governed by the Privacy Act 1988. Any data collected as part of this project is stored securely as per QUT’s management of research data policy.

What are the benefits of participating?

379


Your answers to the survey questions and self‐measures may give you new insights into your health, or may identify issues you may like to discuss with your doctor. You will also be contributing information that will guide future healthcare policies, guidelines and public health programs that will be relevant to you and your family for years to come. Unidentified information from your responses will be combined with other respondent’s information to assess the health of operational employees in general – the more people participating, the more accurate this picture will be. This data will enable a wide range of research into the relationship between physical activity and health outcomes for everyone in the ambulance community. This information will be compared with the general population through the Australian Health Survey data and previous surveys on operational employee’s sleep and fatigue. I do not expect to be providing any comparisons and analyses of the combined data until late 2015. Data collections will be conducted in early 2015 with analysis starting mid‐2015. Results and discussions will be freely available, published in scientific journals, presented at conferences and at organised station meetings throughout Queensland.

Self-Measures

I am asking you or someone close to you or a co‐worker to measure your height, weight, hip and waist circumference, systolic and diastolic blood pressure. These measures will take approximately 15 minutes to collect. These results can be recorded in the survey instrument where you will also find instructions on accurately recording the body composition data.

Survey

The survey instrument is quiet large but should only take approximately 30 minutes of your time to complete, and comprises four areas: 5. A health risk component.

6. Self‐measured body composition data.

7. A Personal Interest Component.

8. A Health Culture Audit.

You can access, complete and submit the online version of the survey via QUTs Key Survey. You have been emailed via Key Survey which has generated an individual URL for each respondent (preferred response method) or Electronically (it has also been emailed to you via the QAS email system) and to maintain de‐identification, if you choose to email your return please do so to: cedm‐[email protected] or Manually – you can print the survey instrument, complete and return via post to:

Ambulance Health Survey 2015 Centre for Emergency and Disaster Management QUT Kelvin Grove KELVIN GROVE QLD 4059

Returns that are made online are unidentifiable and those returns made by email or post will be separated from the original email/post by an independent person at QUT’s Centre for Emergency and Disaster Management (CEDM), who is not connected to the research, and forwarded on to the researcher. In this way, all respondents remain unidentifiable.

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Appendices

YOU SHOULD CONSIDER COMPLETING YOUR SELF‐MEASURES, AND IF A PARAMEDIC GATHER YOUR STATION WORKLOAD FROM YOUR OFFICER IN CHARGE (OIC) BEFORE ATTEMPTING TO

COMPLETE THIS SURVEY.

What will happen to my survey response and recorded measures?

After the data has been entered into a database and checked for accuracy, any paper results will be stored in a locked filing cabinet at QUT for 15 years. Electronic responses will be recorded on a password protected laptop and on a secure QUT server (also password protected). This data will only be accessible by the researcher and a nominated person from QUT, at this stage, the Principal Supervisor. You may withdraw your consent to participate at any time. THIS SURVEY IS NOT INTENDED FOR THE PURPOSE OF DIAGNOSING OR TREATING ANY HEALTH PROBLEMS

YOU MAY HAVE. PARTICIPATION IN THIS SURVEY DOES NOT TAKE THE PLACE OF VISITS TO A DOCTOR OR OTHER HEALTH

PROFESSIONALS.

Concerns or complaints regarding the conduct of this research

The QAS and QUT are committed to research integrity and the ethical conduct of research projects. However, if you have any concerns or complaints about the ethical conduct of this research you may contact the QUT Human Research Ethics Unit on 07 3138 5123 or email [email protected]. The QUT Human Research Ethics Unit is not connected with the research project and can facilitate a resolution to your concerns in an impartial manner. Thank you for helping with this research project and please keep this sheet for your information.

Research Team contacts:

Principal researcher: Richard Galeano [email protected] 04

3772 9735

Principal supervisor: Professor Gerard FitzGerald [email protected] 07 3138 3935 Associate supervisor: Professor Tony Parker [email protected] 07 3138 6173 Associate supervisor: Professor Michele Clark [email protected] 07 3138 3519

8.1.3 Consent Form for the AHS 2015

STATEMENT OF CONSENT

By entering your unique code below, you are indicating that you:

Have read and understood the information document regarding this project.

Have had any questions answered to your satisfaction.

Understand that if you have any additional questions you can contact the researcher.

Understand that you are free to withdraw at any time, without comment or penalty.

Understand that you can contact the Research Ethics Unit on 07 3138 5123 or email [email protected] if you have concerns about the ethical conduct of the project.

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Understand that non‐identifiable data collected in this project may be used as comparative data in future projects. However, your survey will not be linked to any other survey where this unique code is used, when there is a possibility of identifying you.

Agree to participate in the project.

By providing your unique code, you are deemed to have given your consent to participate in this research whilst remaining unidentifiable. However, your information may be compared across other and future surveys that use the same code.

Your code is made up of your mother’s initials and year of birth, e.g. my mother’s initials are ‘MMH’ and her year of birth is 1916 – my unique code is MMH1916

Your unique code:

Date you completed this survey:

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Appendices

Demographics

GENDER: Male☐ Female☐

AGE: YEARS WORKING IN AMBULANCE:

EMPLOYMENT TYPE: EMPLOYMENT STATUS:

01 EMD ☐ 01 PERMANENT FULL TIME ☐

02 PTO ☐ 02 PERMANENT PART TIME ☐

03 STUDENT PARAMEDIC ☐ 03 CASUAL ☐

04 ACP ☐ 04 OTHER ☐

05 CCP ☐

06 OIC/SUPERVISOR (SO1 TO SO4) ☐

07 MANAGER (M6 TO M8) ☐

08 DIRECTOR ☐

09 EXECUTIVE ☐

SHIFT WORK: Years ‐ If less than one year enter ‘1’. This is a cumulative total of shift work

throughout your working life and may include shift work outside of the QAS. Enter whole numbers only (no decimals and no

text) and take your answer to the closest whole number e.g. 1 year and 4 months is ‘1’ year.

Do you currently work shift work? Yes☐ No☐

If Yes ‐ how long have you currently worked shift work? Years leave blank if

not currently working shift work

If No, how long is it since you stopped working shift work? Years

STATION CATEGORY (1, 2, 3, 4 or 5) will allow me to make comparisons between self‐reported

health and different categories of QAS stations. If you do not know your station category please ask

your Officer in Charge (OIC).

Indicate your Station category here: 1 ☐ 2 ☐ 3 ☐ 4 ☐ 5 ☐ OR

I do not work at a station ☐→ 01

For the purposes of assessing whether there is an association between self‐reported health and workload

for paramedics, I am interested in the workload (number of cases – Code 1, 2, 3 and 4) at your station for

the 2014 CALENDER YEAR. This is critical information ‐ you should be able to gather this information from

your OIC.

Code 1: Code 2:

Code 3: Code 4:

383


If, after trying, you are unable to provide your workload and are willing to provide your station name

I will gather this information: Station Name:

On page 21 of this instrument is a sheet that needs to be completed, where I am asking you or someone you know (e.g. family

member, co‐worker) to measure your height, weight, hip and waist circumference and blood pressure (both systolic and

diastolic) using the included guidelines.

01 WHAT IS YOUR RELATIONSHIP STATUS?

01 Never in relationship ☐

02 In relationship ☐

03 Separated ☐

04 Divorced ☐

05 Widowed ☐

Education

02 WHAT IS THE HIGHEST LEVEL OF PRIMARY OR SECONDARY SCHOOL THAT YOU HAVE COMPLETED?

01 Year 12 or equivalent ☐




05 Year 8 or below ☐

06 Never attended school ☐

03 WHAT IS THE HIGHEST QUALIFICATION THAT YOU HAVE COMPLETED?

01 Trade certificate ☐

02 Diploma ☐

03 Undergraduate Degree ☐

04 Higher degree ☐

05 Other educational qualification

Employment

04 OTHER THAN YOUR QAS OCCUPATION, DO YOU DO OTHER WORK? More than one response is

allowed

01 None ☐

02 Paid employment ☐

03 Own business ☐

04 Voluntary work ☐

05 Home duties ☐

06 Other ☐

05 AT ANY TIME IN THE LAST YEAR HAVE YOU CONSIDERED LEAVING THE AMBULANCE SERVICE FOR OTHER EMPLOYMENT?

01 Yes ☐

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Appendices

02 No ☐→ 07

06 WHAT IS THE MAIN REASON YOU CONSIDERED LEAVING THE AMBULANCE SERVICE?

More than one response is allowed

01 Family pressures ☐

02 Retirement ☐

03 Health ☐

04 Work pressures ☐

05 Work unit climate ☐

06 Job dissatisfaction ☐

07 Shift work ☐

08 Lifestyle ☐

09 Remuneration ☐

10 Career opportunities ☐

11 Other ☐

07 WHAT KIND OF ROSTER DO YOU WORK?

01 Regular day/afternoon/night shifts ☐

02 Regular day/night shifts ☐

03 Regular day/afternoon shifts ☐

04 Regular day shifts ☐

05 Day and/or afternoon on call ☐

06 Other (please describe)

07 I do not do shift work ☐

Healthy Lifestyle

08 IN GENERAL WOULD YOU SAY YOUR HEALTH IS EXCELLENT, VERY GOOD, GOOD, FAIR OR POOR?

01 Excellent ☐

02 Very good ☐

03 Good ☐

04 Fair ☐

05 Poor ☐

09 DO YOU HAVE CHECK‐UPS WITH YOUR GENERAL PRACTIONER (GP)? Check‐ups may include – asking about your medical history; weighing you; taking your blood pressure; giving advice on how to improve your blood pressure; having other tests done.

01 Yes ☐

02 No ☐

03 Don’t have a GP ☐

04 Don’t know ☐

Mental Wellbeing

10 IN THE LAST 4 WEEKS ABOUT HOW OFTEN DID YOU FEEL TIRED FOR NO GOOD REASON?

01 All of the time ☐

385


02 Most of the time ☐

03 Some of the time ☐

04 A little of the time ☐

05 None of the time ☐

11 IN THE PAST 4 WEEKS ABOUT HOW OFTEN DID YOU FEEL NERVOUS?






12 IN THE PAST 4 WEEKS ABOUT HOW OFTEN DID YOU FEEL SO NERVOUS THAT NOTHING COULD

CALM YOU DOWN?






13 IN THE PAST 4 WEEKS ABOUT HOW OFTEN DID YOU FEEL HOPELESS?






14 IN THE PAST 4 WEEKS ABOUT HOW OFTEN DID YOU FEEL RESTLESS OR FIDGETY?






15 IN THE PAST 4 WEEKS ABOUT HOW OFTEN DID YOU FEEL SO RESTLESS THAT YOU COULD NOT

SIT STILL?






16 IN THE PAST 4 WEEKS ABOUT HOW OFTEN DID YOU FEEL DEPRESSED?






386

Appendices

17 IN THE PAST 4 WEEKS ABOUT HOW OFTEN DID YOU FEEL THAT EVERYTHING WAS AN EFFORT?






18 IN THE PAST 4 WEEKS ABOUT HOW OFTEN DID YOU FEEL SO SAD THAT NOTHING COULD CHEER YOU UP? 01 All of the time ☐





19 IN THE PAST 4 WEEKS ABOUT HOW OFTEN DID YOU FEEL WORTHLESS?






20 IN THE PAST 4 WEEKS HAVE YOU TAKEN ANY MEDICATIONS FOR YOUR MENTAL WELL‐BEING?

01 Yes ☐

02 No ☐ → 22

21 PLEASE INDICATE WHICH OF THE FOLLOWING MEDICATIONS YOU HAVE TAKEN?

More than one response allowed

01 Sleeping tablets or capsules ☐

02 Tablets or capsules for anxiety or nerves ☐

03 Tranquillisers ☐

04 Antidepressants ☐

05 Mood stabilisers ☐

06 Other medication for your mental well‐being – please specify

Pain

22 HOW MUCH BODILY PAIN HAVE YOU HAD IN THE LAST 4 WEEKS?

01 None ☐

02 Very mild ☐

03 Mild ☐

04 Moderate ☐

05 Severe ☐

06 Very severe ☐

387


23 DURING THE LAST FOUR WEEKS DID PAIN INTERFERE WITH YOUR NORMAL WORK (INCLUDING

WORK OUTSIDE THE HOME AND HOUSEWORK)?

01 Not at all ☐

02 A little bit ☐

03 Moderately ☐

04 Quite a bit ☐

05 Extremely ☐

Disability

24 DO YOU HAVE ANY OF THE FOLLOWING CONDITIONS? More than one response is

allowed

01 Sight problems not corrected by glasses or contact lenses

☐

02 Hearing problems

☐

03 Speech problems

☐

04 Blackouts, fits or loss of consciousness

☐

05 Difficulty learning or understanding things

☐

06 Limited use of arms or fingers

☐

07 Difficulty gripping things

☐

08 Limited use of legs or feet

☐

09 Any condition that restricts physical activity or physical work e.g. back problems, migraines

☐

10 Any disfigurement or deformity

☐

11 Any mental illness for which help or supervision is required

☐

12 Other

13 No disabilities

☐

Self-Reported Body Mass

25 DO YOU CONSIDER YOURSELF TO BE?

01 Acceptable weight ☐

02 Underweight ☐

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Appendices

03 Overweight ☐

04 Currently pregnant ☐

Exercise

The next few questions are about walking for fitness, recreation, sport or other reasons (e.g.

household chores, gardening or house work).

26 IN THE LAST 2 WEEKS HAVE YOU WALKED FOR FITNESS, RECREATION, SPORT OR OTHER

REASONS?

01 Yes ☐

02 No ☐ → 29

27 HOW MANY TIMES DID YOU WALK FOR FITNESS, RECREATION, SPORT OR OTHER REASONS IN

THE LAST 2 WEEKS?

Times

28 WHAT WAS THE TOTAL AMOUNT OF TIME YOU SPENT WALKING FOR FITNESS, RECREATION

SPORT OR OTHER REASONS IN THE LAST 2 WEEKS?

Hours minutes

The next few questions are about moderate and vigorous exercise. Please exclude walking that

you may have done for fitness, recreation, sport or other reasons.

29 IN THE LAST TWO WEEKS DID YOU DO ANY EXERCISE WHICH CAUSED A MODERATE INCREASE IN

YOUR HEART RATE OR BREATHING – THAT IS, MODERATE EXERCISE?

01 Yes ☐

02 No ☐ → 32

30 HOW MANY TIMES DID YOU DO MODERATE EXERCISE IN THE LAST TWO WEEKS?

Times

31 WHAT WAS THE TOTAL AMOUNT OF TIME YOU SPENT DOING MODERATE EXERCISE IN THE LAST

TWO WEEKS?

Hours minutes

32 IN THE LAST TWO WEEKS DID YOU DO ANY OTHER EXERCISE WHICH CAUSED A LARGE INCREASE

IN YOUR HEART RATE OR BREATHING THAT IS VIGOROUS EXERCISE?

01 Yes ☐

02 No ☐ → 35

33 HOW MANY TIMES DID YOU DO VIGOROUS EXERCISE IN THE LAST TWO WEEKS?

Times

389


34 WHAT WAS THE TOTAL AMOUNT OF TIME YOU SPENT DOING VIGOROUS EXERCISE IN THE LAST

TWO WEEKS?

Hours

minutes

35 THINKING ABOUT ALL THE PHYSICAL EXERCISE THAT YOU DO, WOULD YOU SAY YOU ARE MORE ACTIVE THAN THIS TIME LAST YEAR?

01 Yes ☐

02 No ☐

36 WHEN YOU ARE AT WORK, WHAT BEST DESCRIBES WHAT YOU DO ON A TYPICAL WORK DAY?

01 Mostly sitting ☐

02 Mostly standing ☐

03 Mostly walking ☐

04 Mostly heavy labour or physically demanding work ☐

05 Don’t know ☐

37 HOW MUCH TIME DO YOU SPEND SITTING AT WORK ON A TYPICAL DAY?

Hours

minutes

38 EXCLUDING ANY TIME YOU HAVE ALREADY MENTIONED, HOW MUCH TIME WOULD YOU

USUALLY SPEND SITTING WHILE WATCHING TELEVISION OR USING THE COMPUTER BEFORE OR AFTER WORK?

Hours

minutes

39 HOW MUCH TIME DO YOU SPEND SITTING IN OTHER LEISURE TIME ON A USUAL WORK/WEEK

DAY? e.g. Reading; eating; talking on the phone; listening to music; visiting friends; doing craft

or hobbies; other activities (not including watching television or using a computer)

Hours

minutes

40 ON THE DAYS YOU SPEND AWAY FROM WORK, HOW MUCH TIME DO YOU SPEND SITTING ON A TYPICAL DAY?

Hours

minutes

Smoking

41 DO YOU CURRENTLY SMOKE?

01 Yes ☐

02 No ☐ → 43

390

Appendices

42 DO YOU SMOKE REGULARLY, THAT IS AT LEAST ONCE PER DAY?

01 Yes ☐ → 46

02 No ☐

43 HAVE YOU EVER SMOKED REGULARLY, THAT IS, AT LEAST ONCE PER DAY?

02 Yes ☐

03 No ☐ → 46

44 HOW OLD WERE YOU WHEN YOU STOPPED SMOKING? Years

45 DID YOU STOP SMOKING IN THE LAST YEAR?

01 Yes ☐

02 No ☐

Dietary Behaviours

46 HOW MANY SERVES OF VEGETABLES DO YOU USUALLY EAT EACH DAY? (A standard serve is one

cup of green or orange vegetables, ½ cup dried or cooked beans or lentils or one cup of green

leafy or salad vegetables)

01 1 serve or less ☐

02 2 serves ☐

03 3 serves ☐

04 4 serves ☐

05 5 serves ☐

06 6 serves or more ☐

07 Don’t eat vegetables ☐

08 Don’t know ☐

47 HOW MANY SERVES OF FRUIT DO YOU USUALLY EAT EACH DAY? (A standard serve of fruit is 1

medium piece (apple, banana, orange or pear); 2 small pieces (apricots, kiwi fruit, plum); 1 cup

diced pieces or canned fruit, ½ cup juice; 4 dried apricots or 1 ½ tablespoons of sultanas)

01 1 serve or less ☐

02 2 serves ☐

03 3 serves ☐

04 4 serves ☐

05 5 serves ☐

06 6 serves or more ☐

07 Don’t eat fruit ☐

08 Don’t know ☐

Alcohol

48 HOW LONG AGO DID YOU LAST HAVE AN ALCOHOLIC DRINK?

01 1 week or less ☐

391


02 More than 1 week to less than 2 weeks ☐

03 2 weeks to less than 1 month ☐

04 1 month to less than 3 months ☐

05 3 months to less than 12 months ☐

06 12 months ☐

07 More than 12 months ☐

08 Never ☐ → 55

09 Don’t remember ☐

49 ON WHICH DAYS IN THE LAST 7 DAYS DID YOU HAVE DRINKS THAT CONTAINED ALCOHOL?


01 All ☐

02 Monday ☐

03 Tuesday ☐

04 Wednesday ☐

05 Thursday ☐

06 Friday ☐

07 Saturday ☐

08 Sunday ☐

09 I did not drink alcohol in the last seven days ☐ → 51

50 HOW MANY DRINKS DID YOU HAVE IN THE LAST 7 DAYS? Drinks

51 HOW OFTEN DID YOU HAVE AN ALCOHOLIC DRINK IN THE LAST 12 MONTHS?

01 Every day ☐

02 5 to 6 days a week ☐



05 2 to 3 days a month ☐

06 About 1 day a month ☐

07 Less often than 1 day a month ☐

08 Don’t know ☐

The next questions are about the number of standard drinks you have had in the last 12 months

(See standard drink guide on the next page)

52 IN THE LAST 12 MONTHS HOW OFTEN HAVE YOU HAD 5 OR MORE STANDARD DRINKS IN A

DAY? More than one response is allowed

01 Times per week

02 Number of times in the last 12 months

03 ☐ Nil

53 HAS THE AMOUNT OF ALCOHOL THAT YOU USUALLY DRINK INCREASED, DECREASED OR STAYED

ABOUT THE SAME SINCE LAST YEAR?

01 Increased ☐

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Appendices

02 Decreased ☐

03 Stayed about the same ☐

54 ON HOW MANY DAYS IN THE LAST 12 MONTHS DID YOU STAY AWAY FROM WORK BECAUSE OF

ALCOHOL OR OTHER SUBSTANCE ABUSE?

01 I did not stay away from work ☐

02 days

Asthma

55 HAVE YOU EVER BEEN DIAGNOSED WITH ASTHMA?

01 Yes ☐

02 No ☐

56 SYMPTOMS OF ASTHMA INCLUDE COUGHING, WHEEZING, SHORTNESS OF BREATH AND CHEST

TIGHTNESS. HAVE YOU HAD ANY SYMPTOMS OF ASTHMA OR TAKEN TREATMENT FOR ASTHMA

IN THE LAST 12 MONTHS?

01 Yes ☐

02 No ☐→58


YOUR ASTHMA?


02 days

Cancer

58 DO YOU OR ANYONE ELSE REGULARLY CHECK YOUR SKIN FOR ANY CHANGES IN FRECKLES OR

MOLES?

01 Yes ☐

02 No ☐

03 Don’t know ☐

59 HAVE YOU EVER BEEN DIAGNOSED WITH CANCER?

01 Yes ☐

02 No ☐ → 62

60 INCLUDING CANCER WHICH IS IN REMISSION, DO YOU CURRENTLY HAVE CANCER?

01 Yes ☐

02 No ☐→ 62


YOUR CANCER?


393


02 days

Cardiovascular/Circulatory

62 INCLUDING ANY CONDITIONS WHICH CAN BE CONTROLED BY MEDICATION, HAVE YOU EVER

BEEN DIAGNOSED WITH A CARDIOVASCULAR OR CIRCULATORY CONDITION?

01 Yes ☐

02 No ☐ → 65

63 WHAT ARE THE NAMES OF THESE CONDITIONS? More than one response is allowed

01 Rheumatic heart disease ☐

02 Heart attack ☐

03 Heart failure ☐

04 Stroke (including after effects of stroke) ☐

05 Angina ☐

06 High blood pressure/hypertension ☐

07 Low blood pressure/hypotension ☐

08 Hardening of the arteries/atherosclerosis/arteriosclerosis ☐

09 Fluid problems/fluid retention/oedema ☐

10 High cholesterol ☐

11 Rapid or irregular heartbeats/tachycardia/palpitations ☐

12 Heart murmurs/heart valve disorder ☐

13 Haemorrhoids ☐

14 Varicose veins ☐

15 Other – specify


YOUR CONDITION/S?


02 days

65 WAS YOUR CHOLESTEROL CHECKED IN THE LAST 12 MONTHS?

01 Yes ☐

02 No ☐

03 Don’t know ☐

66 HAVE YOU HAD YOUR BLOOD PRESSURE CHECKED IN THE LAST 12 MONTHS?

01 Yes ☐

01 No ☐→ 68

02 Don’t know ☐→ 68

394

Appendices

67 IF YOU HAD YOUR BLOOD PRESSURE TESTED IN THE LAST 12 MONTHS, WHAT WAS IT? This

should not be the Blood Pressure that you have measured for this survey.

01 Systolic /Diastolic

02 Don’t remember ☐

Arthritis

68 HAVE YOU EVER HAD GOUT?

01 Yes ☐

02 No ☐

03 Don’t know ☐

69 HAVE YOU EVER HAD RHEUMATISM?

01 Yes ☐

02 No ☐

03 Don’t know ☐

70 HAVE YOU EVER BEEN DIAGNOSED WITH ARTHRITIS?

01 Yes ☐

02 No ☐→ 72



YOUR ARTHRITIS?


02 days

Osteoporosis

72 HAVE YOU EVER BEEN DIAGNOSED WITH OSTEOPOROSIS OR OSTEOPENIA (OSTEOPENIA IS A

MILD LOSS OF BONE DENSITY THAT CAN LEAD TO OSTEOPOROSIS)

01 Yes ☐

02 No ☐→ 75


73 WHICH ONE WERE YOU TOLD YOU HAVE?

01 Osteoporosis ☐

02 Osteopenia ☐

03 Both ☐


YOUR OSTEOPOROSIS/OSTEOPENIA?

395



02 days

Diabetes

75 HAVE YOU EVER BEEN DIAGNOSED WITH DIABETES?

01 Yes ☐

02 No ☐

76 HAVE YOU EVER BEEN DIAGNOSED WITH HIGH SUGAR LEVELS IN YOUR BLOOD OR URINE?

01 Yes ☐

02 No ☐

→82

77 WHAT TYPE OF DIABETES WERE YOU TOLD YOU HAVE? More than one response is

allowed

01 Type 1 (Insulin Dependent Diabetes Mellitus/Juvenile Onset Diabetes/Type A) ☐

02 Type 2 (Non‐Insulin Dependent Diabetes Mellitus/Adult Onset Diabetes/Type B) ☐

03 Gestational (pregnancy) ☐

04 Diabetes Insipidus ☐


06 Don’t know ☐

78 DO YOU CURRENTLY HAVE DIABETES?

01 Yes ☐

02 No ☐

→ 82

03 Don’t know ☐

→ 82

79 DID YOUR DIABETES/HIGH SUGAR LEVELS INTERFERE WITH YOUR WORK OR OTHER DAILY

ACTIVITIES IN THE LAST 12 MONTHS?

01 Yes ☐

02 No ☐

03 Don’t know ☐


YOUR DIABETES/HIGH SUGAR LEVELS?


02 days

81 ARE YOU CURRENTLY HAVING INSULIN EVERY DAY?

01 Yes ☐

396

Appendices

02 No ☐

03 Don’t know ☐

Sight and Hearing

82 ARE YOU COLOUR BLIND? 01 Yes ☐

02 No ☐

83 DO YOU CURRENTLY WEAR GLASSES OR CONTACT LENSES TO PARTIALLY CORRECT YOUR SIGHT?

01 Yes ☐

02 No ☐ → 85

84 ARE ANY OF YOUR SIGHT PROBLEMS DUE TO YOUR DIABETES/HIGH SUGAR LEVEL?

01 Yes ☐

02 No ☐

03 Don’t know ☐

85 HAVE YOU ANY HEARING PROBLEMS OR PROBLEMS WITH YOUR EARS THAT HAVE LASTED, OR

ARE EXPECTED TO LAST, FOR 6 MONTHS OR MORE?

01 Yes ☐

02 No ☐ → 87

86 WHAT HEARING OR EAR PROBLEMS DO YOU HAVE? More than one response is allowed

01 Total deafness ☐

02 Deaf in 1 ear ☐

03 Hearing loss/partially deaf ☐

04 Tinnitus ☐

05 Meniere’s Disease ☐

06 Otitis Media ☐


08 Don’t know ☐

Long Term Conditions

87 WHICH OF THESE DO YOU HAVE? More than one response is allowed.

01 Hay fever ☐

02 Sinusitis or sinus allergy ☐

03 Other allergy ☐

04 Anaemia ☐

05 Bronchitis ☐

397


06 Emphysema ☐

07 Epilepsy ☐

08 Fluid problems/fluid retention/oedema (exclude those due to heart conditions) ☐

09 Hernias ☐

10 Kidney stones ☐

11 Migraine ☐

12 Psoriasis ☐

13 Stomach ulcers or other gastrointestinal ulcers ☐

14 Thyroid trouble/goitre ☐

15 Depression ☐

16 Feeling depressed ☐

17 Back – slipped disc or other disc problems ☐

18 Back problems or back pain ☐

19 Other

20 I do not have any long term conditions ☐

Mental Health

88 DO YOU HAVE A DIAGNOSED MENTAL HEALTH CONDITION?

01 Yes ☐

02 No ☐→ 92

89 WHAT TYPE OF MENTAL HEALTH CONDITION WHERE YOU DIAGNOSED WITH?


01 Depression ☐

02 Post‐Traumatic Stress Disorder ☐

03 Generalised Anxiety Disorder ☐

04 Adjustment Disorder ☐

05 Substance abuse ☐

06 Other

90 DO YOU TAKE ANY MEDICATIONS FOR YOUR MENTAL HEALTH CONDITION/S?


01 Sleeping tablets or capsules ☐

02 Tablets or capsules for anxiety or nerves ☐

03 Tranquillisers ☐

04 Anti‐depressants ☐

05 Mood stabilisers ☐

06 Other medication (specify)

07 No medication ☐

398

Appendices

91 ON HOW MANY DAYS DID YOU STAY AWAY FROM WORK IN THE LAST 12 MONTHS BECAUSE OF

YOUR MENTAL HEALTH CONDITION/S?


02 days

Family Stressors

The next few questions are about the things that may have been a problem for you, family or

close friends, during the last 12 months.

92 HAVE ANY OF THESE BEEN A PROBLEM FOR YOU OR ANYONE ELSE CLOSE TO YOU DURING THE

LAST 12 MONTHS? More than one response allowed

01 Serious illness ☐

02 Serious accident ☐

03 Death of a family member or close friend ☐

04 Mental illness ☐

05 Serious disability ☐

06 None of these issues have been a problem ☐

93 HAVE ANY OF THESE BEEN A PROBLEM FOR YOU IN THE LAST 12 MONTHS?

More than one response allowed

01 Divorce or separation ☐

02 Involuntary loss of job ☐

03 Alcohol or drug related problems ☐

04 Witness to violence ☐

05 Abuse or violent crime ☐

06 Trouble with police ☐

07 Gambling problem ☐


09 None of these have been a problem ☐

BODY COMPOSITION DATA

Self‐measured results

Height (cm)

Weight (kg)

Hip © (cm)

Waist ©(cm)

Blood Pressure (BP) Systolic

Blood Pressure (BP) Diastolic

399


Height: Remove your shoes, bulky clothing, hair ornaments, and unbraid hair that interferes with the measurement. Take the height measurement on flooring that is not carpeted and against a flat surface such as a wall with no molding. Stand with feet flat, together, and against the wall. Make sure legs are straight, arms are at sides, and shoulders are level. Make sure you are looking straight ahead and that the line of sight is parallel with the floor and stand with head, shoulders, buttocks, and heels touching the flat surface (wall). Weight should be measured after the shoes have been removed and any heavy objects (Keys, wallets, loose change, bulky clothing) have been removed. The weight scales used should be reliable and preferably recently calibrated. Hip circumference should be measured at the widest part of the buttocks. The measuring tape should be snug, not restrictive and stretch resistant. You should be standing with feet together and weight evenly distributed across the feet. You should adapt a relaxed posture and the measurement should be taken at the end of expiration.(Nishida et al., 2010) Waist circumference is measured at the midpoint between the lowest palpable rib and the top of the iliac crest. The measuring tape should be snug, not restrictive and stretch resistant. You should be standing with feet together and weight evenly distributed across the feet. You should adapt a relaxed posture and the measurement should be taken at the end of expiration.(Nishida et al., 2010)

Blood pressure should be measured with the participant sitting in a relaxed posture after one minute of deep, slow breathing. Preferably your blood pressure should be taken using a ‘Lifepak 12’ or the ‘Corpuls 3’ Monitor Defibrillator.

400

Appendices

PERSONAL INTEREST

A Personal Interest Survey is an important component of the data gathering and may uncover met

and unmet health needs with QAS operational personnel and will, in formulating recommendations,

ensure that effort in relation to their health is placed where it is not only needed, but where their

interests lay. A workplace, for this survey, is defined as any location (hospital, station, ambulance,

shop, road side, patient’s house, OpCen, office, etc.), that a person may be present at during work

and may include a number of different workplaces throughout a working day.

Breaks

94 DO YOU TYPICALLY TAKE/GET REGULAR BREAKS DURING THE DAY?

01 Yes, most “typical” days ☐

02 No, usually I don’t take/get a break ☐ → Q 96

95 WHAT SORT OF BREAKS DO YOU GET/TAKE? More than one response is

allowed

01 Morning tea break/s

☐

02 Afternoon tea break/s

☐

03 Lunch break/s

☐

04 Station Breaks

☐

05 Hospital breaks

☐

06 Other (specify)

96 IF YOU DON’T TAKE BREAKS OR CAN’T PLAN FOR BREAKS, WHY NOT? (more than one response

is allowed)

01 Pressure to get work done

☐

02 I eat on the run

☐

03 I feel that time spent (e.g. chatting, returning, waiting at hospital, etc.) takes up my “break”

time ☐

04 I just don’t want to

☐

05 I feel guilty

☐

06 Other (specify)

07 I get regular breaks

☐

401


97 DOES YOUR WORKPLACE ENCOURAGE YOU TO TAKE A BREAK?

01 Yes

☐

02 No

☐

03 Don’t know

☐

Lifestyle changes

98 WOULD YOU MORE LIKELY BE INVOLVED IN A WELLNESS PROGRAM IF THERE WERE INCENTIVES? 01 Yes

☐ 02 No

☐ 03 Don’t know

☐ As part of their wellness program, some worksites provide gym memberships and/or facilities,

group activities, access to nutritionists, dieticians and exercise physiologists.

99 WOULD YOU USE THESE FACILITIES IF AVAILABLE?

01 Definitely, I would use them ☐

02 It sounds interesting and I’d try to use them ☐

03 It sounds interesting, but I probably wouldn’t use them ☐

04 It’s not something I would use at all ☐

05 Other (specify)

Barriers to Regular Physical Activity

100 HOW OFTEN DO THE FOLLOWING PREVENT YOU FROM GETTING REGULAR PHYSICAL ACTIVITY?

Please choose one answer for each item.

Never Rarely Sometimes Often Very Often

01 Self‐conscious about my looks when I exercise

1 ☐ 2 ☐ 3 ☐ 4 ☐ 5 ☐

02 Lack of interest in exercise or physical activity

1 ☐ 2 ☐ 3 ☐ 4 ☐ 5 ☐

03 Lack of time 1 ☐ 2 ☐ 3 ☐ 4 ☐ 5 ☐ 04 Lack of energy 1 ☐ 2 ☐ 3 ☐ 4 ☐ 5 ☐ 05 Lack of companionship 1 ☐ 2 ☐ 3 ☐ 4 ☐ 5 ☐ 06 Lack of enjoyment from exercise or

physical activity 1 ☐ 2 ☐ 3 ☐ 4 ☐ 5 ☐

07 Discouragement 1 ☐ 2 ☐ 3 ☐ 4 ☐ 5 ☐ 08 Lack of equipment 1 ☐ 2 ☐ 3 ☐ 4 ☐ 5 ☐ 09 Lack of good weather 1 ☐ 2 ☐ 3 ☐ 4 ☐ 5 ☐ 10 Lack of skills 1 ☐ 2 ☐ 3 ☐ 4 ☐ 5 ☐ 11 Lack of facilities or space 1 ☐ 2 ☐ 3 ☐ 4 ☐ 5 ☐ 12 Lack of knowledge on how to exercise 1 ☐ 2 ☐ 3 ☐ 4 ☐ 5 ☐ 13 Lack of good health 1 ☐ 2 ☐ 3 ☐ 4 ☐ 5 ☐

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Appendices

14 Fear of injury 1 ☐ 2 ☐ 3 ☐ 4 ☐ 5 ☐ 15 Cost of trainers/coaches 1 ☐ 2 ☐ 3 ☐ 4 ☐ 5 ☐

101 PLEASE INDICATE HOW LIKELY YOU WOULD BE TO PARTICIPATE IN HEALTH PROGRAMS DURING

THE FOLLOWING TIMES.

Please choose one answer for each item.

Extremely likely

Somewhat likely

Somewhat unlikely

Extremely unlikely

01 Before work 1 ☐ 2 ☐ 3 ☐ 4 ☐

02 During work 1 ☐ 2 ☐ 3 ☐ 4 ☐

03 After work 1 ☐ 2 ☐ 3 ☐ 4 ☐

04 Rostered days off 1 ☐ 2 ☐ 3 ☐ 4 ☐

Are there any other suggestions you would like to make?

HEALTH CULTURE

A Health Culture Audit looks at how people stay healthy, individual attitudes and personal

perceptions in relation to a healthy lifestyle, job satisfaction and social health whilst working for

the QAS and, how does the organisational culture play a role in supporting healthy employee

lifestyles?

Work Unit Climate

102 MY IMMEDIATE SUPERVISOR SUPPORTS EFFORTS TO ADOPT HEALTHIER LIFESTYLE PRACTICES?

01 Strongly agree ☐

02 Agree ☐

03 Undecided/don’t know ☐

04 Disagree ☐

05 Strongly disagree ☐

103 MY WORK UNIT HAS A SENSE OF COMMUNITY (e.g. people really get to know each other, feel

that they belong and care for each other in times of need)?


02 Agree ☐


04 Disagree ☐


104 MY WORK UNIT HAS A SHARED VISION (e.g. people feel that the organisation’s conduct is

consistent with personal values and people are clear about how they fit in)?


02 Agree ☐


403


04 Disagree ☐


105 MY WORK UNIT HAS A POSITIVE OUTLOOK (e.g. people enjoy their work, celebrate

accomplishments, adopt a “we can do it” attitude and bring out the best in each other)?


02 Agree ☐


04 Disagree ☐


404

Appendices

Job Satisfaction

Job satisfaction levels have been shown to be an important factor influencing the health

of workers (Faragher et al., 2005) and worry about work conditions seems to be an

important risk factor for health complaints from workers (Aasa, Brulin, et al., 2005).

106 I LOOK FORWARD TO GOING TO WORK AT THE START OF EVERY BLOCK OF SHIFTS OR THE

START OF EVERY WEEK?

01 Yes ☐

02 No ☐

107 I FEEL POSITIVE AND UP MOST OF THE TIME I AM AT WORK?

01 Yes ☐

02 No ☐

108 I HAVE ENERGY AT THE END OF A WORK DAY TO ATTEND TO THE PEOPLE I CARE ABOUT?

01 Yes ☐

02 No ☐

109 I HAVE ENERGY AT THE END OF THE DAY TO ENGAGE IN PERSONAL INTERESTS?

01 Yes ☐

02 No ☐

110 MOST INTERACTIONS AT WORK ARE POSITIVE?

01 Yes ☐

02 No ☐

111 I HAVE GOOD FRIENDS AT WORK?

01 Yes ☐

02 No ☐

112 I FEEL RECOGNISED AND APPRECIATED AT WORK?

01 Yes ☐

02 No ☐

03 Don’t know ☐

113 MY VALUES FIT WITH THE ORGANISATIONAL VALUES?

01 Yes ☐

02 No ☐

03 Don’t know ☐

114 I TRUST OUR LEADERSHIP TEAM?

01 Yes ☐

02 No ☐

03 Don’t know ☐

115 I RESPECT THE WORK OF MY PEERS?

01 Yes ☐

405


02 No ☐

116 I FEEL INVOLVED IN DECISIONS THAT AFFECT MY WORK UNIT?

01 Yes ☐

02 No ☐

117 CREATIVITY AND INNOVATION ARE SUPPORTED?

01 Yes ☐

02 No ☐

03 Don’t know ☐

118 I FEEL INFORMED ABOUT WHAT IS GOING ON?

01 Yes ☐

02 No ☐

03 Don’t know ☐

119 I KNOW WHAT IS EXPECTED OF ME AT WORK?

01 Yes ☐

02 No ☐

120 MY IMMEDIATE SUPERVISOR CARES ABOUT ME AS A PERSON?

01 Yes ☐

02 No ☐

03 Don’t know ☐

121 MY OPINION COUNTS?

01 Yes ☐

02 No ☐

03 Don’t know ☐

122 MY IMMEDIATE SUPERVISOR REVIEWS MY PROGRESS?

01 Yes ☐

02 No ☐

Feelings While Working

Job satisfaction, job performance and fatigue can be shown to be related (Schwartz, 2010). Whilst

it is fully recognised that there are great variations in the level of fatigue and tiredness

experienced as a consequence of work, this next section is aimed at developing an overall

impression about how YOU feel at work while on day shifts (0600/0700 to 1800/1900 – includes

day workers only), evening shifts (any shift that starts either late morning or early afternoon and

finishes at or before midnight), night shifts (1800/1900 to 0600/0700), an on‐call roster (day/on‐

call or day/afternoon on call or a 24 hour roster with and on‐call component) and on days off.

123 HOW OFTEN DO YOU EXPERIENCE ANXIETY AND TENSION WHILST AT WORK?

01 Occasionally ☐

02 Frequently ☐

03 Constantly ☐

04 Never ☐→ Q 125

406

Appendices

124 WHEN YOU DO EXPERIENCE ANXIETY AND TENSION, HOW WOULD YOU DESCRIBE IT?

01 Mild ☐

02 Moderate ☐

03 High ☐

125 HOW OFTEN DO YOU EXPERIENCE FATIGUE WHILST AT WORK?

01 Occasionally ☐

02 Frequently ☐

03 Constantly ☐

04 Never ☐→ Q 136

126 WHEN YOU EXPERIENCE FATIGUE, HOW WOULD YOU DESCRIBE IT?

01 Mild ☐

02 Moderate ☐

03 High ☐

127 WHEN YOU EXPERIENCE FATIGUE DOES YOUR ANXIETY AND TENSION LEVELS:

01 Increase? ☐

02 Decrease? ☐

03 Stay the same? ☐

128 WHEN YOU EXPERIENCE FATIGUE, DURING WHICH PART OF THE SHIFT ARE YOU MOST

AFFECTED?

01 Start ☐

02 Middle ☐

03 End ☐

04 On‐call ☐

129 PLEASE RATE YOUR VULNERABILITY TO FATIGUE WHILST WORKING DAY SHIFTS?

01 Not at all ☐

02 A little ☐

03 Quite a bit ☐

04 Very much ☐

130 PLEASE RATE YOUR VULNERABILITY TO FATIGUE WHILST WORKING EVENING/AFTERNOON

SHIFTS?

01 Not at all ☐

02 A little ☐

03 Quite a bit ☐

04 Very much ☐

05 I do not work evening shifts ☐

131 PLEASE RATE YOUR VULNERABILITY TO FATIGUE WHILST WORKING NIGHT SHIFTS?

01 Not at all ☐

02 A little ☐

03 Quite a bit ☐

04 Very much ☐

05 I do not work night shifts ☐

407


132 PLEASE RATE YOUR VULNERABILITY TO FATIGUE WHILST WORKING ON‐CALL?

06 Not at all ☐

07 A little ☐

08 Quite a bit ☐

09 Very much ☐

10 I do not work on‐call ☐

133 DOES YOUR VULNERABILITY TO FATIGUE CHANGE IF, DURING YOUR SHIFT, YOU DO NOT GET A

BREAK?

01 Not at all ☐

02 A little ☐

03 Quite a bit ☐

04 Very much ☐

134 DOES YOUR VULNERABILITY TO FATIGUE CHANGE IF YOU HAVE TO WORK INVOLUNTARY

OVERTIME? (i.e. overtime that occurs because you started a case before shift end, overtime that

occurs because you are the closest/only available car after your shift finish time, overtime that

occurs because someone is running late, shortage of staff, or you have to meet deadlines on an

important project or issue).

01 Not at all ☐

02 A little ☐

03 Quite a bit ☐

04 Very much ☐

135 DOES YOUR VULNERABILITY TO FATIGUE CHANGE IF YOU HAVE TO WORK DURING THE ON‐CALL

COMPONENT OF YOUR ROSTER EVEN THOUGH YOU MAY GET A FULL 10 HOUR FATIGUE BREAK?

05 Not at all ☐

06 A little ☐

07 Quite a bit ☐

08 Very much ☐

136 DO YOU EXPERIENCE INCREASED LEVELS OF ANXIETY AND TENSION AS A RESULT OF HAVING TO

WORK INVOLUNTARY OVERTIME? (i.e. overtime that occurs because you started a case before

shift end, overtime that occurs because you are the closest/only available car after your shift

finish time, overtime that occurs because someone is running late, shortage of staff, or you have

to meet deadlines on an important project or issue).

01 Not at all ☐

02 A little ☐

03 Quite a bit ☐

04 Very much ☐

137 DO YOU EXPERIENCE INCREASED LEVELS OF ANXIETY AND TENSION AS A RESULT OF HAVING TO

WORK DURING THE ON‐CALL COMPONENT OF YOUR ROSTER?

05 Not at all ☐

06 A little ☐

07 Quite a bit ☐

08 Very much ☐

408

Appendices

Hours worked

138 ABOUT HOW MANY HOURS DID YOU WORK IN THE LAST FOUR WEEKS (28 DAYS – this should

include all hours worked in all types of paid employment)?

01 Hours

Work Experiences/Performance

These questions are about the time you spent during your hours of work in the past four weeks.

Please indicate for each question the aspect which comes closest to your own experience.

139 HOW OFTEN WAS YOUR PERFORMANCE HIGHER THAN MOST WORKERS ON YOUR JOB? All of the time Most of the

time Some of the

time A little of the

time None of the

time

☐ ☐ ☐ ☐ ☐ 140 HOW OFTEN WAS YOUR PERFORMANCE LOWER THAN MOST WORKERS ON YOUR JOB?

All of the time Most of the time

Some of the time

A little of the time

None of the time

☐ ☐ ☐ ☐ ☐ 141 HOW OFTEN DID YOU DO NO WORK AT TIMES WHEN YOU WERE SUPPOSED TO BE WORKING?

All of the time Most of the time

Some of the time

A little of the time

None of the time

☐ ☐ ☐ ☐ ☐

142 USING A SCALE OF ZERO TO TEN, WHERE ZERO IS THE WORST JOB PERFORMANCE ANYONE

COULD HAVE AT YOUR JOB AND TEN IS THE PERFORMANCE OF A TOP WORKER, HOW WOULD

YOU RATE YOUR OVERALL JOB PERFORMANCE ON THE DAYS YOU WORKED IN THE LAST FOUR

WEEKS?

Worst Performance

Top Performance

☐ 0

☐ 1

☐ 2

☐ 3

☐ 4

☐ 5

☐ 6

☐ 7

☐ 8

☐ 9

☐ 10

409


Shift work is associated with sleep deprivation and an interruption of physical, mental and

behavioural cycles. The resultant fatigue is often associated with poor work performance due to

frequent lapses of attention and increased reaction time (Barker & Nussbaum, 2011). The

following questions relate to your sleep habits on day shifts (0600/0700 to 1800/1900 ‐ this applies

to workers who only work days), evening /afternoon shifts (any shift that starts either late

morning or early afternoon and finishes at or before midnight), night shifts (1800/1900 to

0600/0700), the on call component of your roster and on days off.

Sleep Patterns While On Day Shift

143 HOW MANY HOURS DO YOU USUALLY SLEEP PER 24 HOURS WHILE ON DAY SHIFT? Day shifts

include those who work days only e.g. a CSO who works weekends but only does day shifts or a

manager working out of a LASN or central office.

01 Less than 4 hours ☐

02 4 – 6 hours ☐

03 7 – 8 hours ☐

04 More than 8 hours per day ☐

05 I do not work day shifts ☐

144 HOW WOULD YOU TYPICALLY DESCRIBE THE QUALITY OF YOUR SLEEP WHILE ON DAY SHIFTS?

01 Very poor ☐

02 Poor ☐

03 Fair ☐

04 Good ☐

05 Very good ☐

Sleep Patterns Whilst On Evening Shifts

145 HOW MANY HOURS DO YOU USUALLY SLEEP PER 24 HOURS WHILE ON EVENING SHIFT?


02 4 – 6 hours ☐

03 7 – 8 hours ☐


05 I do not work evening shifts ☐→ 147

146 HOW WOULD YOU TYPICALLY DESCRIBE THE QUALITY OF YOUR SLEEP WHILE ON EVENING

SHIFTS?

01 Very poor ☐

02 Poor ☐

03 Fair ☐

04 Good ☐

05 Very good ☐

Sleep Patterns While On Night Shift

410

Appendices

147 HOW MANY HOURS DO YOU USUALLY SLEEP PER 24 HOURS WHILE ON NIGHT SHIFT?


02 4 – 6 hours ☐

03 7 – 8 hours ☐


05 I do not work night shifts ☐→ 150

148 HOW WOULD YOU TYPICALLY DESCRIBE THE QUALITY OF YOUR SLEEP WHILE ON NIGHT SHIFTS?

01 Very poor ☐

02 Poor ☐

03 Fair ☐

04 Good ☐

05 Very good ☐

149 DURING A NIGHT SHIFT HOW MUCH SLEEP WOULD YOU GET DURING THE SHIFT?

Number of hours Hours

Sleep Patterns during the On-Call Component of Your Roster

150 HOW MANY HOURS DO YOU USUALLY SLEEP PER 24 HOURS DURING THE ON‐CALL COMPONENT

OF YOUR ROSTER (e.g. for the 7 days on‐call during the 8 day component of an 8/6 roster)?


02 4 – 6 hours ☐

03 7 – 8 hours ☐


05 I do not work on‐call ☐→ 153

151 HOW WOULD YOU TYPICALLY DESCRIBE THE QUALITY OF YOUR SLEEP DURING THE ON‐CALL

COMPONENT OF YOUR ROSTER (e.g. for the 7 days on‐call during the 8 day component of an

8/6 roster)?

01 Very poor ☐

02 Poor ☐

03 Fair ☐

04 Good ☐

05 Very good ☐

152 DURING THE ON‐CALL COMPONENT OF YOUR ROSTER HOW MUCH SLEEP WOULD YOU

TYPICALLY GET WHILST ACTUALLY ON‐CALL (e.g. you are on call for 14 hours per 24 hour period

and get 8 hours sleep on average)?

Number of hours Hours

Sleep Patterns While On Rostered Days Off Between Shifts

153 HOW MANY HOURS DO YOU USUALLY SLEEP PER 24 HOURS WHILE ON DAYS OFF?


411


02 4 – 6 hours ☐

03 7 – 8 hours ☐


154 HOW WOULD YOU TYPICALLY DESCRIBE THE QUALITY OF YOUR SLEEP WHILE ON DAYS OFF?

01 Very poor ☐

02 Poor ☐

03 Fair ☐

04 Good ☐

05 Very good ☐

hank you for the valuable information you have

provided.

Your input, together with other respondents, will allow

me to compose a comprehensive picture of the health

of operational personnel in the QAS, identify factors

that influence their health, identify associations

between work and lifestyle, and determine how work

groups and ambulance organisations may impact

positively on the overall health of personnel.

T

412

Appendices

8.2 Appendix B: Literature Review Synopsis

Citation Aim Method Limitations Population/Sample Outcome Petrie, K. et al 2018

Assess the prevalence of mental health conditions worldwide

Systematic review Eligibility – quantitative data and at least one of PTSD, depression, anxiety and psychological distress

Peer review English journal & all international work may not have been captured

Publication bias in smaller studies

Currently employed ambulance personnel only

Ambulance personnel including paramedics, EMTs, ambulance workers. EMDs, administrative staff and student paramedics who were not undertaking on-the-job training were excluded

PTSD considerably higher than rates seen in the general population

Maguire, B. et al. 2018

Assess the literature on violence against EMS personnel

Systematic review of literature from 2000-2016. English language 25 out of 2655 studies met criteria.

Other personnel e.g. nurses may have been missed

English language only may have excluded some papers

Differences in workforce

25 studies which included 1006236 personnel, 32579 cases & 1100 injury reports from Spain, Canada, Australia, Poland, U.S., Turkey, India, Sweden, Iranian

Violence is a common risk Lack of interventions Current interventions have no evidence base

Varker, T. et al 20117

Evidence map methodology to assess peer reviewed articles re mental health in Aus. Emergency services personnel

Evidence mapping systematic review of peer reviewed articles between Jan 2011 and July 2016

Maybe subjective, but used experts

Snapshot No assessment of

quality or bias in studies

Only 5 years

Paramedics 45% Police 33% Firefighters 21% 76endS 8853 PARAMEDICS 3541 Police officer All police form 24 stations 3343 student paramedics 5934 firefighters

Need for further research as few studies on suicide, personality, stigma, & preemployment factors that may contribute. No studies on self-harm, bullying, alcohol/substance abuse, barriers to care, family experiences

413


Citation Aim Method Limitations Population/Sample Outcome Pek, E. et al. (2015)

Assess physical & mental health of Hungarian amb. Workers using the SF36 Compare ć already known national & international indicators

C/S survey using the SF36 questionnaire*

Literature search limited

Self-report data Cross sectional

design

Return rate of 77% (n = 810) Pre-hospital personnel from 65 amb. Stations in 6 counties in Hungary

↑ health status compared to the GP 76.5% vs. 54.2%) ć with VG/G Limitations due to HS < GP ↑overweightness and obesity than GP (74.4% vs. 55.4%) ↑ smoking than GP (66.5% vs. 45%) ↑ Alcohol consumption than GP (56.8% vs. 19%) ↑ years of labour and secondary jobs influences health status –vly. ↑ workload & shift work related to nutritional habits, free time & evident in rates of BMI

Studnek, J. R. et al. (2010)

Describe key health indicators Quantify health conditions Work conditions ass. ć health conditions & indicators

C/S survey including some items from: BRFSS & LEADS

Cross sectional design Non-respondent bias Healthy worker bias Recall bias

Return rate of 52% (n = 30560) nationally recertified EMTs and paramedics in the U.S.A. Those responses with missing data were removed leaving a RR of 34% (n = 19960)

25.8% were obese 23.5% reported at least one health condition 75.3% did not meet CDC recommendations for physical activity 17% smoked Age, BMI & fitness were ass. ć pre-existing health conditions

Weaver, MD. et al. (2015b)

Examine shift length & its impact on occupational injury & illness

Analysis of administrative data from14 EMS agencies in the U.S.A. with 37 sites: Exposure interest = shift length Outcome interest = occupational injury and illness

Observational study Results not representative of agencies ć < 100 employees Workload of shift unknown Psychosocial norms not known No standard of recording between agencies Confounding variables (age, sex, sleep, health status are unknown

Shift lengths& injury and illness reports were excluded if the task was non-clinical. 966082 work shifts and 950 injury or illness reports from 4382 employees were analysed

Injury rate was lower for shifts <8 hours Shifts >16 & ≤ 24 hours had an injury rate 60% relative to shifts > 8 & ≤ 24 hours. Overall – as shift length increase so does the occupational injury and illness rate.

Patterson, PD Et al. (2016)

Examine the relationship between crew familiarity & work-related injuries

Retrospective cohort design of administrative records to determine crew familiarity and work-related injuries

Observational study Shift workload unknown Confounding variables (age, sex, sleep, health status are unknown

715826 shift & 803 injury records from 14 EMS agencies with 37 bases and a total of 4446 EMS employees.

Less familiarity between crews is ass. ć ↑ incidence of workplace related injury: 1 shift – IR = 33.5/100 FTEs 2-3 shifts – IR = 14.2/100 FTE 4-9 shifts – IR = 8.3/100 FTE ≥ 10 shifts – IR = 0.3/100 FTE

414

Appendices

Citation Aim Method Limitations Population/Sample Outcome Weaver, MD. Et al. (2015a)

Determine if weekly work hours is associated occupational injuries.

Analysis of administrative data from14 EMS agencies in the U.S.A. with 37 sites: Exposure interest = weekly work hours Outcome interest = occupational injury and illness

Observational study Secondary analysis of data Other hours worked are not captured OSHA 300 logs are known to underestimate injury rates between 20-70% Confounding variables (age, sex, sleep, health status are unknown

Weekly hours & injury and illness reports were excluded if the task was non-clinical. 966082 work shifts and 950 (using the OSHA form 3ooA log of work-related injuries) reports from 4382 employees were analysed

Weekly work hours were not associated with occupational injury

Crill, M. T. Hostler, D. (2005)

Examine EMT/paramedic strength & flexibility & its relationship to lifestyle and reports of back pain.

C/S Survey of researchers own design primarily to ascertain previous back injury and known risks, lifestyle & health. Assessment included height, weight, BMI, hamstring flexibility & back extension time

Findings may not be able to be generalised to EMS population as participants were diverse. Extension test not designed as a research tool Back strength test does not provide useful info. Re muscle strength or endurance

90 EMS providers attending an EMS conference in Pennsylvania U.S.A. EMS primary occupation 63.7% of time. Volunteers 16.3% Firefighter/EMTs 14.8%

49% had a back injury in the last 6 months – only 39% of these injuries occurred in EMS duties. 52% reported their injury interfered with daily activities. No participants met the CDC guidelines for exercise All participants were taking at least one prescription medication daily Significant rates of obesity reported (actual rate not specified)

Reichard, A. A. Et al (2017)

Identify the characteristics of EMS workers who had treatment in EDs for occupational injuries, illnesses & exposures

EMS workers were identified through the (NEISS-Work) system and a follow back survey by telephone was conducted after informed consent was obtained via letter. Survey developed by a panel of experts, pilot tested, revised &assessed again. Delivered by trained operators using a computer assisted interview technique

Only looked at EMS workers treated in EDs may not be representative Time lapse between injury and call back may have introduced recall bias.

n =572 EMS workers treated in hospital EDs from July 2010 to June 2014 – this equated to a 74% RR amongst EMS workers who were identified and successfully contacted

Injury rate of 8.6/100 FTE > 50% had less than 10 years in service Sprains & strains 40% n=24000 had body motion injuries, (lifting, carrying, transferring a patient or equipment) n = 24400 were exposed to harmful substances followed by & in order: Slips, trips & falls = 14400 Motor vehicle incidents = 7400 Occupational violence = 6400

415


Citation Aim Method Limitations Population/Sample Outcome Wiitavaara, B. et al. (2007)

Explore illness and wellness amongst ambulance personnel with MSK symptoms

A grounded theory approach - each interview started broadly and evolved to explore interesting themes. Interviewees were asked to narrate their story and reflect on their understanding of concepts related to health and wellness

Small sample size All men working as ambulance personnel in one ambulance station in Sweden n = 10 based on varied MSK symptoms, age and years of employment

A model characterised by striking a balance through: Wellness through development Accepting & handling illness Encountering illness as an experience and a threat

Airila, A. et al. (2012)

Is work engagement ass. ć work ability after adjusting for age, lifestyle & work-related factors.

C/S study including: WAI questionnaire* Lifestyle questions The Basic Nordic Sleep Questionnaire* Working conditions including workload, job demands, supervisory relations & task resources Utrecht Work Engagement Scale (UWES-9)*

C/S design Self-reported measures Not applicable to other emergency workers

Male Swedish firefighters who had responded to previous survey in 1999. RR = 73% (n = 408).

Work engagement is important in determining work ability amongst other factors which included good sleep, frequent exercise, not to high physical workload & job demands and good task resourcing.

Punakallio, A. et al. (2016)

Describe changes in aerobic capacity at 3 and 13 year follow ups & review the lifestyle factors predicting these changes

Selected by stratified sampling Longitudinal study in 1996, 1999 &2009 Questionnaire – lifestyle, exercise & experience in the fire service Measurement of Vo2 max FPE work ability

High dropout rate especially of older firefighters Self-reported data for questionnaire

Finnish male firefighters Questionnaire: 1996 – 76%, 1999 - 72%, 2009 – 68% Physical Capacity & health: 1996 – 89%, 1999 – 65%, 2009 – 37%. FPE: 1996 – 88%, 1999 – 50%, 2009 – 22%

Annual decline in aerobic capacity in absolute and relative terms was same as GP but in older firefighters aerobic capacity was less than that needed for the job Smoking and > 15 units of alcohol per week were risk factors for decline in aerobic capacity Exercising 4-5 time per week were the best protective factors

Jenkins, N. et al. (2016)

To identify factors that contribute to risk of MSK injury in paramedics

Systematic review using search terms: MSK, workplace, injury, industrial accident, pre-employment physical capacity testing, paramedic, emergency service employee, firefighter & police

N/A Exclusions – did not mention pre-employment physical capacity testing, MSK injuries or not published in English.

30 articles included

Physical fitness, age, gender, equipment and demographic variables e.g. work location key factors in paramedic injuries Little evidence to quantify the relationship between pre-employment physical capacity testing and subsequent injuries in paramedics.

416

Appendices

Citation Aim Method Limitations Population/Sample Outcome Mirhaghi, A. et al. (2016)

Identify a personality type that makes an individual suitable for paramedic practice

Systematic literature review using search terms: Personality, trait, rescue, emergency, medical, services, personnel, paramedic and technician using a PRISMA approach

N/A 27 articles were included – 7 U.S.A., 6 Australia, 2 Germany, 2 Slovakia, 1 South Africa, Austria, Canada, Sweden, Hungary, Iran, Norway, Romania, China & U.K. with a total of 9721 participant paramedics

Paramedics scored low for neuroticism EMDs scored high for neuroticism Paramedics scored low for extraversion EMP scored low on openness EMPs scored high for conscientiousness EMPs scored low for agreeableness No clear personality trait was identified for paramedics

Hegg-Doyle, S. et al. (2015)

Identify the impact of work stress on paramedics

Systematic review of the literature - effects of paramedic jobs on health status using the following terms: paramedics, emergency responders, emergency workers, shift workers, post-traumatic symptoms, obesity, stress, heart rate variability, physiological response, BP, CV and cortisol

N/A 42 articles identified – 17 excluded because – not paramedics, no occupational exposure, no physical assessment, death at work – representing 14845 paramedics and one systematic review

Paramedics develop risk factors: Acute & chronic stress ≈ CVD PTSD Other issues include: Sleep problems Obesity No methods are used to monitor the health or CRF of paramedics

Shariat, A. et al (2015)

Identify the side effects of abnormal rhythm of sleeping associated with shift work.

Systematic review using key words: shift work, biological rhythm of sleeping, diseases, physiological issues, physical issues and field study.

Acceptance of papers written in English may have excluded other relevant articles and databases

700 studies identified & 10 finally evaluated. Exclusions – no lit. reviews. Non-longitudinal studies, no clear methodology or results

Consistent issues found include: Fragmented daytime sleep, ↑ risk of CV, gastrointestinal, haematological & immunological disorders, metabolic syndrome, and diabetes and obesity Causal links are described as reduced sleep, altered diet and ↓ physical activity

Orellana , RC. et al. (2016)

Identify the nasal carriage prevalence of MRSA in EMS personnel and the ass. Risk factors

C/S survey including demographics, occupational Hx, health status, hygiene practices & cohabitant characteristics. Swab for MRSA in the anterior nares.

Only tested for nasal MRSA C/S design limits causality & whether MRSA was transient or persistent Small sample size

280 EMS personnel from 84 agencies across Ohio, U.S.A. using probability sampling

4.6% with MRSA (13/280) Risk factors – infrequent hand hygiene, living with someone with a recent staphylococcal infection, poor Hx of hand washing after glove use and EMS workers with open wounds or a recent infection

417


Citation Aim Method Limitations Population/Sample Outcome Dropkin, J. et al. (2015)

For EMS workers identify: Work related health problems Risk factors Prevention strategies Examine responses from workers & supervisors

Qualitative research using grounded theory: In depth interviews Focus groups

Small sample size Self-reported Misclassification of health problems from respondents Information bias due to selective memory or team perceptions Selection bias (older participants)

Drawn from 319 amb. from a hospital based amb. Service in NE U.S.A. (convenience sample) 10 teams – 1* paramedic & EMT (n=20) Focus groups = workers (n=68) and supervisors (n = 22) – randomly assigned to make up 40

MSK injures ass. With patient handling Organisational and psychosocial factors such fitness, wages, breaks and shift scheduling ass. with injuries Lack of trust between workers & supervisors identified. Preemployment screening identified as a way of reducing injuries

418

Appendices

Citation Aim Method Limitations Population/Sample Outcome Maguire, BJ. (2011)

Review data from the US Department of Labour (DOL) Bureau of Labour Statistics to determine transport related risks for EMS personnel

Review data, rates, relative risks & proportions ass. with EMS injury and fatality transport related accidents. This data does not include patient injuries or fatalities

No data available on workload, driver training, policies, seatbelt use, vehicle speed and type, lights and sirens use, call type

1050 injury cases and 30 fatalities in the U.S.A between 2006-8.

Transport related injury 5 times higher than the national average. Females 53% yet only 27% of workforce. 20% resulted in 31 or more lost work days.

Roberts, H.M. et al.

(2015)

Investigate risk of lower back & upper limb MSK injury& mental injury in paramedics & compare that with social, welfare & nurse professionals

Examine the Compensation Research database in Victoria, Aus. For info. on claimants, demography, industry, occupation, employer, workplace, injury, claim costs and payment summaries.

WC claims likely underestimate the prevalence of work related conditions

WC claims to the Victorian Workers Authority 1/7/2003 – 30/06/2012 – 214355 claims amongst the 6 occupational groups studied.

Paramedic risk of lower back MSC injury and mental injury was 13 time higher than nurses Paramedics had the highest claim rate of 100/1000FTE which was 5 times higher than that of other occupational groups. MSK injures account for approx. half of all claims. Paramedics had an increasing claim rate over the study period against other occupational f groups where there was a decline

Broniecki, M. et al 2012

Examine whether working conditions, physical & psychological factors were ass. ć injuries & claims

On-line C/S survey using a self-developed tool with questions from other validated tools: Uncontrolled environment Exposure to violence Keeping up with clinical competency

C/S study Low response rate Casual factors raised but study type unable to substantiate

RR = 27.5% (n = 243) from an Australian Ambulance Service.

No SSA between recent manual handling training & physical exercise & injury rate. High levels of physical work were SSA related to injury claim. Those who had adequate breaks were SSA < likely to sustain a back, shoulder or neck MSC injury. Casual factors raised include the uncontrolled environment & non-adherence to manual handling techniques.

Hertz, RP. (2004)

Explore the relationship between obesity, CV risk factors and work limitations

Analysis of clinical data from National Health & Nutrition Examination Survey (NHANES) III in the USA 1999-2000

N/A 2381 participants from the 6060NAHNES 111 i.e. employed, had a measurable BMI & > 20 years of age.

Obese workers have the highest prevalence of work limitations as against normal weight workers (6.9% vs 3.0%), hypertension (35.2% vs 8.6%), high cholesterol (36.4% vs. 22.1%), T2DM (11.9% vs 3.2%) & metabolic syndrome %3.6% vs. 5.75%). CVD risk profile & work limitations of obese workers represents that of non-obese workers 20 year older.

419


Citation Aim Method Limitations Population/Sample Outcome Broniecki, M. et al 2010

Review studies investigating the prevalence and determinants of MSK disorders in ambulance officers & their limitations

Systematic review using search terms: ambulance personnel, officer & worker, paramedic, EMT, EMS personnel, MSK disorder, injury & disease

N/A MSK disorders- 2 Risk factors for MSK disorders - 2

High prevalence of back, neck & shoulder MSK disorders in paramedics and significant association between individual, physical & psychosocial demands and MSK disorders of the lower back, neck and shoulders of paramedics.

El ahrache , K. Imbeau, D. (2009)

Examines the application of rest allowance models on Static muscular work in manufacturing work stations using 4 models

Work stations selected based on static exertions & postures, no. of workers assigned to stations & MSK symptoms described by most workers. Two workers at each workstation were videoed.

Static rest models Non-ambulance

7 workstations in automotive, printing and plant industries

Rest allowances essential for reducing fatigue level. Information on which model may suit is not available Shoulders in this static model required more rest than other muscle groups.

Maguire, BJ. Smith, S. (2013)

Determine the rates of fatal and non-fatal injuries amongst paramedics in the U.S.A.

Retrospective cohort study using pre-existing fatality and injury data for the U.S. department of Labour –

Exposure to work rates not known Type of work not know Missing data as some paramedics might be classified as firefighters

Injuries that resulted in at least one day of lost time and fatalities from 2003 – 2007. 21690 injuries & 59 fatalities

Sprains & strains most common injury Back 43% ć patient listed as cause in 37% of cases. 55% of assaults were on females yet females were only 27% of workforce Most common events were: overexertion 56%, falls 10%, transport related 9% 59 fatalities – 86% transport,

Tullar, JM. (2010)

Review health and safety interventions to determine their effect on MSK health status

Systematic review using OHS intervention terms, health care setting/worker terms & MSK health outcome terms. These are defined in Table 1 of the article and are too numerous to include here.

Level of evidence found was ‘moderate’

15 of 2918 from the original search. Inclusion criteria were based on a set of questions for quality (19), data extraction items (43) and final selection based on the rating of the evidence - high, and medium-high.

Patient handling training alone & cognitive behaviour training have no effect on MSK health Training alone is not effective Exercise provides positive health benefits Multi component patient handling interventions have a moderate level of evidence in regards a positive impact A moderate level of evidence exists for the use of lifting equipment

420

Appendices

Citation Aim Method Limitations Population/Sample Outcome Maguire, BJ. et al. (2005)

Describe the injury rate of EMS personnel and compare this with other occupational groups

Retrospective examination of basic demographic and injury data.

Data sets differed in format and some content. Small subset of all EMS employees in the U.S.A. Injury rate may be under reported. Hours worked, and type of work not reported

3 data sets from two anonymous EMS agencies in the U.S.A. (representing 409 FTE EMS workers). EMS 1: Jan 1998 – May 2000 &Oct 2001- July 2004. EMS 2Jan 1999 – Dec 2001. 476 cases were analysed. This EMS agency data was used because of convenience and willingness to participate.

Injury rate 34.6/100 FTE Sprains, strains and tears leading category of injury Backs the most injured body part 57% of cases resulted in lost days Injury rates for EMS workers higher than any other industry in the U.S.A.

Hansen, CD. Et al. (2012)

Compare the health status of amb. Personnel against that of the GP. Describe the work environment & compare against other occupations Examine ass. between physical & psychosocial work env. & 5 health outcomes.

Data taken from Cohort study called Men, Accidents, Risk & safety) MARS using: Demographics quest. 1 General health quest. SF36. 1 item – Nordic Sleep Quest. 10 items – Dutch MSK quest. Short ver. Of Copenhagen Psychosocial Quest.*

c/S design Underestimate of exposures in the work env. due to policy of transferring lower work ability personnel to other jobs.

RR of 43.5% (n = 1691) employees from the largest ambulance company in Denmark (3888 employees) Reference population (n = 14241) using the same health measures as the amb. study

Amb. Personnel had: Half the levels of self-reported poor health (5 vs.10%) Same levels for mental health as GP Higher levels of emotional demands, meaningfulness of & commitment to work. Lower levels of quantitative demands & influence at work Emotional demands ass. ć poorer mental health &sleep quality Performing short, maximal force exertions ↑ the odds of pain in the neck, arm, shoulder & lower back.

Studnek, JR. et al. (2007)

Analyses the association between back problems & individual & work characteristics of EMTs.

Case control analysis of EMTs from the Longitudinal Emergency Medical Technicians Attributes & Demographics Study (LEADS) which is an annual data collection of EMTs in the U.S.A.

Returns on a year-to-year basis are low from LEADS. Possible non-response bias Misclassification of variables from self-reported data.

Practicing EMTs with back problems (n = 104) in 2003 and 2004 against controls without back problems (n = 475) in 2003

Dissatisfaction with current assignment were sig. more likely to report back problems and fair to good fitness as compared to those with excellent fitness.

421


Citation Aim Method Limitations Population/Sample Outcome Sterud, JR. et al. (2006)

Explore health problems, work related & individual health predictors in ambulance personnel

Systematic review including terms: ambulance personnel, worker and men, paramedics, EMTs, EMS personnel with mental health, stress, stress disorders, PTSD, psychological stress, depression anxiety, burnout, physical health & health status

May have missed important articles Reviews came from Western countries and can’t generalise results to other parts of the world.

(n =49)/573 based on inclusion criteria: Original study published in a peer-reviewed journal Published after 1996. USA (21), UK (12), Sweden (4), Netherlands (3), Canada ((3), Aus (2), France (1) Germany (1), NZ (1), Japan (1).

Ambulance workers have: ↑ standardised mortality rate ↑ level of fatal accidents ↑ level of accident injuries ↑ level of early retirement on medical grounds ↑ level of MSK problems – than the GWP

Gore, M. et al. Find the comorbidity prevalence with patients with chronic lower back pain & to evaluate pain related treatment patients & costs ass. with those pts.

Data obtained from a data base covering 98 commercially managed health care plans covering 62 million individuals and more than 4 billion claims. All pts with 2 or more claims ass. ć CLBP during 2007 and 8 with each diagnosis at least 90 days apart

Errors in coding & recording Comorbidities may have been overestimated as they were estimated on one episode of CLBP

(n = 101294) with CLBP and the 1:1 control group matched for age, sex and locality

Those with CLBP have a significantly higher comorbidity burden.\ Frequency of MSK pain (13.0% vs. 6.1%) Depression (8.0% vs. 3.4%) Sleep disorders (10.0% vs. 3.4%) Pain related meds (37.0% vs 14.8%)NSAID 26.2% vs. 9.6%) Tramadol (8.2% vs 1.2%) Health care costs ($8386±$17507 vs. $3607±$10845)

Murray, J. (2013)

Promote mental health and well-being amongst paramedics, EMDs and students.

Part A – basic strategies & theoretical concepts relevant to health & well-being of ambulance personnel & directs the reader to particular readings & exercises as part of the development process. Part B – collection of relevant real-life experiences to clarify, bring relevance to &consolidate key learning concepts.

One part of an educational package.

All paramedics, EMDs and paramedic students in the QAS.

Chap 1 – Oh, I couldn’t do your job Chap 2 – Stress: the good, the bad and that’s life Chap3 – Trauma: shaken to the foundations Chap 4 – Rebuilding foundations: building resilience Chap 5 – The witnessing of grief Chap 6 – Ambulance stories

Blau, G. Chapman, S. (2011)

Examine important items in relation to leaving EMS & relationship to life satisfaction after leaving

C/S survey – annual from 1999 – 2008 including: Where working now? Reasons for leaving (17

items Life satisfaction post

leaving

Large number of not applicable responses ↓ number of reasons for leaving to 9. Memories may be distorted between leaving and doing the survey

478 respondents initially but only 127/1036 (12%) full time paid EMTs in the U.S.A. were included because: only 244 had worked in EMS & of those 45 were part time & 72 were volunteers

Stress/burnout of highest importance for leaving Desire for better pay & conditions had the lowest importance Desire for career change +vly related to life satisfaction post-leaving & -vly related to returning to EMS

422

Appendices

Citation Aim Method Limitations Population/Sample Outcome Kirby, R. et al. (2011)

Explore coping strategies related to +ve &-ve post trauma outcomes

C/S Survey using following tools: Post Traumatic Growth Inventory* The Impact of Events Scale-Revised* Revised-COPE Inventory*

Other factors contribute to trauma outcomes e.g. personality & length of time since trauma Self-reported measures Generalisation not possible as those ć maladaptive coping strategies usually leave employment

125 operational QLD paramedics including new recruits (n = 26) 22%), paramedics > 4 years experience (n = 33) 28%) paramedic peer support officers (n = 59) 50%)

Adaptive coping strategies: (Self Help - support-seeking, expressing & understanding emotion (Accommodation – Optimism, Acceptance, positive reframing, restraint; & Approach – Active coping & planning) ass. ć +ve changes post trauma. Maladaptive coping strategies (Avoidance – disengagement, denial, blame; & Self-Punishment- self-blame, rumination, suppression) linked to ↑ risk of -ve changes post trauma

Rybojad, B. et al. (2016)

Asses the influence of sociodemographic & occupational factors on PTSD amongst paramedics Suggest preventative strategies

Survey: Demographics Polish version of the Impacts of Events Scale-Revised (IES-R)*

Small population Not all factors that may affect PTSD were measured

100 Polish paramedics – it was not specified how they were selected

PTSD 40% (Females 64.3% vs. males 36.1%) & more so for contract than self-employed paramedics. PTSD was less frequent in those with higher education.

Bennett, P. et al. (2005)

Examine the prevalence & predictors (personal and work factors) of PTSD, anxiety and depression

C/S Survey including: Ambulance Work Stress Questionnaire (AWSQ)* Hospital Anxiety & Depression Scale (HADS)* Post traumatic Diagnostic Scale (PDS)* Cognitive Appraisal Scale(CAQ)*

Cross sectional design & therefore causes, and the direction of the relationship was unable to be determined.

Some respondents (depressed/anxious) may inflate associations between variables

(n = 617) 65% - EMT & Paramedics in a large U.K. based ambulance service serving 3 million people in rural and urban settings.

Troubling memories – 50% PTSD 22% (males 23% Females 15%) Anxiety 23% Depression 9% Key predictors of severity were: Organisational stress, frequency of traumatic events, length of service and disassociation in relation to an incident

Bennett, P. et al. (2004)

Identify the prevalence of PTSD and other emotional disorders among emergency personnel

C/S survey including; The presence of intrusive past or present work-related memories for at least one month completed the: (PDS)* All respondents completed the Hospital Anxiety & Depression Scale (HADS)*

Cross sectional design Self-reported data Low return rate for the PDS completion. Possible response bias - PTSD avoided completing the survey. Those with no problems saw no value in completing survey

All Paramedics & EMTs in one UK NHS Trust ambulance service. 1029 were sent a questionnaire. A return rate of 60% (n = 617) was obtained. Those who reported past or present work related intrusive memories were asked to complete the PDS (n = 293) 47%.

PTSD 22% (Females 15% vs. males 23%) Depression 10% Anxiety 22% Troubling memories in the present 56%

423


Citation Aim Method Limitations Population/Sample Outcome Van der Ploeg, E. Kleber, RJ. (2003)

Determine factors predictive of: Post-traumatic Stress,

fatigue & burnout due to acute and chronic work stressors

Longitudinal survey over 12 months T1 - initial & T2 - at one year Using: Demographic questions Acute & chronic ambulance stressor questions - Table…. QEAW) Self-reported health - Dutch - ‘Impact of Events Scale’* Fatigue - the ‘Checklist Individual Strength’ (CIS)* Maslach Burnout Inventory*

T2 response rate is 39% less than the response rate at T1

T1 – 221/1393 (16%) & T2 156/221 (71%) – including paramedics & drivers from 10 regional amb. services in the Netherlands. These services were randomly drawn from 80 ambulance services.

Chronic work-related stressors were higher than found for a reference group (other health care workers in Holland) 10% had a clinical level of post-traumatic stress, were10% were fatigued to a level where they were at risk of sick leave and work disability & suffered burnout Health symptoms were predictors by lack of support from a supervisor and poor communication.

Regehr, C. et al. (2002)

Examine the relationship between traumatic stress symptoms and functional disability including work leave, PTSS, social support & personality.

C/S survey including: Demographics, exposure to critical incidents & mental health stress (MHS) leave Beck Depression Inventory* PTSD - (IES)* Personality factors influencing chronic PTSD – (BORRTI)* (SPS)* Self-rated support level from family, friends, union and employers – scale of 0 to 5

Cross sectional design Sample size and type (convenience) Not all elements of PTSD were surveyed

(n = 86) 11% - paramedics from the Toronto ambulance service employing 800 paramedics

29.1% report high range PTSS Those who had taken MHS leave in the past were more likely to report PTSS in the high to severe range. Personality style was the strongest predictor of those who took MHS leave and in particular characterised by suspiciousness, hostility & isolation with a tendency toward demanding, controlling and manipulative behaviour in relationships.

Soh, M. et al. (2016)

Investigate different aspects of well-being: Work engagement Job satisfaction job stress And predictors including personality and perceived organisational support.

C/S survey including: Eisenberger Survey of Organisational Support* Patchen’s Perceived Supervisor Support PSS)* Utretch Work Engagement Scale* Job satisfaction – 9 single item specific job aspects Personality – (SIMP)*

Cross sectional design Lack of comparison studies

Ambulance service staff in the U.K (n=490) selected. Response rate determined.

Previous well-being models based on treating -ve symptoms of illness &PTSD Improved well-being model should include +ve aspects such as: job-satisfaction, stress & engagement i.e. what is it about those who see these aspects in a +ve light that can be transferrable to others. Well-being predicted by emotional stability & perceived organisational support

Ul-Haq, Z. Mackay, d.f. Fenwick, E. Pell, JP. (2013)

Systematic review of obesity ass. ć health related QoL & differing relationship ć mental & physical QoL

Systematic review – only those studies that use the SF-36* QoL survey & reported the overall physical & mental component score.

All cross sectional studies Individual data not available and no adjustment for confounders

(n = 8) studies incl. 43086 participants from the U.S.A., Australia, Germany, Canada, Sweden and U.K, populations

↑BMI was ass. ć ↓ physical QoL Class III obese and overweight individuals were ass. ć ↓ mental QoL Obesity + chronic conditions ass. ć deteriorating physical & emotional QoL

424

Appendices

Citation Aim Method Limitations Population/Sample Outcome Heraclides, AM. et al. (2012)

Examine interaction between work stress & obesity in relation to T2DM in a gender specific manner

Longitudinal study 1985 -2009: Clinical examination and Psychosocial stress – JSQ*

Not noted Phase 1(1985) (n = 10308) to Phase 9 (2009) (n = 6755) employees from 20 U.K. civil service departments

Work stress (high job demands/low job control) ass. ć T2DM in females and a higher risk of T2DM in obese females.

Foss, B. Dyrstad, SM. (2011)

Examine how stress and obesity might be linked & discuss the cause/consequence relationship between the stress response and obesity.

Scientific review N/A N/A Stress as a cause of obesity is well established. Stress as a consequence of obesity is a possibility

Scully, PJ. (2011)

Propose a model of care for emergency services personnel

Outline of an already established program

N/A QAS) peer support program – development, selection, training & operations

Supports the current model of care in the QAS

Regehr, C. (2005)

Explore the experiences of spouses of paramedics and impact of trauma exposure on the spouses of paramedics.

Qualitative study: Semi-structured interviews and included questions on: Family situation Effects of shift work The paramedic role Impact of traumatic events

on the family Social supports and

challenges Strategies for managing

Not able to be generalised for other professions

14 spouses of paramedics from 2 services in Toronto, Canada

Aftermath of traumatic events often can be transferred to the family. The family of paramedics is not included in the support mechanisms offered to paramedics

van Rossum, EFC. (2017)

Examine the hypothesis - continuous loop may exist between obesity, an unhealthy lifestyle, and increased cortisol, which maintains or worsens obesity and may counteract weight loss

Scientific review N/A N/A Long term cortisol levels are higher in obesity however not all obese patients have elevated hair cortisol.

425


Citation Aim Method Limitations Population/Sample Outcome Backé, EM. Et al. (2009)

How often emergency & non-urgent amb. Operations result in a cortisol increase. Examine diurnal cortisol on days with different work tasks in the same personnel Examine strong acute stress in relation to physiological response Measure HR in parallel to cortisol

Mobile Intensive Care Amb. Officers MICA) monitored via Salivary cortisol (4 times/day, at the time the work call was received, after loading the patient, at the time of delivery of the patient, & 30min after delivery) and heart rate (continuous) on consecutive days. Day 1 doing emergency work and day 2 doing non-urgent transport. Subjects rated their physical & emotional strain on a 9-point scale. Two members were in each team & were rated against each other.

Not all salivary cortisol was reliably collected, especially in the mornings Questionnaires concerning situations that may affect stress response need to be included A detailed analysis of the workplace also needs to be undertaken

Mobile Intensive Care Ambulance Officer2 in an urban German ambulance station (n=24). RR not able to be determined.

Cortisol levels rose higher in the morning of emergency work than in the morning of non-urgent work. Stress is not perceived in work situations characterised by routines HR distinguished better than cortisol between operations in emergency or non-urgent transport There were only a few situations with strong endocrine reaction in emergency and non-urgent work – MICA officers were thought to be more conscious about caring for patients rather than concern for their own sensitivities. Differences in endocrine reaction were experienced in teams often with the Driver having increased endocrine reactions

Weibel, L. et al. (2003)

Establish diurnal salivary cortisol levels in a medical dispatch centre. Objectify the belief that EMDs work under high stress conditions.

EMDs sampled every 2/24 from 0900 to 1900 hours during day work. Control subjects matched for age, gender & smoking status and sampled at similar times to EMDs during their leisure time Confounders were matched & all subjects had a medical examination & no medication. Subjective stress perceptions & attitude to work.

Small sample size Results have not been verified by other studies Causes unable to be established Work situations that have the most risk are unknown

8 French EMDs 8 French Laboratory staff

Cortisol markers higher for EMDs on day shift than matched sample during leisure time Among EMDs subjective perception of emotional stress was positively correlated with cortisol concentrations Negative perceptions of the physical work environment were positively correlated to the feeling of poor relationships with hierarchy

426

Appendices

Citation Aim Method Limitations Population/Sample Outcome Brough, P. (2005)

Compare experiences of organisational & operational work stressors, work-family conflict, neuroticism, job satisfaction, work-related psychological well-being in Police, fire and ambulance populations

C/S survey including: Minor work demands – PDHS* Work-family conflict – Warr’s (1990) 4 item measure of negative job carry over* Neuroticism – Eysenck et al. (1985) Neuroticism Scale* Job satisfaction – Warr et al. (1979) Job Satisfaction Scale* Work well-being – Warr’s (1990) twelve item scale*

Comparison of Police, Fire & Ambulance may not be predictive of results as Amb. Officers have higher education levels & diversity, which are thought to be two mitigating factors for higher well-being

Voluntary participants from Police (n = 229) 46%, Fire (n = 241) 48% & Ambulance (n=253) 51% in N.Z. Overall response rate was 48% EMDs approx. 10% of 253 amb. respondents

Amb. had higher levels of psychological well-being than Police and Fire Fighters Well-being significantly predicted by work-family conflict, neuroticism, & job satisfaction

Arial, M. et al. (2011)

Investigate the respective influence of work characteristics, the effort–reward ratio, and over commitment on the poor mental health of out-of-hospital care providers.

C/S survey including: GHQ-12* Effort Reward Imbalance

Questionnaire* Demographics Work Characteristics Health-related info. Work Practice Analysis 416 hours of observation

in 11 amb. services

Cross sectional design Direction of effect unclear Self- reported data Voluntary & self-selection bias may be present

374/669 (56%) ambulance services personnel from 27 EMS services in French speaking Switzerland. Forty-one were excluded because they did ambulance work only for a small part of their working time, leaving n = 333 (50%).

High perceived effort ass ć low perceived reward is ass. ć poor mental health Low perceived self-esteem ass. ć poor mental health Over-committed females more strongly ass. ć poor mental health Interhospital transfers ass. ć poor mental health Potential psychiatric disorders between 15-20%

Donnelly, EA. et al. (2016)

Examine relationship between amb. stressors & PTSD Determine preferred source of support

C/S Survey using following tools: PTSD Checklist (PCL)* EMS Chronic Stress Scales* Critical Incident Stress Inventory*

Unable to determine if respondents were representative of the amb. service Used a convenience sample

(n = 162) (60%) Canadian paramedics from one county. Nine declined & 8 not included (< 85% of survey completed) Final response rate (n = 145) (54%)

Chronic & CIS sig. ass. ć PTSS Holistic health initiatives mitigate impact of PTSS Interventions ↑ ć peer, family & friend support

Sterud, T. et al. (2008b)

Determine if: Amb stressors are inherently stressful

C/S Survey: conducted ć JSS – general org stress* (NASS)* Population density Basic Character Inventory – personality*

C/S design Response rate low Did not consider nesting of personnel at stations

1180/3200 (37%) -paramedics & managers who described operational amb. work to be > 50% of working time from 19 amb. Regions in Norway

Amb. specific stressors (see Table …) more severe & occur ↑ ʄ than org. stressors More dense population ass. ć increased stressors. Lack of support was ass. ć neuroticism and greater in younger personnel with higher ʄ of stress exposure.

427


Citation Aim Method Limitations Population/Sample Outcome Sterud, T. et al. (2011)

Assess: Importance of amb. & org stressors Individual characteristics related to job satisfaction & health complaints (emotional exhaustion, psychological distress, & MSK pain)

C/S survey conducted ć: Survey tools as in Sterud

(2008b)

Response rate low – 75% less than Sterud (2008b)

Pop. from Sterud (2008b) were offered a follow up survey one year later. 298 (25%) responded.

Lower job satisfaction predicted by ʄ of lack of leader support& severity of challenging job tasks. Emotional exhaustion predicted by neuroticism, ʄ of lack of leader support, time pressure, & physical demands. Psychological distress predicted by neuroticism and lack of co-worker support MSK pain was predicted by ↑ age, neuroticism, lack of co-worker support and severity of physical demands.

Donnelly, E. Siebert, D. (2009)

Systematically examine gaps in the literature and develop a model to serve as a future basis for intervention and research

Systematic review using combinations of the following search terms: (EMT), (EMS), paramedic, paramedical, stress, personnel, first responder, stressor, occupation, injury, occupational hazard, trauma occupational risk, PTSD, morbidity, mortality, assault, alcohol, alcoholism, drug abuse, chemical dependence, measurement, measures, evaluation, & outcome.

Based on Pearlin’s Stress Process Model* & has been adapted to include elements that are regarded as important in EMS e.g. “life events” has been replaced by “critical incident stress”.

Literature relevant to search terms and EMS

80-100% exposure to traumatic events Prevalence of PTSD > 20% High risk drug & alcohol abuse as high as 40% Occupational related stress linked to PTSD and high-risk alcohol and drug use Chronic stressors, insufficient salary, alienated & unsupportive administration, lack of support or conflict with colleagues & interference with non-work-related activities described as being enhanced by shift work

Coxon, A. et al. (2016)

Identify key stressors and their impact on staff well-being of EMDs

Semi-structured interviews – questions developed with the assistance of the Emergency Operations Manager– analysed using an inductive bottom up thematic analysis

Small sample size Potential bias - questions being developed ć manager The opinions are only one NHS operations centres & may not be representative

Purposeful sampling to reflect age, years of experience and breadth of opinion (n = 9) 25% of EMDs in the south of England, U.K. ambulance emergency operations centre

EMDs feel undervalued and overloaded Greater support needed at work to reduce stress and the likelihood of sickness absence. Better and ↑education to build on existing coping strategies

Shakespeare-Finch, Jane Rees, A. Armstrong, D. (2015)

Assess the impact of self-efficacy, and giving and receiving social support on psychological well-being, posttraumatic growth (PTG), and symptoms of PTSD.

C/S including; Stage 1 – traumatic incident? – if yes → Stage 2 C/S ć: Social support - (NGSE)* 2-way (SSS)* Well-being- (PWBS)* PTSD - (IES-R)* Posttraumatic Growth

(PTGI)*

Small sample size\ Cross sectional design Self-reported data may be distorted due to memory.

QAS EMDs Stage 1: (n = 60) 50% Stage 2: (n = 44) 73%

Social support sig. +ve predictor of well-being. & Post Traumatic Growth (PTG), & sig. -ve predictor of PTSD. Self-efficacy was found to sig. +ve predictor of well-being. Shift-work sig. -ve predict of PTSD

428

Appendices

Citation Aim Method Limitations Population/Sample Outcome Jimmieson, NL. Tucker, MK. Walsh, AJ. (2016)

Develop a more comprehensive understanding of the joint influence of multiple stressors being experienced simultaneously by employees

C/S Survey ć: Psychological strain –GHQ* Burnout - Physical Fatigue Scale* Other items were taken from studies on: time, cognitive &emotional demand, stress remedial intentions & job satisfaction

Response rates from high workload employees were low Cross sectional design

Sample 1: (n = 125) 32% of Regional Hospital & Healthcare Service employees. Sample 2: (n = 93) 35% of Ambulance service employees

High levels of multiple demands exacerbated stress. Reducing the levels of one demand neutralised the effects of the other two demands on stress

Kukowski, C. King, DB. DeLongis, A. (2016)

Examine the moderating role of burnout in the association between post-traumatic stress and sleep quality i.e. high levels of burnout would exacerbate this relationship

C/S Survey including: Maslach Burnout Inventory (MBI)* PTSD Checklist Civilian Version* Pittsburgh Quality of Sleep Index*

Cross sectional design Response rate not reported Self-reported data Source of stress relationship with PTS not determined

87 full time paramedics in Canadian Metropolitan areas recruited via online advertising, internal organisational notifications and word-of-mouth

PTSD ass. ć lower than average sleep quality ↑ burnout exacerbated the effect of PTSD on sleep

Sterud, T. et al. (2008)

Estimate the prevalence of anxiety & depression symptoms & compare to the GP. Investigate somatic complaints and their ass. ć anxiety & depression Investigate the relationship between health complaints & the type of professional help sought

C/S Survey including: HADS* Karolinska Sleep

Questionnaire* Subjective Health

Complaint Questionnaire (SHC)*(Shakespeare-Finch & Scully, 2004; Scully, 2011)

Cross sectional design Causes & direction of the relationship not determined Those with anxiety & depression may have been less likely to be involved in the survey Differences between the two groups makes it hard to make a purposeful comparison

(n = 1180) 37% - officers and managers from the 19 regions in Norway’s Amb. Service. (n = 31987) 35% of the population of Nord-Trondelag County, Norway (only those with full time employment)

Anxiety (Males: 3.5% vs 3.9% GP) & (Females: 4.0% vs. 4.4%GP) Depression (Males: 2.3% vs. 2.8% GP) & (females: 2.9% vs. 3.1% GP). Help seeking in the amb. Sample was < gen. pop. MSC pain was ass. ć help seeking from a chiropractor & only sleep disturbances were ass. ć seeking help from a psychologist Depressive or anxiety → ↑ disturbed sleep, ↑ need for recovery & ↑ somatic complaints

Arial, M. et al. (2010)

Identify work related stressors ass. ć psychiatric symptoms in

police officers Identify work related stressors predictive of psychiatric symptoms in police officers

C/S Survey including: Author determined questions on work stressors Lagner’s scale of psychiatric health symptoms*

Cross sectional design prevents determining causal relationship & the direction of the association Officers with psychiatric symptoms may/may not have been motivated to participate The use of non-professional staff bias

(n=354) 35% of Police Officers in one Swiss canton including police officers, inspectors & non-professional staff.

Psychiatric symptoms 11.9% ass. ć; Lack of support from superior Self-perception of bad quality work Inadequate work schedule High mental demand Age in decades Physical environment complaints

429


Citation Aim Method Limitations Population/Sample Outcome Malinauskiene, V. et al. (2011)

Investigate ass. between self-rated health & psychosocial factors at work & everyday life (job demands, job control, social support, workplace bullying, life-threatening events); health behaviours (smoking, alcohol, being overweight, obesity, low physical activity); mental distress; job satisfaction; & sense of coherence (SOC)

C/S survey including: Self-rated health questions Psychosocial job

characteristics – Swedish version of the Karask Demand-Control questionnaire.*

Mental distress – GHQ-12*

3 Item SOC* Lifestyle factors

Cross sectional design Causes not able to be determined Direction of the relationship not able to be determined

(n=748) 54% of randomly selected nurses from three university and six district hospitals in Lithuania from Internal Medicine departments

Poor self-rated health & psychological distress ass. ć job stress & medically certified job absence 60.4% rated their health negatively & this was ass. ć; High job demands, low job control, low

social support at work, life threatening events, low physical activity, overweightness & obesity, mental distress, job dissatisfaction and weak SOC

Hilton, M. F. Whiteford, H. A. (2010)

Investigate the role of psychological distress in workplace accidents Examine the relationship between psychological distress workplace failures, & workplace successes.

C/S survey including: WHO Health and Work

Performance Questionnaire (HPQ)*

Included in the HPQ was the Kessler 6*

Employees self-selected as to those who would participate & companies self-selected as to their participation Cross sectional design does not allow for causality or the direction of the association Blue collar employers were under represented (n = 1411)

(n=60556) 25% from (n = 58) 29% of 201 large government and private sector employers who were invited & were ass. ć 11 industry categories

↑ Psychological distress ass. ć ↑ rates of workplace accidents & failures High Psychological distress OR = 1.4 for a workplace accident. High and moderate psychological distress OR = 2.3 and 2.6 respectively for workplace failure

430

Appendices

Legend:

* = validated survey tool +ve = positive; -ve = negative ↑ = increased ↓ = decreased amb. = ambulance Ass. = associated BORRTI = the Bell Object Relations & reality testing Inventory ć = with CIS = Critical Incident Stress EMDs – emergency medical dispatchers EMS = Emergency Medical System QAS = Queensland Ambulance Service GP = General Population EMY = emergency medical technician IES = Impact of Events Scale HADS = Hospital Anxiety and Depression Scale ʄ = frequency; c/s = cross sectional IES-R = Impact of Events Scale-Revised JSS = Job Stress Survey JSQ = Job Strain Questionnaire NASS = Norwegian Ambulance Stress Survey MSC = musculoskeletal PDHS = Police Daily Hassle’s Scale NGSE = New General Self Efficacy Scale pop. = population PDS = Posttraumatic Diagnostic Scale PTGI = Posttraumatic Growth Inventory PTG = Post Traumatic Growth PTSS = Post Traumatic Stress Symptomology PTSD = Post Traumatic Stress Disorder QEAW = Questionnaire on the Experience & assessment of Work PWBS = Well-being-Psychological Support Scale Sig. = significant QoL = quality of life SPS = Perceptions of Social Support Scale SIMP = Single Item Measure of Personality T2DM = Type 2 Diabetes Mellitus SSS = Social Support Scale BFRSS = Behavioural Risk Factor Surveillance System LEADS = Longitudinal EMT Attributes & Demographics Study FCE = Functional capacity Evaluation Env = environment GWP = General Working Population

431


8.3 Appendix C Associations, Effects and Odds Ratio Tables

Table 8.3-1 Station Classification

Station Classification

Description

One Single officer station (day shift/on-call – single officer response)

Two Two officer stations (day shift/on call – single officer response)

Three Three officer stations (day/evening shift/on-call – part of the time is a two-officer response)

Four Up to 10 officers (day/evening/ on-call – two officer response)

Five Minimum 17 officers running two officer response 24 hours per day – some of these stations also have an on-call roster built in to the 24-hour roster.

Non-station Does not work at a station; 83 EMDs, 38 OIC/Supervisors who do not work at stations, 23 ‘M’ scale (managers), one director and one executive.

Table 8.3-2 Other Associations with Decreasing Self-Reported Health

Gender Issue Chi-Square Tests

Male & female Moderate bodily pain χ² (20, n = 628) = 61.844, p = .000

Male & female Bodily pain interferes with work χ² (16, n = 663) = 60.956, p = .000

Female Arthritis χ² (8, n = 241) = 17.106, p = .029

Female Asthma χ² (4, n = 241) = 14.349, p = .006

Male & female Sight & hearing disabilities χ² (24, n = 663) = 39.631, p = .023

Male & female Cardiovascular disease χ² (4, n = 663) = 21.005, p = .000

Male Diabetes χ² (4, n = 422) = 18.997, p = .001

432

Appendices

Table 8.3-3 Mental Health Conditions vs. Job Satisfaction

Males with diagnosed mental health condition tend not to:

Look forward to going to work χ² (1, n = 422) = 9.055, p = .003

Feel positive at work χ² (1, n = 422) = 5.181, p = .023

Have energy for people cared about χ² (1, n = 422) = 6.477, p = .011

Have energy for personal interests χ² (1, n = 422) = 6.875, p = .009

Report interactions at work to be positive χ² (1, n = 422) = 12.933, p = .000

Personal values fitting organisational values χ² (2, n = 422) = 8.417, p = .015

Feel involved in decisions about workplace χ² (1, n = 422) = 4.342, p = .037

Know what is expected at work χ² (1, n = 422) = 11.289, p = .001

Immediate supervisor cares about them as a person

χ² (2, n = 422) = 6.265, p = .044

Table 8.3-4 Personal and Family Stressors

Problem/s for self, family or close friends – more than one response was allowed

Problem/s for self – more than one response was allowed

AP% Resp% AP% Resp%

Death of family member or close friend

18.8 24.0 Divorce or separation

6.4 6.2

Serious illness 15.8 20.1 Witness to violence

1.8 5.6

Mental illness 7.3 17.9 Abuse or violent crime

1.9 3.6

Serious accident 3.5 3.8 Alcohol or drug related

4.7 2.9

Serious disability 2.7 2.6 Gambling 1.5 0.9

Involuntary loss of job

3.8 0.8

Trouble with police

2.5 0.6

17.5% of respondents reported one or more of the above problems

Abbreviations: AP = Australian population, Resp = Respondent

433


Table 8.3-5 Disability vs. Employment Type

% of Employment Type

Type n % EMD Operational Mgrs.

Sight Problems 30 4.5 3.6 4.5 8.0

Hearing Problems 53 8.0 7.3 7.7 16.0

Speech Problems 2 0.3 0.0 0.3 0.0

Blackouts, Fits, LOC 2 0.3 1.2 0.2 0.0

Difficulty Learning 24 3.6 4.8 3.4 4.0

Limited use of arms 9 1.4 1.2 1.3 4.0

Difficulty griping 13 2.0 2.4 2.0 0.0

Limited use of legs 6 0.9 1.2 0.7 4.0

Conditions restricting work activity 134 20.2 30.5 18.3 28.0

Disfigurement 5 0.8 0.0 0.9 0.0

Mental Illness–needs help/

supervision

19 2.9 2.4 3.1 0.0

Total 249 46.9 57.6 42.4 64.0

Table 8.3-6 Types of Cardiovascular Disease - Respondents

Typ

e

Hyp

erte

nsi

on

Hig

h C

hole

ster

ol

Rap

id/I

rreg

ula

r H

B

Oth

er

Hea

rt m

urm

urs

Hea

rt a

ttac

k

Ath

eros

cler

osis

Str

oke

Hae

mor

rhoi

ds

Hyp

oten

sion

An

gin

a

Rh

eum

atic

hea

rt

Hea

rt f

ailu

re

Flu

id r

eten

tion

Var

icos

e ve

ins

Tot

al

n 49 30 22 12 7 6 5 3 3 2 2 1 1 1 1 82

% 7.4 4.5 3.3 1.8 1.1 0.9 0.8 0.5 0.5 0.3 0.3 0.2 0.2 0.2 0.2 12.4

434

Appendices

Table 8.3-7 Self-Reported Overweightness & Health Status Indicators

Males overweight Females overweight

↑ K10 score χ² (9, n = 422) = 40.144, p = .000

Arthritis χ² (4, n = 241) = 12.777, p = .012

Asthma χ² (3, n = 422) = 17.912, p = .000 χ² (12, n = 54) = 58.611, p = .000

Bodily Pain χ² (15, n = 422) = 432.963, p = .000

Cancer χ² (3, n = 422) = 12.444, p = .006

Cardiovascular χ² (3, n = 422) = 9.377, p = .025

Diabetes χ² (3, n = 422) = 27.142, p = .000

Table 8.3-8 Fruit & Vegetable Consumption - Respondents

Fruit n % Vegetables n %

1 serve or less 255 38.5 1 serve or less 139 21.0

2 serves 263 39.7 2 serves 227 34.2

3 serves 100 15.1 3 serves 156 23.5

4 serves 26 3.9 4 serves 81 12.2

5 serves 8 1.2 5 serves 33 5.0

6 serves 4 .6 6 serves 23 3.5

Don’t eat fruit 5 .8 Don’t eat fruit 3 .5

Don’t know 2 .3 Don’t know 1 .2

Total 663 100 Total 663

435


Table 8.3-9 Fruit & Vegetables vs. Health & Works Status Indicators

Element Consequence Males Females

Veg↓ K10↑ χ² (21, n = 422) = 43.021, p = .003

χ² (15, n = 241) = 34.271, p = .003

Veg ↑ Arthritis ↓ χ² (7, n = 422) = 21.595, p = .003

No veg Cancer more likely

χ² (7, n = 422) = 17.046, p = .017

Fruit ↓

Bodily Pain ↑ χ² (35, n = 422) = 113.647, p = .000

Fruit ↑ CV ↓ χ² (5, n = 241) = 16.495, p = .006

Fruit ↑ SWY ↑ ……χ² (42, n = 663) = 59.780, p = .037……

Fruit ↑ Fatigue ↓ χ² (21, n = 422) =43.723, p = .003

Fruit ↑ Obesity↓ χ² (21, n = 422) = 115.896, p = .000

Fruit ↑

Veg ↑

YIA ↑ χ² (30, n = 241) = 139.983, p = .000

Fruit ↑

Veg ↑

BP ↓ ……χ² (14, n = 644) = 32.839, p = .003……

Abbreviations: Veg = vegetables; CV = Cardiovascular or circulatory; SWY = Shift

work years; BP = Blood pressure, SSAs = Statistically Significant Associations

436

Appendices

Table 8.3-10 Alcohol Consumption – Respondents

Every day

5-6 days/wk

3-4 days/wk

1-2 days/wk

2-3 days/ mth

About 1 day/ mth

< 1 day/mth

Don’t know

EMD 0.0% 6.3% 10.1% 19.0% 17.7% 17.7% 21.5% 7.6%

PTO 0.0% 13.3% 6.7% 13.3% 40.0% 6.7% 13.3% 6.7%

Student 0.0% 7.7% 7.7% 7.7% 15.4% 7.7% 53.8% 0.0%

ACP 0.3% 3.3% 18.9% 25.1% 22.6% 8.9% 19.2% 1.7%

CCP 0.0% 6.8% 18.3% 36.4% 15.9% 11.4% 11.4% 0.0%

Supervisor 0.9% 6.3% 22.3% 25.7% 11.6% 8.0% 12.5% 2.7%

Manager 0.0% 17.4% 30.4% 17.4% 4.2% 4.3% 21.7% 4.3%

Total 0.3% 5.3% 18.2% 26.3% 19.2% 9.7% 18.4% 2.6%

Legend: EMD, operational Personnel, supervisors/managers

Table 8.3-11 Sleep & Other Statistically Significant Associations

Element Consequence Males Females Mental health ↓ ↓ hours of sleep

Poor sleep ↑ χ² (3, n = 422) = 15.739, p = .001 χ² (21,

Bodily Pain ↑ ↓ hours of sleep Poor sleep ↑

χ² (15, n = 422) = 46.908, p = .000

Cancer Hours of sleep ↓ χ² (3, n = 422) = 10.430, p = .015

Cardiovascular Poor sleep χ² (4, n = 422) = 14.337, p = .006

CCPs, Managers

Poor sleep ……χ ² (24, n = 462) = 36.351, p = .049……

Constant fatigue

Hours of sleep ↓ χ² (9, n = 422) = 34.051, p = .018

Leaving ↓ hours of sleep Poor sleep ↑

χ² (4, n = 422) = 11.668, p = .009

Positive job satisfaction

Hours of sleep ↑ Poor sleep ↓

χ² (8, n = 422) = 18.149, p = .020

Waist-hip >0.81♀/0.91♂

Hours of sleep ↓ ……χ² (6, n = 520) = 16.125, p = .013……

Smoking Hours of sleep ↓ χ² (3, n = 663) = 8.239, p = .041

Sitting hours ↑ Hours of sleep ↓ χ² (9, n = 373) = 18.863, p = .028

437


Table 8.3-12 Work-Related Health Culture Responses

Question

Agree Undecided Disagree

Male n/%

Female n/%

Male n/%

Female n/%

Male n/%

Female n/%

1 (SS) 127/19.2 85/12.8 180/27.1 110/16.6 115/17.3 46/6.9

2 (Com) 264/39.8 164/24.7 71/69.6 31/30.4 87/13.1 46/6.9

3 (SV) 175/26.4 89/14.1 91/13.7 72/10.9 156/23.5 130/19.6

4 (PO) 211/31.8 110/16.6 69/10.4 44/6.6 142/21.4 87/13.1

Abbreviations: SS = supervisor support, Com = sense of community, SC = shared vision, PO = positive outlook

438

Appendices

Table 8.3-13 Job Satisfaction Scale & Responses

Question n (Yes)

% (Yes)

1. I look forward to going to work 367 55.4

2. I am positive most of the time at work 517 78.0

3. I have energy at the end of the day for people cared about

376 56.7

4. I have energy at the end of the day for personal interests

324 48.9

5. Most interactions at work are positive 555 83.7

6. I have good friends at work 541 81.6

7. I feel recognised and appreciated at work 302 45.6

8. My values fit with the organisational values 438 66.1

9. I trust our leadership team 235 35.4

10. I respect the work of my peers 613 92.5

11. I feel involved indecisions that affect my work unit 263 39.7

12. Creativity and innovation are supported 192 29.0

13. I feel informed about what is going on 298 44.9

14. I know what is expected of me at work 608 91.7

15. My immediate supervisor cares about me as a person 379 57.1

16. My opinion counts 241 36.3

17. My immediate supervisor reviews my progress 430 64.9

Overall Job Satisfaction Score 53 8.0

Legend: Positive; Negative

439


Table 8.3-14 Job Satisfaction vs. Increased Alcohol Consumption

Those with increased alcohol are less likely to:

Feel positive χ² (2, n = 647) = 15.363, p = .000

Energy - cared about χ² (2, n = 411) = 7.910, p = .019

Interaction work positive χ² (2, n = 411) = 17.368, p = .000

Recognised and appreciated χ² (4, n = 647) = 14.296, p = .006

Personal vs organisational values χ² (4, n = 647) = 10.899, p = .028

Trust the leadership team χ² (4, n = 647) = 12.826, p = .012

Creativity and innovation χ² (4, n = 236) = 9.727, p = .045

Knowing what is expected χ² (2, n = 411) = 8.249, p = .016

Abbreviations: SSA = Statistically Significant Association

Table 8.3-15 Job Satisfaction vs. Bodily Pain

Males who report bodily pain are less likely to:

Look forward to work χ² (5, n = 422) = 16.362, p = .006

Be positive at work χ² (5, n = 422) = 22.071, p = .001

Have energy – people cared about χ² (5, n = 422) = 16.362, p = .006

Have energy – personal interests χ² (5, n = 422) = 14.150, p = .015

Have positive interactions at work χ² (5, n = 422) = 21.062, p = .001

Have good friends at work χ² (5, n = 422) = 11.525, p = .042

Feel recognised and appreciated χ² (10, n = 422) = 35.590, p = .000

Personal values fit organisational values χ² (10, n = 422) = 33.381, p = .000

Respect the work of peers χ² (5, n = 422) = 19.909, p = .001

Feel involved in decisions χ² (5, n = 422) = 18.080, p = .003

Creativity and innovation is supported χ² (10, n = 422) = 25.359, p = .005

Report knowing what is expected at work χ² (5, n = 422) = 18.496, p = .002

Immediate supervisor cares about them as a person

χ² (10, n = 422) = 18.534, p = .047

Feel their opinion counts χ² (10, n = 422) = 19.868, p = .031

440

Appendices

Table 8.3-16 Job Satisfaction vs. Disability

Males who have a disability are less likely to:

Look forward to work (sight) χ² (6, n = 422) = 20.541, p = .002

Be positive at work (hearing & sight) χ² (6, n = 422) = 13.610, p = .034

Have energy – people cared about (hearing & speech)

χ² (6, n = 422) = 18.875, p = .004

Have energy – personal interests (hearing & speech)

χ² (6, n = 422) = 23.670, p = .001

Have positive interactions at work (hearing, sight &speech)

χ² (6, n = 422) = 21.026, p = .002

Feel recognised and appreciated (hearing & speech)

χ² (12, n = 422) = 26.121, p = .010

Have personal values fit organisational values (hearing)

χ² (12, n = 422) = 23.219, p = .026

Respect the work of peers (speech) χ² (6, n = 422) = 23.167, p = .001

Feel creativity and innovation is supported (hearing)

χ² (12, n = 422) = 21.730, p = .041

Feel informed (sight, hearing &speech) χ² (12, n = 422) = 21.674, p = .041

Report knowing what is expected at work (hearing)

χ² (6, n = 422) = 23.459, p = .001

Females who have disability are less likely to:

Have good friends at work χ² (4, n = 241) = 13.722, p = .008

Females & males who have a disability are less likely to:

Feel involved in decisions (hearing &speech)

χ² (6, n = 663) = 15.458, p = .017

441


Table 8.3-17 Regular Rest Breaks and Positive Job Satisfaction Associations

Associations Males Females

↑ Job satisfaction

……χ² (2, n = 663) = 8.231, p = .016……

↓ fatigue χ² (3, n = 422) = 45.913, p = .000

↓ Barriers to exercise

χ² (6, n = 422) = 24.543, p = .000

Performance higher

χ² (4, n = 422) = 13.649, p = .009

χ² (4, n = 241) = 14.847, p = .005

↑ sleep quality ……χ² (4, n = 462) = 10.962, p = .027……

↑hours of sleep χ² (4, n = 422) = 23.137, p = .000

χ² (4, n = 241) = 20.095, p = .000

Sense of community

χ² (4, n = 422) = 25.697, p = .000

χ² (4, n = 241) = 13.094, p = .011

Shared vison χ² (4, n = 422) = 29.402, p = .000

Positive workplace

χ² (4, n = 422) = 34.763, p = .000

Table 8.3-18 Irregular Rest Breaks & Negative Associations

More likely Males Females

Health status ↓ χ² (4, n = 422) = 16.4892, p = .002

K10 score↑ χ² (3, n = 422) = 25.312, p = .000

Bodily Pain ↑ χ² (5, n = 422) = 14.037, p = .015

Thoughts of leaving↑

χ² (1, n = 422) = 11.725, p = .000

↑ sitting hours

χ² (3, n = 207) = 12.773, p = .005

……CCPs χ² (3, n = 41) = 12.483, p = .006……

442

Appendices

Table 8.3-19 Lack of Time or Energy – Associations

Element Barrier

(lack of:)

Males Females

Asthma Time χ² (6, n = 422) = 15.635, p = .016

Overweight Time & energy

χ² (18, n = 422) = 34.802, p = .01

χ² (8, n = 229) = 21.211, p = .000

Bodily Pain χ² (25, n = 422) = 39.085, p = .036

χ² (16, n = 241) = 35.522, p = .003

K10 ↑ Time & energy

χ² (15, n = 422) = 60.173, p = .000

χ² (9, n = 27) = 17.991, p = .000

Leaving Time & energy

……χ² (5, n = 663) = 23.480, p = .000……

↑SWY Time …….χ² (36, n = 663) = 54.200, p = .026……

Abbreviations: SSAs = Statistically Significant Associations

Table 8.3-20 Workplace Wellness Programs Associations

Job satisfaction Males Females

Recognised & appreciated at work

……χ² (4, n = 663) = 13.027, p = .011……

Personal values fit with organisational values

χ² (4, n = 422) = 13.543, p = .009

χ² (4, n = 241) = 17.201, p = .002

Creativity & innovation supported

χ² (4, n = 422) = 9.475, p = .050

Work-Related Health culture Males Females

Sense of community χ² (8, n = 422) = 17.595, p = .024

Shared vision χ² (8, n = 422) = 19.276, p = .013

Positive outlook χ² (8, n = 422) = 28.850, p = .000

443


8.4 Appendix D: Regression Plan

Regression Analysis Plan – determining effect size on known

associations to determine which independent variables to include in

Regression analysis

Analyze → Descriptive statistics → Crosstabs

Dependent variables → row, Independent variables → column, Confounders →

Layer 1 of 1

Statistics → Chi‐square, Phi and Cramer’s V

Cell: Counts → Observed, Percentage → Row, Residuals → adjusted standardised

2*2 Tables use Phi: effect criteria are: small = 0.10, medium 0.30, large 0.50. Where

the value of Phi and Cramer’s V are the same, Phi is reported, and those

independent variables are crossed out where an effect size is less the ‘medium’.

Where the effect is ‘medium’ or ‘large’ but it is not statistically significant, the

independent variable is also crossed out. (Cohen 1988)

Greater than 2*2 tables use Cramer’s V.

Effect Criteria for tables larger than 2*2: R = Row, C = Column

1. R‐1, C‐1 equal to 1 (two categories): small = 0.01, medium = 0.30, large =

0.50

2. R‐1, C‐1 equal to 2 (two categories): small= 0.07, medium= 0.21, large = 0.35

3. R‐1, C‐1 equal to 3 (three categories): small = 0.06, medium = 0.17, large =

0.29

Abbreviations:

MHD = Mental Health Disorder

K10 = Psychological Distress Scale

CD = Chronic Disease

444

Appendices

Health Status Dependent Variable (Outcome)

Independent Variable (predictor) - Singular Statistical Association already established

Confounders

Self-reported health Revised (nominal) 1= poor/fair 2= Good 3= Excellent /very good Use Ordinal Logistic Regression

drinks per week (Phi 0.36) ↑ alcohol over last 12/12 (Phi 0.16) SBP > 140mmHG (Phi .0.49) & DBP > 90mm HG

(Phi 0.45) & High BP >140/90 (Phi 0.16) Overweightness & obesity (Phi 1.28) Bodily pain (Phi 0.22) Bodily pain interferes with work (Phi 0.27) Arthritis (Phi 0.11) (15-24 age Phi 0.37) Asthma (Phi 0.27) Sight (Phi 0.08) & hearing disabilities (Phi 0.08) CVD (Phi 0.14) Diabetes (Phi 0.16) (55-64 age Phi 0.47)

Age Gender

MHD (nominal) 0= No 1= Yes Use Binary Logistic regression

K10 (Phi 0.32) Anxiety (Phi 0.15) Younger & older age have Phi >

0.31) Job insecurity (Phi 0.02) Disability (Phi 0.16) Arthritis (Phi 0.08) Cancer (Phi 0.08) Asthma (Phi 0.16) (Age Phi 0.31) CVD (Phi 0.13) Other work (Phi 0.08) Thoughts of leaving (Phi 0.03) Job satisfaction (Phi 0.11) Personnel Stressors (Phi 0.11) Family Stressors (Phi 0.11)

Gender Age

K10 (nominal) 1 = well 2 = Mild 3 = Moderate 4 = Severe Use Ordinal Logistic Regression

SR health status (Phi 0.38) Family Stressors (Phi 0.20) (15-24 age Phi .32) Personal stressors (Phi 0.25) (age >0.30) Asthma (Phi 0.11) (55-64 age Phi 0.32) Arthritis (Younger & older age have Phi > 0.31) Cancer (Phi 0.20) (35-44 age Phi 0.31) CVD (Phi 0.12) (55-64 age Phi 0.33) Alcohol – how many/week (Phi 0.54) Diabetes (55-64 age Phi 0.43) Other work (Phi 0.08) Job satisfaction (Cramer’s V 0.21)

Age Gender

Disability. 0 = No 1 = Yes Use Binary Logistic Regression

Checkups with GP (Phi 0.12) SR Health (Phi 0.15) adjusted for SWY - >25 SWY

Phi (0.30) Employment type (Phi 0.08) Gender (Phi 0.08) Age (Phi 0.31) Job satisfaction – adjusted for SWY - > 20SWY (Phi

>0.34)

SWY

445


Health Status Dependent Variable (Outcome)


Confounders

LTC (Nominal) 0 = N0 1 = Yes Use Binary Logistic Regression

Employee skill group adjusted for SWY, age (Phi > 0.32), for gender – males Phi. 0.32

Asthma (SWY Phi > 0.30, Age & gender Phi > 0.30) Anxiety Description (Phi 0.31) Fatigue description (Phi 0.30) Barriers to exercise CVD, (>10 SWY Phi >0.30) Cancer (>15 SWY Phi > 0.30, >26 Years Phi >0.30) Disability (SWY Phi > 0.30, Age & gender Phi >

0.30) Diet (vegies & fruit SWY, age & gender Phi > 0.30) Job satisfaction (Age Phi > 0.30, Gender Phi 0.32) MHD (Phi O.31) Sleep RDOs (Phi. 0.30) Stressors (> 10SWY Phi > 0.32) Job satisfaction (SWY Phi > 0.32, Age Phi > 0.40) Health Culture (SWY Phi >0.32), (Age Phi >0.31) SR Health (SWY Phi > 0.32), (age Phi > 0.31),

Gender (Phi > 0.32) GP checkups (SWY 15-24 Phi > 0.32) (Age Phi >

0.40)

Age Gender SWY

446

Appendices

Chronic Disease Dependent Variable (Outcome)


Confounders

Asthma (Nominal) 0 = No 1 = Yes Binary Logistic Regression

Barriers to exercise (Phi < 0.00) Education (phi < 0.07) (Highest qual. 15-

24 age Phi 0.36) LTC (Phi 0.13) Overweightness (Phi < 0.51) (Waist-hip

15 24 age Phi 0.33) Performance (Phi 0.04) Relationship status (Phi 0.08) Family Stressor (Phi 0.14) Personal stressors (Phi 0.03)

Gender

CVD (Nominal) 0 = No 1 = Yes Binary Logistic Regression

Anxiety Barriers to exercise (Phi < 0.30) Overweight (Phi <0.30) Performance (Emp. Skill - EMD Phi 0.49) Diabetes (Phi < 0.30) Fatigue (55-64 age Phi 0.32) Diet (Phi 0.34 for not currently working

shift work) Job satisfaction (EMD Phi 0.33) Other work (Phi 0.11) Sleep (Phi <0.31) Work health culture (Phi < 0.31) Family stressors (Phi <0.30)

Employment skill

Gender Shift work Age

Cancer (Nominal) 0 = No 1 = Yes Binary Logistic Regression

Alcohol last 7 days (Age Phi 0.31, gender Phi 0.34, shift work Phi 0.34, ESG Phi 0.34)

Alcohol 5 or more at one time (SW Phi 0.43, 55-64 age Phi 0.30)

Anxiety description (45-54 Phi 0.30, EMD Phi 0.32)

Overweightness (Phi <0.30) Breaks (Phi <0.30) Diabetes (EMD Phi 0.36)) Disability (Phi< 0.30) Job satisfaction (SW Phi 0.30) Diet (Phi, 0.30) Relationship status (S/M Phi 0.32) Smoking (Phi < 0.30) Stressors (Phi < 0.30) Work health culture (Phi < 0.30)

Age Gender Employment

skill

447




Confounders

Diabetes (Nominal) 0 = No 1 = Yes Binary Logistic Regression

Arthritis (Phi < 0.30) Overweight (Phi < 0.30) Bodily pain (Cat 3 Phi 0.49) Disability (Phi <0.30) Education (Phi < 0.31) Fatigue (How Often - Cat 3 Phi 0.31,

Don’t work @ stat. Phi 0.36, Describe fatigue Cat3 Phi 0.32)

Job satisfaction (Cat 3 Phi 0.45 & Cat 4 Phi 0.32)

Other work (Phi < 0.30) Performance (S/M Phi 0.31, Cat 2 Phi

0.57) SR health (S/M Phi 0.33, 55-64 age Phi

0.55, Cat 4 Phi 1.00)

Age Gender Employment skill Station category

Arthritis (Nominal) 0 = No 1 = Yes Binary Logistic Regression

Barriers to exercise (Phi < 0.30) BP (SBP Sep. Phi 0.32, Div. Phi 0.7,

DBP- Sep. Phi 0.41, Div. Phi 0.75, High BP EMD Phi 0.31, Sep. Phi 0.47, Div. Phi 0.69)

Bodily pain (Phi < 0.30) Overweight (W/H divorced Phi 0.43,

Body pain 25-34 age Phi 0.33, 55-64 age Phi 0.40, Rel. Status – Separated Phi 0.32, Divorced Phi 0.71))

Cancer (Phi < 0.30) Disability (15-24 age Phi 0.36, Rel. Status

– Never Phi 0.33, Div. Phi 0.35) Fatigue (how often -Separated 0.40, div.

0.47, describe – div. Phi 0.67) Job satisfaction (15-24 age Phi 0.44, SW

Phi 0.3, Never rel. Phi 0.31, Separated Phi 0.34, Div. Phi 0.35)

Diet (Vegies - EMD Phi 0.30, Never Phi 0.30, Div. Phi 0.31, Fruit, 15-24 age Phi 0.43, Sep. Phi 0.32, Div. Phi 0.47

Sleep quality (Div. Phi 0.30) Stressors (Phi < 0.30)

Gender Employment skill Relationship status Shift work Age

448

Appendices



Confounders

3 or more CD (Nominal) 0 1 2 3 4 Use ordinal Logistic Regression

Overweight (55-64 age Phi 0.42, ta1 Phi 0.34, Cat 2 Phi 0.34, Cat 3 Phi 0.46, W/H EMD Cramer’s V 0.32, Cat 3 Cramer’s V 0.74

Cancer (Phi 0.36) Sleep (Hours - 55-64 age Phi 0.46, No

stat. Phi 0.34, Quality – 55-64 age Phi 0.72, Cat 4 Phi 0.64

Fatigue (how often 15-24 Cramer’s V 0.41, 25-34 Cramer’s V 0.21, Des Fatigue S/M Phi 0.52, 25-34 age Cramer’s V 0.28, 55-63 age Cramer’s V 0.40, Cat 3 Phi 0.70, Cat 5 Phi 0.32)

Anxiety (How often – 35-44 age Phi 0.32, Cat 3 Phi 0.72, Describe Anx. S/M Phi 0.45, Male Phi 0.31, 25-34 age Phi 0.50,

Job satisfaction (S/M Cramer’s V 0.27, Male Cramer’s V 0.18, 15-24 age Cramer’s V 0.39, 25-34 age Cramer’s V 0.19, NO stat. Cramer’s V 0.24)

BP (Phi 0.30) SBP, DBP & High BP have Phi > 0.30 ass. With M/S, Cat 3 Stat. & 25-34 age group.

Stressors (Family - EMD Phi 0.39, Personal – S/M Phi 0.34

LTC (Phi 1.20) Alcohol how many/per week (Phi 1.2) Alcohol 5 or more (Phi 0.38) Smoking (S/M Phi 0.33, Disability (Emp. Type Phi 0.34, Gender

Phi 0.34) Body pain (M/S Cramer’s V 0.35, Para

Cramer’s V 0.21, Gender Cramer’s V 0.24, age Cramer’s V 0.21, Stat. Cat. Cramer’s V 0.24)

K10 (Phi 0.40) GP check ups SR health (Phi 0.30) Consider leaving (Cat one Phi 0.55) Shift work Diet (Vegies – EMD Phi 0.58, Fruit No

Stat. Phi 0.46

Station category Age Gender Employment skill

449


Organisational symptomology Dependent Variable (Outcome)


Confounders

Job satisfaction (nominal) 1 Depressing 2 Bad 3 Ok 4 Good 5 Great Use Ordinal Logistic Regression

Employment type (15-24 age Phi 0.51, 35-44 age Phi0.39, 45-54 age Phi 0.32)

Current shift workers (Phi 0.13) SWY (Phi 0.26) Qualifications (Highest school 55-64

Phi 0.48, Highest Qual. 15-24 Phi 0.61)

Alcohol more than last year (55-64 age Phi 0.54)

Bodily Pain (25-34 Phi 0.38) SR health (35-44 age Phi 0.30) Cancer (Phi 0.12) Station category (Phi 0.14) Disability (55-64 age Phi 0.41) Fatigue (how often Phi 0.38, describe

Phi 0.36) Anxiety (how often Phi 0.37, describe

Phi 0.32)

Age

Health culture (nominal) 0 Negative 1 Positive Binary Logistic Regression

Employment type (10-14 SWT Phi 0.38, 15-19 SWY Phi 0.31, 25-29 SWY Phi 0.32,

Education (Highest school Phi 0.06, Highest qual. Phi 0.12)

SR health (Phi 0.5) Arthritis (Phi -0.09) Cancer (Phi -0.10) CVD (Phi – 0.05) Bodily pain Phi 0.10) LTC (Phi 0.10) Station category (Phi 0.09) Thoughts of leaving (Phi -).27)

SWY

Thoughts of leaving (nominal) None

Alcohol more than last year (Phi 0.12) Arthritis (Phi 0.07) Bodily pain (Phi 0.16) LTC (Phi 0.23) SR health (Phi 0.14) Cancer (Phi 0.16)

Gender Employment

skill Age

450

Appendices

Organisational symptomology Dependent Variable (Outcome)


Confounders

Breaks (nominal) 0 = No 1 = Yes Use Binary Logistic Regression

Job satisfaction (Male Phi 0.30, S/M Phi).30, Para. Phi 0.32, 25-34 age Phi 0.33, 45054 age Phi 0.32, 55-64 age Phi 0.37)

Fatigue (Describe – 45-54 age Phi 0.39, male Phi 033, How often - 45-54 age Phi0. 35, 55-64 age Phi 0.43)

Barriers to exercise (Phi <0.30) Performance (Phi < 0.30) Sleep (quality – 55-64 age Phi 0.33) Work health culture (55-64 age Phi

0.30) SR health (55-64 age Phi 0.40) K10 (S/M Phi 0.37, 45-54 Age Phi

0.30, 55-64 age 0.63) Bodily Pain (55-64 age Phi 0.53) Thoughts of leaving (Phi <0.30) Sitting hours (EMD Phi 0.76,

Gender Age Employment

skill

451


Caring for self Dependent Variable (Outcome)


Confounders

Fatigue (Nominal) 0 = never 1 = Occas…ly 2 = Frequent 3 = Constant Use Ordinal Logistic Regression

Bodily pain (Para Phi 0.30, 15-24 age Phi0.43, 25-34 age Phi 0.35, 55-64 age Phi 0.55, male Phi 0.37)

Job satisfaction (Emp. Type Phi 0.38, age group Phi 0.38, Gender Phi 0.38)

Alcohol – weekly (35-44 age Phi 0.37)

Alcohol – 5 or more in one sitting weekly (EMD Phi 0.53, 35-44 age Phi 0.55, female Phi 0.31))

SR health status (Emp. Type Phi 0.31, age group Phi 0. 31, Female Phi 0.30)

Disabilities (15-24 age Phi 0.37) Breaks (45-54 age Phi 0.35, 55-64 age

Phi 0.43, male Phi 0.33) Station category (Phi < 0.30) Work health culture (S/M Phi 0.30,

25-34 age Phi 0.34)

Employment skill

Gender Age

Sleep (Nominal) 1 = 6 hour or less 2 = 7 hours or more Use Binary Logistic Regression.

Chronic disease (55-64 age Phi 0.46) Qualifications (Phi <0.30) Shift work (EMD Phi 0.77, female Phi

0.60) SR health (S/M Phi 0.31, 55-64 age

Phi 0.31) Alcohol – 5 or more in one sitting

weekly (45-54 age Phi 0.32) Alcohol – amount per week (45-54

age Phi 0.30) MHD (15-24 age Phi 0.33) Bodily Pain (55-64 age Phi 0.43) Cancer (Phi < 0.30) CVD (Phi < 0.30) Fatigue (Phi < 0.31) Thoughts of leaving (Phi <0.30) Job satisfaction (Phi < 0.31) Overweight (W/H 15-24 age Phi 0.36) Smoking (Phi < 0.30) Sitting (Phi < 0.30)


skill

452

Appendices

Caring for self Dependent Variable (Outcome)


Confounders

Barriers to exercise (lack of time) (nominal) 0 = Rarely 1 = Sometimes Binary Logistic Regression

Alcohol – 5 or more at one sitting weekly (EMD Phi 0.43, S/M Phi 0.38, 15-24 age Phi 0.40)

Alcohol – weekly (EMD Phi 0.46) Breaks (Phi < 0.30) Exercise (Para Phi 0.32, 35-44 Phi

0.44, 55-64 Phi 0.63, Gender Phi 0.31)

Diet (Fruit EMD Phi 0.39, 55-64 age Phi 0.36)

Asthma (Phi < O.30) Overweight Phi < 0.30 Bodily Pain (Phi <0.30) K10 (Phi < 0.30) Thoughts of leaving (Phi < 0.31)


skill

Barriers to exercise (lack of energy) (nominal) 0 = Rarely 1 = Sometimes Binary Logistic Regression

Alcohol – 5 or more at one sitting weekly (Phi < 0.30)

Alcohol – more than last year (Phi < 0.30)

Breaks (Phi < 0.30) Exercise (Phi < 0.30) Diet (Fruit - EMD PHI 0.47) Asthma (15-24 age Phi 0.39) Overweight (Phi < 0.30) Bodily Pain (Phi < 0.30) K10 (Phi < 0.30) Thoughts of leaving (Phi < 0.30)


skill

Hours worked 1 < 161 2 161-180 3181-200 4>200 Use Ordinal Logistic Regression

Employment type (25-34 Phi 0.31, 55-64 age Phi 0.42, Female Phi 0.30, EMD Phi 0.32)

YIA (15-24 age Phi 0.51) SWY (Phi < 0.30) Current shift work (Phi < 0.30) Job satisfaction (35-44 Phi 0.36, 55-

64 age Phi 0.66) Work health culture (55-64 age Phi

0.40) Stressors (Phi < 0.30) Fatigue (55-64 age Phi 0.57) Sleep (Phi <0.30)

Age Gender Employment

skill

453


8.5 Appendix E: Semi-Structured Interview

SEMI‐STRUCTURED INTERVIEW ‐ QUESTIONS

1. Opening question:

What did you think of the outcomes from the study19?

2. Key questions:

Now imagine you are part of a committee tasked with identifying and making

recommendations in regard to potential health and well‐being programs for

operational personnel.

o What are the factors that you will make sure the committee considers in

designing these programs?

o These factors can be in many areas: individual and organisational,

equipment, education, skills development, coaching, training, courses,

drivers of culture and professional assistance

The participants in the study identified the issues that they considered were

barriers to exercise.

o What are some of the elements the committee could consider in

attempting to overcome these obstacles and make personnel feel more

comfortable and interested in exercise?

o This could include a wide range of services: new personnel orientation,

support to exercise, financial incentives, exercise promotion, hotline and

anything else you could think of.

A large number of operational personnel did not respond to the survey. There may

be qualms, negative reactions and turns offs that are unknown.

o What are the compelling arguments that will get people to change their

minds and utilise these services?

o What are the non‐obvious benefits that people are looking for?

o How do we sell this program?

3. End questions:

Of all the things we discussed, what is the most important?

Have we missed anything?

19 ‘The study’ refers to ‘The health of operational personnel in the Queensland Ambulance

Service: Influences, associations and impacts’

454

Appendices

Approach email

Subject Title: Semi‐structured interview ‐ The Health of Operational Personnel in the Queensland Ambulance Service: Influences, Associations and Impacts Dear colleagues I’m doing a PhD which aims to evaluate the health and work‐life balance of operational personnel in the QAS. I’m seeking individuals to participate in one hour semi‐structured interview regarding policy and systems with the aim of providing options for improving the health of operational personnel QAS. Your participation in this project is entirely voluntary and approved by the Commissioner QAS. I will come to you to minimise your need to travel. The first part will be an overview on the outcomes of the Ambulance Health Survey (AHS) 2015 which commenced in May 2015 and completed in August 2015. The second part will be the interview, conducted by myself. I will take notes and audio‐record the interview which, in conjunction with the notes, will be used for the purposes of analysis only. I may contact you after the interview to clarify comments, discussion and my conclusions. Please view the attached Participant Information Sheet and Consent Form for further details on the study. Should you wish to participate or have any questions, please contact me via email at [email protected]. Please note that this study has been approved by the QUT Human Research Ethics Committee (approval number 1400000936). Many thanks for your consideration of this request.

Richard Galeano

PhD Candidate

0437 729 735

[email protected]


455


Participant Information Sheet

PARTICIPANT INFORMATION FOR QUT RESEARCH PROJECT – Semi‐structured Interview –

Ambulance Health Survey (AHS) 2015

QUT Ethics Approval Number 1400000936

RESEARCH TEAM Principal Researcher: Mr Richard Galeano, PhD candidate Associate Researcher: Professor Gerard FitzGerald, Principal supervisor Professor Tony Parker, Associate supervisor Professor Michele Clark, Associate supervisor School of Public Health and Social Work, Faculty of Health Queensland University of Technology

DESCRIPTION This project is being undertaken as part of PhD for Richard Galeano. The purpose of this project is to develop a comprehensive picture of the health of operational personnel in the Queensland Ambulance Service (QAS), identify factors that influence their health, identify associations between work and lifestyle, and determine how work groups and ambulance organisations may impact positively on the overall health of operational personnel. Yourself and seven other participants have been approved to participate in this semi‐structured interview by the Commissioner, QAS and are invited to attend as we believe you can provide valuable input into this exploration. This project has approval from the Commission QAS.

PARTICIPATION Your participation will involve a discussion on the outcomes of the AHS 2015 data collections and analysis, followed by an audio recorded semi‐structured interview at an agreed location that will take approximately one hour of your time. Questions will include:

“Imagine you are part of a committee tasked with identifying and making recommendations in regards to potential health and well‐being programs for operational personnel ‐ what are the factors that you will make sure the committee considers in designing these programs?” and,

“Barriers to exercise were identified in the study ‐ what are some of the elements the committee could consider in attempting to overcome these obstacles and make personnel feel more comfortable and interested in exercise?”

The semi‐structured interview will be conducted by myself. Your participation in this interview is entirely voluntary. If you do agree to participate you can withdraw from the interview without comment or penalty. Your decision to participate or not participate will in no way impact upon your current or future relationship with QUT or with QAS.

456

Appendices

EXPECTED BENEFITS It is expected that this project will not benefit you directly. However, it may benefit future healthcare policies, recommendations and public health programs for ambulance personnel. It is expected results of the study will be available in 2017, and will be presented at organised QAS station meetings throughout Queensland. Please note this interview is not intended for the purpose of divulging personal information or complaints you may have.

RISKS There are minimal risks associated with your participation in this project. These may include experiencing discomfort or distress in reliving your experiences, or discomfort with sharing your views in an interview.

Support is available through QAS Priority One at 1800 805 980. QUT also provides for limited free psychology, family therapy or counselling services (face‐to‐face only) for research participants of QUT projects who may experience discomfort or distress as a result of their participation in the research. Should you wish to access this service please call the Clinic Receptionist on 07 3138 0999 (Monday–Friday only 9am–5pm), QUT Psychology and Counselling Clinic, 44 Musk Avenue, Kelvin Grove, and indicate that you are a research participant. Alternatively, Lifeline provides access to online, phone or face‐to‐face support, call 13 11 14 for 24 hour telephone crisis support.

PRIVACY AND CONFIDENTIALITY All comments and responses will be treated confidentially unless required by law. The names of individual persons are not required in any of the responses. You will not have the opportunity to verify your comments and responses prior to final inclusion; however a follow‐up telephone call may be requested to clarify your comments. The audio recording will be transcribed and your responses will be rendered non‐identifiable; the audio recording will be destroyed after the contents have been transcribed. Only the research team will have access to the audio recording. It is not possible to participate in the project without being audio recorded. Any data collected as part of this project will be stored securely as per QUT’s Management of research data policy. 9 Please note that non‐identifiable data collected in this project may be used as comparative data in future projects or stored on an open access database for secondary analysis.

CONSENT TO PARTICIPATE We would like to ask you to sign a written consent form (enclosed) to confirm your agreement to participate.

QUESTIONS / FURTHER INFORMATION ABOUT THE PROJECT If you have any questions or require further information, please contact one of the research team members below. Mr Richard Galeano Professor Gerard FitzGerald Phone 0437 729 735 Phone 0731 383 935 Email [email protected] Email [email protected]

CONCERNS / COMPLAINTS REGARDING THE CONDUCT OF THE PROJECT QUT is committed to research integrity and the ethical conduct of research projects. However, if you do have any concerns or complaints about the ethical conduct of the project you may contact the QUT Research Ethics Advisory Team on [+61 7] 3138 5123 or email [email protected]. The QUT Research Ethics Advisory Team is not connected with the research project and can facilitate a resolution to your concern in an impartial manner.

457


CONSENT FORM FOR QUT RESEARCH PROJECT – Semi‐structured interview –

Ambulance Health Survey (AHS)

QUT Ethics Approval Number 1400000936 RESEARCH TEAM CONTACTS Richard Galeano, PhD candidate Professor Gerard FitzGerald Phone: 0437 729 735 Phone 0731 383 935 Email [email protected] Email [email protected]

STATEMENT OF CONSENT

By signing below, you are indicating that you:

Have read and understood the information document regarding this project.

Have had any questions answered to your satisfaction.

Understand that if you have any additional questions you can contact the research team.

Understand that you are free to withdraw without comment or penalty but after de‐identification of your data withdrawal will not be possible.

Understand that you can contact the Research Ethics Advisory Team on [+61 7] 3138 5123 or email [email protected] if you have concerns about the ethical conduct of the project.

Understand that the project will include an audio recording.

Understand that non‐identifiable data collected in this project may be used as comparative data in future projects.

Agree to participate in the project.

Name

Signature

Date

MEDIA RELEASE PROMOTIONS

From time to time, we may like to promote our research to the general public through, for example, newspaper articles. Would you be willing to be contacted by QUT Media and Communications for possible inclusion in such stories? By ticking this box, it only means you are choosing to be contacted – you can still decide at the time not to be involved in any promotions.

Yes, you may contact me about inclusion in promotions.

No, I do not wish to be contacted about inclusion in promotions.

Please return this sheet to the investigator.

458

Appendices

8.6 Appendix F: Category of Variables

Table 8.6-1 Variables Categorised

459


Variable Variable Category

Gender Ordinal

Age Scale

Age grouped Ordinal

Years of ambulance work Scale

Years of ambulance work grouped Ordinal

Total shift work years Scale

SWYR grouped Ordinal

Employee skill Nominal

Employee skill grouped Ordinal

Employee type Nominal

Employee type grouped Ordinal

Current shift worker Ordinal

Total shift work years Scale

Station Category Ordinal

Station name String

Relationship status Nominal

Relationship status grouped Ordinal

Highest school Nominal

Highest school grouped Ordinal

Highest qualification Nominal

460

Appendices


Highest qualification grouped Ordinal

Other work Ordinal

Consider leaving Ordinal

Roster type Nominal

Self-reported health Ordinal

Self-reported health revised Ordinal

GP Check-ups Ordinal

K10 Count

K10 revised Ordinal

Mental Medications Ordinal

Body Pain Ordinal

Disability Nominal

Disability grouped Ordinal

Self-reported BMI Ordinal

Exercise hours Count

Exercise hours grouped Ordinal

Sitting hours Count

Sitting hours grouped Ordinal

Smoking Ordinal

Diet vegetables Ordinal

461



Diet fruit Ordinal

Alcohol Count

Alcohol grouped Ordinal

Asthma Ordinal

Skin checks Ordinal

Cancer Ordinal

CVD Ordinal

Arthritis Ordinal

BP checks Ordinal

Diabetes Ordinal

Long term condition Ordinal

Mental health disorder Nominal

Back problems computed Ordinal

Family stressors Nominal

Personal stressors Nominal

Height Count

Weight Count

BMI Ratio

Waist Count

Hip Count

462

Appendices


Waist-hip Ratio

BMI group Ordinal

Waist-hip group Ordinal

SBP Count

DBP Count

BP (SBP/DBP) Ratio

Rest breaks Ordinal

Wellness incentives Ordinal

Wellness facility Ordinal

Barriers to exercise Nominal

Job satisfaction (JS) Nominal

JS computed Ordinal

Work related health culture (WRHC) Nominal

WRHC computed Ordinal

Anxiety Nominal

Fatigue Nominal

Hours worked Count

Performance Nominal

Sleep quality Nominal

Sleep hours Count

463
