inpatient mental health staff morale: a national investigation · outcome in final model.....68...

© Queen’s Printer and Controller of HMSO 2011 Project 08/1604/142

Inpatient Mental Health Staff Morale: a National Investigation

Sonia Johnson1, Stephen Wood2, Moli Paul3, David Osborn1, Elizabeth Wearn1, Brynmor Lloyd-Evans1, Jonathan Totman1, Ricardo Araya4 , Elizabeth Burton3 , Bartley Sheehan3, Gillian Hundt3, Nigel Wellman5 , Fiona Nolan1 and Helen Killaspy1

1 University College London 2 University of Sheffield 3 University of Warwick 4 University of Bristol 5 Thames Valley University

Published January 2011


2

Address for correspondence: Professor Sonia Johnson Dept of Mental Health Sciences University College London 67-73 Riding House Street London W1W 7EY Email: [email protected] This report should be referenced as follows: Johnson S, Wood S, Paul M, Osborn DP, Wearn E, Lloyd-Evans B, et al. Inpatient Mental Health Staff Morale: a National Investigation. Final report. NIHR Service Delivery and Organisation programme; 2010. Copyright information: This report may be freely reproduced for the purposes of private research and study and extracts (or indeed, the full report) may be included in professional journals provided that suitable acknowledgement is made and the reproduction is not associated with any form of advertising. Applications for commercial reproduction should be addressed to NETSCC, SDO. Disclaimer:

This report presents independent research commissioned by the National Institute for Health Research (NIHR). The views and opinions expressed by authors in this publication are those of the authors and do not necessarily reflect those of the NHS, the NIHR, the NIHR SDO programme or the Department of Health. The views and opinions expressed by the interviewees in this publication are those of the interviewees and do not necessarily reflect those of the authors, those of the NHS, the NIHR, the NIHR SDO programme or the Department of Health.


3

Contents List of tables................................................................................... 5

List of figures ................................................................................. 8

Glossary of terms/abbreviations ........................................................ 9

Acknowledgements.........................................................................10

Executive summary ........................................................................11

Background ................................................................................11

Aims..........................................................................................11

Methods .....................................................................................11

Results ......................................................................................12

Conclusions ................................................................................14

1 Background .............................................................................16

1.1 The “crisis in acute care” ......................................................16

1.2 The importance of staff morale..............................................17

1.3 Factors influencing staff morale .............................................19

1.4 A framework for investigating staff morale..............................19

1.5 Defining and understanding morale........................................28

2 Aims .......................................................................................32

3 Methods ..................................................................................33

3.1 Modules 1 & 2: The 100 ward survey and Comparison with Community teams .......................................................................33

3.2 Module 3: Qualitative study ..................................................48

3.3 Module 4: Leavers’ Survey....................................................51

3.4 Module 5: Persistence of high and low morale .........................52

3.5 Module 6: Staff sickness and turnover....................................53

4 Results....................................................................................54

4.1 A description of staff morale and its associations with ward type and demographic characteristics....................................................54

4.2 The relationship between indicators of morale .........................75

4.3 Testing the demand, support, control model of morale .............82

4.4 The relationship between Demand, Support and Control and type of service ...................................................................................90


4

4.5 The built environment and morale ....................................... 100

4.6 The prevalence of adverse events and their relationship with indicators of morale ...................................................................103

4.7 The relationship of social deprivation of area and patient population to morale.................................................................. 120

4.8 Organisational context and morale....................................... 129

4.9 Exploratory overall models of staff morale ............................148

4.10 Module 3 – a qualitative investigation of staff morale...........171

4.11 Module 4 – Leavers’ Survey .............................................186

4.12 Module 5 – The persistence of high and low morale .................190

4.12 Module 6 – Ward sickness and turnover rates and their relationship to morale ................................................................200

5 Discussion ............................................................................. 208

5.1 Strengths and Limitations of the study .................................208

5.2 Main Findings ................................................................... 210

5.3 Implications for research .................................................... 219

5.4 Implications for policy and practice ......................................221

References .................................................................................. 227

Appendix 1.................................................................................. 237

Appendix tables ...........................................................................252


5

List of tables Table 1. Overview of the study ....................................................34

Table 2. Sample characteristics....................................................57

Table 3. Levels of staff well-being in wards, crisis teams and community mental health teams across England.................................61

Table 4. Levels of satisfaction and morale in wards, CMHTs and CRTs in England ...................................................................................64

Table 5. Multilevel models for Maslach Burnout Inventory variables – demographic and occupational variables significantly associated with outcome in final model....................................................................68

Table 6. Multilevel models for measures of work-related well-being– demographic and occupational variables significantly associated with outcome in final model....................................................................72

Table 7. Multilevel model for intrinsic job satisfaction – demographic and occupational variables significantly associated with outcome in final model ...................................................................................74

Table 8. Pearson’s Correlations between indicators of morale – all correlations in table highly significant (p<0.0005)...............................76

Table 9. Variance explained through principal components analysis...78

Table 10. Structure matrix from Principal Components Analysis of main morale variable..............................................................................79

Table 11. Structure matrix from Principal Components Analysis of main morale variable..............................................................................80

Table 12. Additive testing of Karasek model: Main effects of demand, support and control ........................................................................84

Table 13. Testing of Multiplicative Karasek model: Main and curvilinear effects, and interaction effects of demands, support and control, having controlled for retained control variables.............................................86

Table 14. Variance explained by Karasek model - Random effects and model deviance statistics for models at each stage of the model building process 88

Table 15. Perceived demand, control and support in wards, crisis teams and community mental health teams across England...........................92

Table 16. Multilevel regression analyses for Maslach Burnout Inventory variables with Demand, Support and Control variables included. ...........94

Table 17. Multilevel regression analyses for Depression-enthusiasm and Anxiety-contentment scales with Demand, Support and Control variables included. 97


6

Table 18. Multilevel regression analysis for Intrinsic Job Satisfaction with Demand, Support and Control variables included ................................99

Table 19. Staff ratings of ward physical environment ...................... 101

Table 20. Physical environment variables significantly associated with morale indicators .........................................................................102

Table 21. Experience of bullying and discrimination in the past year among mental health staff (N=2045) .............................................. 105

Table 22. Experience of threats and violence in the past year among mental health staff (N=2045) ........................................................107

Table 23. Experience of threats, violence, bullying and discrimination in the last year in different service types.............................................109

Table 24. Adverse events in different service types – service managers’ reports ................................................................................. 112

Table 25. Associations between adverse events and burnout variables ... ................................................................................. 117

Table 26. Associations between adverse events and job-related well being variables ............................................................................ 118

Table 27. Associations between adverse events and intrinsic satisfaction ................................................................................. 119

Table 28. Index of multiple deprivation of respondents’ team base (postcode) & catchment area: quartiles, by team type.......................121

Table 29. Case-mix, and number of admissions and mean index of multiple deprivation score for respondents teams, by team type .........122

Table 30. Multilevel models for Maslach Burnout Inventory variables – deprivation and case-mix variables significantly associated with outcome in multi-level model...................................................................... 124

Table 31. Multilevel models for measures of work-related well-being– ward deprivation and case-mix variables significantly associated with outcome in multi-level model .........................................................127

Table 32. Multilevel models for Intrinsic Satisfaction– ward deprivation and case-mix variables significantly associated with outcome in multi-level model ................................................................................. 128

Table 33. Cronbach’s alpha for main scores used to rate organisational context ................................................................................. 130

Table 34. Staff ratings of organisational context.............................132

Table 35. Frequency of training, supervision and personal appraisals by ward type ................................................................................. 134

Table 36. Support and supervision by ward type – service manager reports ................................................................................. 136

Table 37. Leadership on models of care – manager reports..............138


7

Table 38. Associations between organisational variables and Maslach burnout inventory variables ........................................................... 140

Table 39. Associations between organisational variables and job-related well-being ................................................................................. 143

Table 40. Associations between organisational variables and intrinsic job satisfaction ................................................................................. 146

Table 41. Resource use on wards ................................................. 150

Table 42. Final exploratory models – associations with emotional exhaustion: ward and community and ward only samples ..................155

Table 43. Final exploratory models for associations with cynicism: ward and community and ward only samples ........................................... 157

Table 44. Final exploratory models for associations with personal accomplishment: ward and community and ward only samples ...........159

Table 45. Final exploratory models for associations with anxiety-contentment: ward and community and ward only samples................161

Table 46. Final exploratory models for associations with depression-enthusiasm: ward and community and ward only samples .................164

Table 47. Final exploratory models for associations with intrinsic satisfaction: ward and community and ward only samples..................168

Table 48. Reasons for leaving inpatient wards - CMHT and CRT staff .187

Table 49. Characteristics of Leavers’ Survey respondents (n=37) .....188

Table 50. Reasons for leaving: inpatient staff Leavers’ Survey.......... 189

Table 51. Maslach Burnout Inventory change scores over 1 year follow up – individual staff data...............................................................192

Table 52. Stability of burnout scores over 1 year follow-up – individual staff data ................................................................................. 193

Table 53. Change in Warr scale scores over 1 year follow-up – individual staff data ................................................................................. 194

Table 54. Ward staff meeting General Health Questionnaire stressed case criteria: stability over one year follow-up..................................195

Table 55. Proportion of staff with high burnout scores on MBI emotional exhaustion scale – ward level data.................................................. 196

Table 56. Proportion of staff with high burnout scores on MBI cynicism scale – ward level data ................................................................. 197

Table 57. Proportion of staff with high burnout scores on MBI Personal Accomplishment scale – ward level data .......................................... 198

Table 58. Staff meeting GHQ stressed case criteria: ward scores over one year follow-up ....................................................................... 199

Table 59. Self-reported sickness absence: full sample.....................200


8

Table 60. Self-reported sickness absence: wards and community teams. ................................................................................. 200

Table 61. Self-reported sickness absence: ward type ......................201

Table 62. Correlations between self-reported sickness absence and measures of morale...................................................................... 202

Table 63. Descriptive statistics for ward-level sickness and turnover rates at baseline and 1-year follow up. ............................................ 204

Table 64. Correlations between ward-level sickness/turnover and measures of morale: at baseline (retrospective data for turnover and sickness) 205

Table 65. Correlations between ward-level sickness/turnover and measures of morale: at one year follow up (prospective data for turnover and sickness) ..............................................................................206

Table A1. Associations between adverse events and burnout variables.252

Table A2. Associations between adverse events and job-related well being variables..................................................................................... 256

Table A3. Associations between adverse events and intrinsic satisfaction................................................................................................. 259

Table A4. Associations between organisational variables and Maslach burnout inventory variables ........................................................... 260

Table A5. Associations between organisational variables and job-related well-being ................................................................................... 264

Table A6. Associations between organisational variables and intrinsic job satisfaction.................................................................................. 268

Table A7. Maslach Burnout Inventory Emotional Exhaustion domain - ward scores over one year follow-up.......................................................270

Table A8. Maslach Burnout Inventory Cynicism domain- ward scores over one year follow-up ....................................................................... 273

Table A9. Maslach Burnout Inventory Personal Accomplishment domain - ward scores over one year follow-up ............................................... 276

Table A10. Change in Warr scale scores over 1 year follow-up – ward level data ........................................................................................... 279

List of figures Figure 1. Loading plot for the two components derived from principal components analysis of the main morale variables ..............................81


9

Glossary of terms/abbreviations Anxiety-contentment One of the two dimensions of well-being

measured by Warr’s work-related well-being scales

Acute wards General admission psychiatric inpatient units for adults of working age

CAMHS wards Child and Adolescent Mental Health Service wards: inpatient units for children under 18

CMHT Community Mental Health Team: a secondary care community-based service for adults age 16-65 with mental health problems

CRT Crisis Resolution Team: a community-based service providing short-term intensive support to adults experiencing mental health crises

Cynicism A domain of morale measured by the Maslach Burnout Inventory: synonymous with depersonalisation

Depersonalisation A domain of morale measured by the Maslach Burnout Inventory: synonymous with cynicism

Depression-enthusiasm One of the two dimensions of well-being measured by Warr’s work-related well-being scales

Emotional Exhaustion A domain of morale measured by the Maslach Burnout Inventory

Forensic wards Inpatient units for mentally ill offenders

GHQ General Health Questionnaire: a measure of general mental health’/stress

MBI Maslach Burnout Inventory: a measure of “burnout” at work, assessing three domains of morale

Older age wards Inpatient units for older adults, usually with 65 years as the threshold for admission

Personal Accomplishment A domain of morale measured by the Maslach Burnout Inventory

PICU Psychiatric Intensive Care Unit: acute inpatient units providing a higher level of care and containment than general acute wards

Rehab. wards Rehabilitation wards: inpatient unit providing longer-term post-acute care


10

Acknowledgements Major contributions to the successful conduct of the study were made by the other research workers involved: Emma Henderson (University of Warwick, Kate Threapleton (University of Sheffield), Jennifer Betts (UCL), Kathleen Gunn University of Warwick) and Emma Thomson and Charlotte King (University of Bristol). The study received invaluable support from the managers and clinical scientific officers of the Mental Health Research Network North London, South London, East Anglia, South West and Heart of England Hubs. Without their support, a national data collection exercise on this scale would by no means have been possible within the available resources. The analyses in Section 4.3 and some of those in 4.5 were conducted by Dr Chris Stride (Sheffield University), who also oversaw data entry and cleaning. Dr Mai Stafford (UCL) advised on the other statistical analyses. Organisational advice has been obtained for the study from Dr Ruth Belling (South Bank University). Other important contributions to the study have been from other members of its steering group - Professor Scott Weich (University of Warwick), Professor Stephen Pilling (UCL) and Claire Johnston (Camden and Islington NHS Foundation Trust).

Finally, we are extremely grateful to all the clinical staff, managers and patients who generously gave their time to participate in and to help us set up this study.


11

Executive summary

Background

Despite the very large resource invested in in-patient mental health wards, their environment and safety and the lack of contact between staff and patients have been criticised in the UK. Wards are demanding places to work for staff, as they care for people whose needs cannot be met by community services. Staff recruitment and retention is problematic: the use of temporary staff in acute wards is common and costly. The government and many other national and local organisations have been working together to try and improve in-patient care. For these initiatives to succeed, wards need to attract and retain permanent staff who are committed to and satisfied with their work. There is a lack of information from large well-designed studies about NHS inpatient staff morale. Such studies could inform initiatives by service planners to optimise the morale of the inpatient workforce.

Aims

The overall aim of this multi-method study was to examine the morale of the NHS inpatient mental health workforce. Specific objectives were:

a) To describe in-patient staff morale, measured by a cluster of indicators, in a large representative sample of wards.

b) To investigate factors associated with morale. The Demand-Control-Support model, a widely used framework in investigations of job stress, was the starting point. The additional influences of built environment, organisational context, geographical context and clinical population, and adverse events, were investigated.

c) To compare morale between ward and community mental health team (CMHT) and crisis resolution team (CRT) staff and investigate staff’s reported reasons for leaving wards.

d) To investigate prospectively whether good and poor morale persist over time and are associated with staff turnover and sickness rates.

e) To use qualitative methods to elucidate the mechanisms underlying the development of good and poor staff morale.

Methods

The main elements of the study were as follows:


12

Module 1: The 100 ward survey. All staff and ward managers on 100 in-patient wards in 19 Mental Health Trusts in 4 regions were surveyed via questionnaires regarding their morale and potential influences on it.

Module 2: Comparison with community teams. Staff of 19 CMHTs and 19 CRTs completed the same measures of morale and, if applicable, were asked about reasons for leaving in-patient wards.

Module 3: Qualitative investigation of mechanisms underlying good and poor morale. Individual and group interviews were conducted with staff from a range of backgrounds and at varying levels of seniority on 7 wards, including high and low morale wards. These explored how good or poor morale develop and are maintained and the impact of the built environment and ward organisation.

Module 4: Investigation of leavers. As well as surveying CMHT and CRT staff in Module 2, we aimed to elicit reasons for leaving from all staff who left the 100 wards in the following year.

Module 5: Investigation of the persistence of high and morale. One year after the initial survey, repeat questionnaires were sent to a sub-sample of 20 wards with a range of initial morale scores.

Module 6: Prospective investigation of the relationship between morale and staff turnover and sickness. Wards were asked to supply data regarding sickness rates and staff turnover in the year following the Module 1 survey. Because of resource constraints, this took place in only three of the four regions. The relationship between initial morale and rates of sickness absence and staff turnover was explored.

Results

Our findings regarding the above main aims were as follows:

(a) Levels of morale in the inpatient mental health workforce. Most NHS inpatient mental health staff report fairly good job satisfaction and a sense of achievement from their work. Levels of cynicism are low, and scores on two major measures of job-related well-being indicate that enthusiasm predominates over depression and contentment over anxiety, albeit narrowly. Staff reaching the threshold for burnout on the Maslach Burnout Inventory do so most frequently on the emotional exhaustion component of the measure. There were large variations between types of ward in both emotional exhaustion and GHQ score, a general measure of psychological distress. Generic acute ward staff showed most sign of stress: 49% met threshold for burnout on the emotional exhaustion scale and 29% met criteria for psychological distress on the GHQ. On rehabilitation wards,


13

which had the most benign profile, 29% were burnt out on emotional exhaustion.

(b) Factors associated with morale: Individual-level factors were found to have a more important influence on morale than ward-level factors, which typically accounted for only 5-8% of the variation on each morale indicator. Although causal pathways were often ambiguous, some strong associations emerged between morale indicators and other factors.

(i) The Demand-Support-Control model: This model of job strain was largely upheld, particularly in its additive form, which suggests that high demands from a job are associated with psychological strain, but that this is mitigated by having a large amount of control over how the job is done, and by support from peers and managers. Work demands were very strongly associated with emotional exhaustion, but not with personal accomplishment. Job control remained strongly associated with all indicators of morale throughout all analyses. Both support from colleagues and from manager were associated with most dimensions of morale, but associations weakened or disappeared when organisational context variables such as role clarity and team communication were also added to models.

(ii) Organisational context: In addition to the Demand-Support-Control variables, variables relating to role definition (role clarity and role conflict) and to fairness and communication within the team, rated by individual staff, were highly associated with morale. There was evidence that having a Personal Development Plan improved some aspects of morale. Team communication and role clarity tended to be rated as fairly good, but ratings were poorer for fairness and, in particular, voice in influencing senior managers. Only very limited evidence was found of associations between staffing levels or use of agency staff and morale, even though qualitative findings suggest great concern among staff about these.

(iii) Adverse incidents: Nearly a quarter of the sample reported that they had been bullied in the past year and just over half that they had experienced discrimination. The most frequent form of discrimination was on grounds of ethnic background, and 54% of Black African or Caribbean staff reported discrimination, with patients the most frequent source. Staff also experienced high levels of violence: proportion of staff reporting at least one


14

attack in the past year ranged from 45% on rehabilitation wards to 76% on older adult wards. Being bullied and experiencing violent and threatening behaviour were highly associated with poorer morale.

(iv) Built environment, geographical context and ward population: No specific aspects of built environment were associated with morale, but staff perception of the quality of the ward environment was strongly associated with most morale indicators. There was some evidence for poorer morale in the most deprived catchment areas.

(c) Comparison with CMHTs and CRTs: CRTs had a fairly benign profile, with good satisfaction and moderate levels of burnout. CMHT staff showed more evidence of psychological strain than any other group: 60% reached the burnt out threshold for emotional exhaustion, 39% the GHQ threshold for psychological distress. The combination of high demands, low managerial support and very high autonomy at work accounted to some extent for the apparent paradox of high psychological strain together with fairly good job satisfaction among these staff.

(d) Investigation of reasons for leaving: Career development and greater job control were the most prominent reasons for leaving wards reported by staff from CRTs or CMHTs. Our survey of recent ward leavers achieved only a small response: both the wish for greater control and stresses in the ward environment were important reasons.

(e) Persistence of high and low morale: At the level of individual staff, changes on most morale indicators over a year were modest, generally less than half a standard deviation. A tendency was found for initially high burnout wards to show substantial falls in mean burnout level at follow up.

(f) Correlations with sickness and staff turnover: There were modest correlations between morale scores and subsequent sickness rates and turnover.

Conclusions

Even though our study is larger in scope and scale than most that precede it, significant reservations need to be noted regarding our methods and analysis. The study is cross-sectional and exploratory, and chance findings due to multiple testing are a significant risk. We have found a variety of potentially important links, but the causal status of these is often unclear, and reverse causality is often a real possibility. We report here on our initial set of full analyses, but some


15

aspects of our data are yet to be fully explored and models yet to be fully developed.

These reservations aside, some important foci for further research and for policy and service development emerge. Further investigation of morale in CMHTs and the mechanisms that underlie it is a pressing concern. Bullying and violence are prevalent and strongly associated with poor morale, and better ways of addressing these need to be found. Initiatives to improve morale should be tested for effectiveness, as the evidence base is currently slight, and the high importance of control over way of working, role clarity and communication within teams are a potential basis for initiatives to consider whether jobs may be redesigned to improve these and thus optimise staff morale. Voice and fairness appear to be areas where mental health services do not currently perform well: senior managers should consider how this might be addressed.


16

1 Background

1.1 The “crisis in acute care”

Around the turn of the millennium, a “crisis in acute care” (Appleby, 2003) was widely identified in the UK mental health system. Most innovative service development and research in the last decades of the 20th century focused on community settings, even though in-patient services were acknowledged by most to be a necessary part of any mental health system. Recently, the role and functioning of in-patient services have become the focus of greater attention, as by the late 1990s it was increasingly obvious that service users’ experiences of hospital ward stays were often unpleasant and their benefits uncertain. Physical environments were reported often to be poor and opportunities for activity limited, and many in-patients felt unsafe, especially from the threat of violence by other patients (Muijen, 1999; Rose, 2001; Department of Health, 2002). Acute in-patient wards were also criticised for lacking clear therapeutic models and for limited contact between staff and patients (Bray, 1999; Higgins et al, 1999; Quirk and Lelliott, 2001).

The Sainsbury Centre national survey of acute wards, “Acute Care 2004” (Garcia et al. 2005), suggested considerable persisting problems in in-patient care. It highlighted as major current difficulties high staff vacancy and sickness rates, lack of leadership from consultant psychiatrists, poor communication with community teams and limited availability of psychological treatments. The introduction of crisis teams was identified as having resulted in a more disturbed patient population in some wards, increasing demands on staff. A review (Lelliott and Quirk, 2004) suggested that in-patient staff felt they lacked the skills and support they needed to manage patients in ways other than reliance on medication. Department of Health policy is that the NHS should be a ‘model employer’, which values and invests in its staff, allows them flexibility, treats them fairly and introduces innovations which improve their working lives (Department of Health, 2000, 2005). Fulfilment of this aspiration is likely to be very important if current goals for the improvement of the quality of in-patient care are to be attained.

Aspirations to improve this situation have informed a greater focus on in-patient care in national policy (Department of Health, 2002) and a variety of new research, training and service development programmes focused on in-patient care, notably the Acute Care programme led by NIMHE (the National Institute for Mental Health, England) and the in-patient research programmes of the SDO and the Sainsbury Centre for Mental Health. The Star Wards initiative, supported by the National Mental Health Development Unit and


17

apparently adopted by at least 500 wards nationally, encompasses a very substantial and well-marketed set of initiatives for improving the quality of the ward environment and therapeutic activities (Janner, 2007; www.starwards.org.uk). Awards are given to both individuals and teams for implementing its standards. An allied initiative based on a more formally developed set of standards is the Royal College of Psychiatrists’ AIMS (Accreditation for Acute Inpatient Mental Health Services), also now widely adopted as a means of improving quality on inpatient wards. Standards cover a wide range of aspects of inpatient experiences, and are assessed through peer- and self-review. Accreditation for up to 4 years can be obtained, with standards available for both working age adults’ and older people’s wards (Lelliott et al 2006). Probably the simplest of all the recent initiatives to improve quality on wards has been Protected Engagement Time (also known as Protected Therapeutic Time), which simply involves ensuring that there are ring-fenced hours during which the only activity that takes place on a ward is contact between clinical staff and patients (Department of Health, 2009).

1.2 The importance of staff morale

These initiatives are much more likely to succeed if effective and committed staff can be recruited to and retained in in-patient services (Priebe et al. 2005). This is a challenge, as staff shortages have been reported in all the mental health professions (Sainsbury Centre, 2000). The importance of the workforce is acknowledged in various policy documents, including the Department of Health’s (2004) report “The ten essential capabilities”, which assesses staffing and training implications of the National Service Framework for Mental Health and the NHS Plan.

As the systematic review conducted by Cahill et al. (2004) demonstrates, research evidence regarding the morale of the in-patient mental health workforce in England is limited. Their search yielded studies in acute adult, forensic and long stay adult wards. Agreement between the findings of these studies regarding levels of morale was very limited. One methodological limitation was the absence of an agreed definition of morale. Burnout, job satisfaction and overall psychological well-being are the most frequently used indicators, often in combination with proxy indicators such as vacancy and sickness rates. However, none of these is a fully satisfactory indicator of well-being at work and engagement with work. Other important limitations were that sample sizes were often very small, most studies were cross-sectional and confined to one site only and several lacked comparison groups. Thus the authors concluded that high quality research was currently lacking in this area and that larger scale studies of better methodological quality were urgently required. They also defined a set of requirements for such research, including use of well validated measures of morale and examination of a range of potential aetiological factors, including


18

organisational factors, job characteristics, role clarity and workloads. Subsequently Bowers and colleagues (2009) have reported on a much bigger sample of more than 1500 staff on acute wards across the country, surveyed in 2004/5. This found that morale was generally good across the acute ward nursing workforce, with moderate to low rates of burnout on all sub-scales. Limitations of this study are its cross-sectional nature and the fact that it does not include disciplines other than nursing and healthcare assistants and ward types other than general acute wards.

Staff morale is of critical importance to mental health services in several respects. Firstly, the NHS is one of the world’s largest employers, and achieving high levels of health and well-being among its staff is increasingly seen as a very important goal in itself. The recent Boorman review on the health and well-being of the NHS workforce (Department of Health, 2009b) cogently argues that, in an era of increasing emphasis on preventive intervention, the NHS should aim to achieve the status of an exemplary employer, with high levels of health, well-being and positive lifestyle among its employees. A number of specific stresses may results from working in the mental health services: engaging with patients who are often distressed and disturbed may have considerable emotional impact, and managing risk creates stresses both because staff may be at immediate risk of violence or abuse and because the responsibility of preventing adverse events is an onerous one, especially in the instance of major acts of violence such as homicides (Clarke, 2008). Thus the well-being of this part of the workforce is in itself an important concern, and sickness absence figures, which indicate that sickness levels are higher than the private sector and most other parts of the public sector workforce, suggest that NHS workforce health is not currently exemplary (Department of Health, 2009), especially as work-related causes, including depression and anxiety, are also higher than in other sectors.

Staff health and well-being have wider impacts on health service functioning through several mechanisms. Most immediately, the cost to the nation of high rates of sickness is great: the 10.3 million days of sickness absence reported each year cost £1.7 billion per year (Department of Health, 2009b): reducing this is all the more important in the current era of widespread public sector cost savings. In mental health, we have relatively little data that allows us to make direct links between staff well being and patient experiences and service functioning. However, in the wider NHS, the Boorman report describes substantial correlations between patient satisfaction in acute Trusts and staff well-being, measured by stress levels, job satisfaction, injury rates and turnover intentions. An SDO-funded study currently in progress and due to report in 2011 (SDO 08/1819/213, led by Jill Maben at Kings College London - http://www.sdo.nihr.ac.uk/projdetails.php?ref=08-1819-213) will involve a more in depth investigation of the links between staff and


19

patient well-being. In inpatient mental health, there is increasing evidence that therapeutic relationships are key determinants of patient experiences (Johnson et al., 2010): staff attitudes and well-being are very likely to influence these.

Finally a central issue in strategies to improve patient outcomes in mental health is implementation. The growing body of literature investigating and discussing “implementation sciences” (Tansella and Thornicroft, 2009) focuses on the many blocks that exist in translating knowledge about potentially effective interventions to routine clinical practice. Professional attitudes and culture have been identified as a major block to adoption of evidence based practices, again suggesting potentially widespread adverse effects from low staff morale and negative attitudes to work. Measures to reduce the current government budget deficit may also make significant demands on staff morale.

1.3 Factors influencing staff morale

Studies of staff morale have also been carried out in community settings and outside the UK, and these yield some evidence about factors that may influence morale, though many of them also have significant methodological limitations. Various methods have been used, some studies using simple self-report questionnaires that elicit staff views about which aspects of their work are stressful or satisfying, others a wide range of measures of various psychological and organisational concepts. Most studies are confined to a limited range of potential influences on morale. Too much administrative work (Prosser et al. 1997, Onyett et al. 1997; Billings et al, 2003; Priebe et al, 2005), lack of role clarity (Onyett et al., 1997; Carpenter et al., 2003; Sainsbury Centre, 2000), large caseloads (Coffey and Coleman, 2001), poor supervision and management (Sainsbury Centre, 2000; Harper and Minghella, 2001) and the threat of violence (Reid et al., 1999, Sainsbury Centre, 2000) have been identified as sources of stress in UK studies of mental health staff. Bowers’ (2009) acute inpatient ward study reports on one of the most extensive investigations so far reported, with a particular emphasis on relationships between morale and structural and organisational features of acute wards. Working in a deprived area, experiencing higher levels of verbal abuse and lower levels of routine and organisation on wards were among the aspects of staff’s experience found to be associated with higher levels of burnout.

1.4 A framework for investigating staff morale

Thus while a number of investigations have provided pointers to potential determinants of morale, there is still a relative lack of large and systematic investigations that encompass the main potentially relevant factors. In this section, we will discuss some of the main candidates. A natural starting point is the Demand-Support-Control


20

Model, the predominant theory of work stress in occupational psychology. The built environment, organisational characteristics of wards and jobs, adverse events on wards, and the geographical setting and patient population are further potential influences on morale, either via their influence on demand, control and support or as variables that might be independently associated with morale.

1.4.1 The Demand-Support-Control Model

Karasek’s demand and control model of work-related stress, and extensions of it that include support from immediate line managers or colleagues, have been at the centre of well-being theory within work psychology for the past two decades (Karasek, 1979; 1989). The trinity of demands, control and support provides a good starting point for an assessment of the factors affecting staff well-being amongst mental health workers.

Some of the factors commonly associated with mental health settings that have been linked to low staff well-being – such as excessive administrative duties, shift working, high patient turnover, acutely ill and uncooperative patients, violent incidents and drug use on the wards, high patient turnover, and constraints on creating an adequate therapeutic environment – may be subsumed under the more general factors, e.g. high demands, low control or managerial support. Using this model as a starting point then allows us to assess the relevance of constructs that cannot be subsumed under these more generic headings by estimating the additional explanatory power they add relative to Karasek’s triad of concepts. Similarly, the Karasek model can be used as a benchmark for assessing the role of other non-mental health specific factors, including recent managerial initiatives such as the introduction of appraisal or mentoring systems, or more longstanding ones such as training and development. The applicability of the Demand-Support-Control model does not previously appear to have been systematically tested across a range of mental health professions (although Tummers et al. (2001) compared mental health nurses with general nurses in the Netherlands using the Karasek framework).

Research studies, both prior and subsequent to the Cahill et al. report (2004), have infrequently included one or more of support, demands and control, but the range of antecedents to well-being explored has remained wide, encompassing a mixture of mental health-specific measures (e.g. the threat of violence from acutely ill patients), and general factors (e.g. good pay, task clarity). Cahill and colleagues concluded that it was not possible to determine which factors are most likely to increase levels of satisfaction and morale of mental health workers on the basis of the existing studies of inpatient care, though workloads, job characteristics and social support dominated the lists of potential determinants suggested by the evidence so far.


21

Reid et al. (1999), in one of the few qualitative studies comparing community and ward staff, indicated that lack of autonomy was a source of dissatisfaction amongst ward staff, whereas the excessive demands associated with client care were more significant for community staff. Edwards et al. (2002), reviewing evidence from outside the UK, included high autonomy (in decision-making), co-worker support and good supervision (which we can take to mean supportive management) amongst significant factors explaining low levels of stress.

Karasek’s theory is centred on the notion that psychological strain results from the demands of a work situation and the range of decision-making freedom available to the worker facing those demands. His model thus identifies job characteristics as the principal source of distress in the workplace, since it proposes that psychological strain is caused by the combination of high job demands and low job control. The underlying rationale of the model is that workers experience distress when this combination of circumstances exists because they are prevented from formulating effective responses to deal with the challenges of the job. Conversely, low demands and high control are associated with high levels of well-being.

The importance of supportive relationships in organisations is a recurring theme in work psychology; the increasing salience given to bullying illustrates the renewed concern about extreme forms of non-supportive behaviour. Payne (1979) in particular added the concept of support to the demand and control model, and suggested that support, particularly when interpersonal, could reduce the level of adaptive energy needed to cope with high demands under conditions of low control. Karasek incorporated such thinking into his model so that social support buffers or protects the individual against the worse effects of strain; hence, social support and decision latitude buffer the adverse effects of high job demands. Such social support manifests itself in a variety of ways. For example it may help people manage their feelings better so they resolve problems more easily, or offer motivation so that they are reassured that extra effort or persistence with a problem will pay off (Warr, 2007). It may also contribute positively to role clarity.

This demand–control–support model for well-being can be formulated in two ways: initially in an additive form, and then extended to an interactive form. The additive form states that high demands, low control and low social support each cause psychological strain: i.e. the unique (independent) effects of each of the three constructs have a significant impact upon strain. For example, supportive management or colleagues have a beneficial effect irrespective of whether people are facing stressful demands or have limited control.

The interactive form of the demand–control–support model further predicts that control and social support buffer the negative impact of


22

high demands on well-being (i.e. they interact with demands to reduce its negative impact). Under both the additive or interactive forms of the model, we would expect that psychological strain will be greatest given the combination of high demands with low control and low social support (in the demand–control–support model), or conversely that well-being will be greatest when employees have low demands, high control and high support. The Karasek model can therefore be tested in two ways: initially by examining whether job demands, job controls and support independently predict well-being, and then by assessing whether there is a further interactive (i.e. multiplicative) relationship between them.

A further consideration when probing this model is the possibility of non-linear effects of job demands or job control upon well-being. For instance, just as high demands may be overwhelming, or “toxic” to use Warr’s (2007) word, low demands may also be so unchallenging as to create feelings of frustration and monotony. Likewise, just as high control may prove beneficial to well-being, very low control may act positively in freeing employees from a sense of responsibility. And, though increasing support initially benefits well-being, there may come a point where extra amounts of support offer no further benefit. A review by de Lange et al. (2003) of research testing the Karasek model revealed that just under half of the published studies provide support for the additive version, and that the corroboration does not vary with the quality of the study. It also concluded that additional interactive relationships are rare. Another review confined to the longitudinal studies, Van der Doef and Maes (1999) showed there was considerable support from these for the additive model, but again – partly because it is infrequently tested, and no doubt also often under-powered and subject to a greater debilitating effect of measurement error – less convincing evidence for the interactive model. These reviewers concluded that the use of specific measures of control that correspond directly to the demands in the jobs being studied is more likely to yield results that support the buffering role of control. Subsequent studies with more focused measures have also failed to find strong interaction affects, and the purely additive model looks the more robust (Warr, 2009). Nonetheless, a general survey of employees in Britain using a two-item measure of demands did offer support for the interactive demands and control model of job satisfaction and contentment–anxiety(Wood, 2008).

1.4.2 Other types of variable which might be associated with morale

Previous investigations and discussions have identified several other types of variable that may impact on staff morale. An aim of the current study is to investigate how far these are in fact associated with morale and, if they are associated, how much these relationships may be subsumed under the Demand-Support-Control model and how far they are independent of this model.


23

1.4.3 The built environment

Public rhetoric, and a long fictional tradition, have demonised the environments of psychiatric hospitals. The great optimistic era of asylum building in the mid 19th century gave way to squalor, overcrowding and degradation. Asylum populations peaked in the mid 20th century. The advent of community care, better treatment, closure of old asylums and building of new psychiatric units in new settings like general hospitals or in the centre of towns has had some effect in changing the image of psychiatric care. Despite ongoing bed closures, many thousands of patients are treated in inpatient facilities at any time.

Some research has touched on the experience of patients in these units. This research assumes that a patient’s experience is more than the effect of separation from stresses, containment of risks, and empirical treatments like psychotropic medication and (when available) manualised psychotherapies. Studies have shown an effect on patient recovery of the physical environment of care. This applies in surgical, medical and psychiatric settings. The limited research in psychiatric settings has often examined the effect on patients of refurbishments of psychiatric facilities, relocation of such facilities, and surveys of patients’ opinions about inpatient care. Research on the staff experience of the physical environment is even less common. Psychiatric nurses form the majority of staff and spend an average of 40 hours a week in these settings, often an intense and enclosed environment. The physical environment is very likely to have an effect on staff – existing research confirms that substantial improvements to units can improve staff absence rates, a key proxy for motivation. Modest improvements in ward facilities have also been shown to increase staff-patient interactions. Other aspects of the built environment reported to be associated with morale include space, lighting, provision of family spaces to allow families to participate in patient recovery, reduced noise, smaller units and easier sight lines.

Work to date has been limited by two main design flaws: 1) the lack of suitable tools to describe the built environment in such settings; and 2) research designs which have been unable to separate out the effect of the built environment from other influences on patient, service and staff outcomes. The current study provides an ideal opportunity to investigate the influence of the built environment on staff morale in UK psychiatric inpatient wards.

1.4.4 Bullying and adverse events

Exposure to violence and the threat of violence may be particularly pertinent to morale among staff on acute inpatient wards. Research from the National Audit Office (2003) reports that in 2001-2002 there were 95,501 documented incidents of violence and aggression against NHS staff. In 2005 the Healthcare Commission published


24

findings from a survey of 265 mental health and learning disability wards in England and Wales. 78% of nursing staff and 36% of service-users reported having experienced violence. 25% of staff reported feeling insufficiently trained in the management of violence and aggression. Reductions in hospital bed numbers may mean that acute care wards now see a more severely unwell client group, with many patients detained under section (McGeorge et al. 2000). There is some concern that this is increasing the risk of violence (Nolan et al. 2001; Foster et al. 2007). While efforts are now being made at national, local and ward levels to address the issue (McGeorge & Rae, 2007), it would seem there is still much to be done. Cowman & Bowers (2009) compared risk management procedures in acute wards in Ireland and London. Overall, they found coherent safety and security policy procedures to be lacking. They stress the need for EU-wide guidelines on best practice and minimum standards for safety and security.

Both verbal and physical aggression threaten the wellbeing and morale of staff and increase sickness absence (Wykes, 1994; Whittington et al. 1994;1996; Brennan 2000). Common psychological responses to assault include anger, anxiety, guilt and symptoms of PTSD (Needham 2005). A recent study in a US paediatric hospital compared levels of psychological distress in those who had been assaulted within the previous 6 months and those who had not (Ryan et al. 2008). The former reported higher anxiety, feelings of vulnerability and work impairment, and were more likely to contemplate leaving. Another study by Richter & Berger (2006) assessed for symptoms of PTSD in 46 victims of assault from nine hospitals. 17% met criteria for PTSD immediately after the assault and 11% at 4 month follow-up. 74% of 106 emergency department nurses reported impaired job satisfaction following assault, with 38% considering job change (Fernandes et al. 2002).

In the UK and Republic of Ireland, security maintenance falls largely to the nursing staff (unlike in the US and Canada, where hospitals employ security personnel). A review of the international qualitative literature on the experiences of staff and patients on acute psychiatric wards suggests that staff are preoccupied with the management of patient aggression (Lelliott & Quirk, 2004). This may lead staff to experience role conflict, as the primary therapeutic element of their work is jeopardized by the need for force (Rask & Hallberg, 2000).

Research from general health settings regarding workplace bullying may also be relevant to inpatient mental health wards. Workplace bullying is known to be associated with poor physical and psychological health (Quine, 1999; Johnson, 2009. Victims of bullying are at increased risk of burnout (Sa & Flemming, 2008) and symptoms of depression (Kivimaki et al. 2003), PTSD (Matthiesen & Einarsen, 2004) and sleep disturbance (Niedhammer et al. 2009). In a longitudinal study involving 4647 participants followed up over 5


25

years, Hogh et al. (2005) found that aggressive teasing at baseline was a significant predictor of psychological health problems at follow-up.

Bullying is also associated with poor job satisfaction and elevated rates of sickness absence (Quine, 1999; Kivimaki et al. 2000) European studies suggest that bullying is particularly common within the healthcare professions (Zapf et al. 2003). Alexander et al. (2000) carried out a large-scale survey with over 2000 staff of a Scottish NHS Trust. Overall, 16% reported having been bullied, 18% had suffered discrimination, 22% had experienced harassment, and 13% had been victimized. These experiences often went unreported. Hauge et al. (2007) found that bullying is more prevalent in stressful work environments characterised by role conflict, interpersonal conflict and avoidant leadership. There is evidence that bullying fosters reciprocation, creating the danger of a bullying culture (Lee & Botheridge, 2006). The large literature on “lateral aggression” amongst nurses suggests there may be some reality to this notion (Jackson et al. 2002; Hutchinson et al. 2006 Robertss et al. 2009).

1.4.5 Organisational factors

The Karasek model focuses on the characteristics of employees’ core job, the demands it makes on them, and how much discretion they have in meeting these demands. The support component of the triad in particular encapsulates some key organisational characteristics of wards. A number of further key aspects of the organisational context in which people’s jobs are set are likely also to influence directly their well-being. Those which we examine in the current study are (a) the definition of the core job and the extent of role conflict and clarity resulting from this (b) the functioning of the team – this is to some extent encapsulates the peer and manager support concepts, but includes also how effectively the team work together and communicate (c) key human resource practices, especially training, supervision and appraisal; (d) the extent of employee involvement or voice; and (e) the extent of procedural and substantive fairness.

a) Role clarity and conflict

Role theory indicates that individuals will become more stressed, more dissatisfied and less effective if the behaviours expected of him or her are inconsistent and they experience role conflict (Rizzo et al, 1970). Role conflict is also heightened when the direction and clarity of ‘chain of command’ are unclear (Rizzo et al, 1970). Role clarity can be understood as the degree to which employees have the information they need about how they are expected to perform their jobs and is the antonym of role ambiguity (Teas et al., 1979). Role clarity is influenced by task-related and supervisor-related factors (Jackson and Schuler, 1985). Role ambiguity and role conflict have been shown to be factorially identifiable and independent work-related factors in organisations (Rizzo et al, 1970).


26

b) Team cohesion and functioning

Team cohesion is a factor connected to group performance, or team functioning, which has been defined as "an individual's sense of belonging to a particular group and his or her feelings of morale associated with membership in groups" (Bollen & Hoyle, 1990, p. 482). Cohesion has previously been more likely to be used in the study of military (Siebold, 1999) or sport related teams or units (Carron et al, 2002). Whilst it can be seen as an attribute of groups, individual members' perceptions of unit cohesiveness have been postulated as being of importance; sense of belonging and morale have been studied as cognitive and affective dimensions of perceived cohesion respectively. Within healthcare, work group teamwork, necessitating cohesion, has been proposed to lead to higher job satisfaction and staff retention (DiMeglio et al, 2005; Kalisch, 2007).

Psychological research has looked at the impact of team cohesion on performance in contexts such as business innovation (Llorens Montes et al. 2005) and the military (Reuven et al. 1986). As well as directly improving performance output, group cohesion is thought to have indirect positive effects through its influence on morale (e.g. Bartone & Adler, 1999). In some cases, team cohesion is actually conceptualised as part of morale. Staff in inpatient and community mental health settings work as part of multi-disciplinary teams. Relatively few studies seem to have looked empirically at the impact of teamwork on the morale of healthcare staff. Sikorska-Simmons (2008) surveyed staff working in supported accommodation for the elderly and found that perceptions of unit morale and better interpersonal relations were associated with lower intragroup conflict and less anticipated turnover. DiMeglio et al. (2005) assessed the impact of a 12-month team building intervention for nursing staff in a general hospital. The programme led to reduced turnover rates and improvements in group cohesion, interpersonal relationships and job satisfaction. Sexton et al. (2000) note that in highly stressful environments, an individual’s breadth of vision narrows, which may thwart effective communication and teamwork. This may be relevant to the potentially stressful environment of inpatient mental health wards. Research has also brought attention to the implications of hierarchies and internal politics on effective team functioning (Singh, 2000).

c) Training, supervision and appraisal

Clinical supervision has been described as ‘a formal process of professional support and learning which enables practitioners to develop knowledge and competence, assume responsibility for their own practice and enhance consumer protection and safety of care in complex situations’ (Department of Health, 1993). Whilst there is no unitary model of format, process or content, it fits well with other concepts such as Clinical Governance and Continuing Professional Development, and is a vehicle for providing support, clinical teaching,


27

training and guidance and promoting the delivery of safe and good quality services. Its key functions have been described as educational, managerial/administrative and supportive (Kadushin, 1992) or alternately as formative, normative and restorative (Proctor, 1986), although they are useful in similar ways (Bond & Holland 1998).

The main aim of appraisal in the NHS appraisal is to identify personal and professional development needs in relation to good clinical practice and standards of competence, care and conduct (Department of Health, 2010). Some appraisal is undertaken along professional lines, e.g. following the General Medical Council's document Good Medical Practice (General Medical Council, 2001), whilst for professionals other than doctors and dentists, it may be linked with the NHS’s Knowledge and Skills Framework (KSF) (Department of Health, 2004), which defines competencies, in terms of the knowledge and skills required to deliver quality services, providing a framework for reviewing staff competencies and development needs. Appraisal is therefore essentially linked with staff having personal (or professional) development plans. The importance of both, alongside formal and informal training is acknowledged by their inclusion in the National NHS Staff Survey (Care Quality Commission, 2008).

d) Perceived fairness and voice in the workplace

Procedural justice can be understood as the fairness of procedures used to resolve disputes and determine how resources are distributed. Distributive justice is the fairness of the resulting outcomes (1991). Fairness has been identified as an antecedent to perceived organisational support, thereby having an effect on Organisational Citizenship Behaviour (Moorman et al, 1998). Perceived employer fairness may be a basis for employee job satisfaction (Moorman et al, 1993).

The term ‘voice’ is increasingly used as a concept that captures the various ways in which employees, directly or through representatives, have opportunities to feed information and ideas into managerial decision making and to be consulted about or negotiate over decisions (Wood and O’Creevy, 2005). It can be linked to procedural justice (Moorman et al, 1993).

1.4.6 Clinical population and geographical context

A further set of potential relationships with morale is with the geographical/social context of the ward and clinical population, including the type and quantity of patients cared for by the team, as well as features of the catchment area, especially social deprivation. Previous studies have suggested that factors such as larger case-loads, lower staff to patient ratios (Edwards et al. (2002) and greater complexity of patients might predict lower staff morale (Coffey and Coleman, 2001). In addition, higher levels of violence among


28

patients and increased levels of substance misuse on a ward have been identified as potential threats to the morale of inpatient psychiatric staff (Reid et al., 1999, Sainsbury Centre, 1998; 2000; 2004). Clinically, there is clear validity to the theory that inpatient staff working with the most complex and demanding patients might show higher degrees of burnout. One of the aims of our study was to examine this possibility.

1.5 Defining and understanding morale

Thus far we have followed Cahill et al. (2004), the SDO-commissioned review that preceded our current work, in treating morale as equivalent to well-being, or as an umbrella term for well-being states. The studies discussed above are therefore studies of stress, job satisfaction and burnout. We are not aware of any studies on the healthcare workforce that have measured morale directly. More generally, whilst there are validated measures of well-being concepts, there are no validated measures of morale.

To produce valid and generalisable results, we have needed in the current investigation to use measures with well-established psychometric properties, preferably also offering comparability with other relevant investigations. Two main instruments have been thus been selected as the main basis of the study, supplemented by a measure of intrinsic job satisfaction. One of these main measures is a measure of job-related well-being that has been used relatively little in mental health services research, but is a very widely used measure in work psychology. Warr (1990, 2007) conceptualises job-related well-being in terms of three dimensions: dissatisfaction to satisfaction, anxiety to contentment (or comfort), and depression to enthusiasm. His model of well-being is based on what is known as the circumplex model of affect (Russell 1980), that describes it in terms of two orthogonal dimensions of pleasure and arousal, derived from the more general models of emotions of Watson and Tellegren (1985) and others (Feldman et al 1999, Remington et al. 1999). Pleasure relates to emotional feelings about whether one is feeling good or bad about one’s job or aspects of it. As such, it is independent of arousal, since arousal may provoke positive or negative feelings. Mental arousal ranges from activation to deactivation and includes varying states, from feeling alert to sluggish, calm to tense, contented to anxious, depressed to enthusiastic.

Positive ends of the continuum in both the anxiety–contentment and depression–enthusiasm dimensions are identified by a state of high pleasure or positive affect. But their negative ends are differentially related to arousal. Anxiety entails high arousal and depression entails low arousal. The traditional emphasis on job satisfaction measures only the pleasure dimension – the extent of pleasure that one gains from one’s job. The job-related well-being scales from which we


29

report results are based on these two major dimensions of well-being.

The second main instrument on which we report is the one most frequently used in mental health services research investigations regarding staff, the Maslach Burnout Inventory, which, like the depression-enthusiasm and anxiety-contentment scales, has been used in several tests of the Karasek model, including in samples of social workers and construction workers. Maslach’s concept of burnout is seen as especially applicable to the caring professions (Landsbergis et al. 1992). The overall term refers to the experience of long-term exhaustion and diminished interest, but Maslach identified three dimensions. The core factor is that of emotional exhaustion, which “refers to feelings of being emotionally overextended and depleted of one’s emotional resources” (Maslach 1998, p. 69). The measures of it in Maslach’s inventory cover tension, anxiety and other factors that are mainly, but not exclusively, related to the anxiety–contentment dimension in Warr’s terms. The other two dimensions of burnout are a reduced sense of personal accomplishment and cynicism (depersonalisation in the original formulation, but in this report we prefer the more recently used description of this dimension as cynicism as both more informative and less likely to be confused with the other way in which the term depersonalisation is used in mental health). Personal accomplishment refers to feelings of low self-efficacy and competency, and is associated negatively with depression and positively with active participation in job-related decision-making; hence, it is related to the depression–enthusiasm dimension in Warr’s terms. Cynicism refers to the extent to which workers become distant from and cynical about their work. In the case of human service work, it is particularly reflected in the extent to which workers are critical of their clients or customers.

Pragmatic considerations readily justify equating morale with these two major measures, but have we lost sight of any important aspects of the concept of morale in doing so? Personal morale is an individual-level variable. Yet the concept of morale also continues to be treated as collective term, as it originally tended to refer to the morale of a group, particularly in the military context. It is often assumed to something that is shared by a group and reflects their communal experiences, which in the military context would often dominate individual’s experience. The daily experience of men in trenches in the First World War would, for example, be considerably more homogeneous than those in modern office or even mental health unit. Motowidlo and Borman’s definition of morale (based on a review of prior definitions captures this collective notion: “morale... might be defined as a psychological state shared by members of a group that consists of general feelings of satisfaction with conditions that have impact on the group and strong motivation to accomplish group objectives despite obstacles or adversity” (Motowidlo and


30

Borman 1978). However, this definition does not adequately differentiate between personal and team morale. Members of groups may share the sense of having the same levels of, both personal and team morale. The referent of the morale in the first case is the individual, his or her psychological well-being whilst in the second, team case it is the group and refers to feelings about the team’s cohesion and motivation to accomplish group objectives in Motowidlo and Borman’s terms. It is not inevitable that the same levels of each are shared by all members of the group.

Aggregating the personal morale measures of each individual member of a group will produce a team-level concept, but this may not be equivalent to the team’s morale. It is also likely that where communal experiences may not dominate the interactions in work places that the level of agreement between individuals in the same workplace may be not that high. This would mean that it might not statistically be legitimate to aggregate up to the group level. In contrast, if team morale is an authentic team level concept we would expect group members to have similar levels of team morale. A validation of the two concepts of morale would therefore entail assessing that they are discrete, that is their discriminate validity, as well as their association with assumed antecedents and behaviours.

While for most purposes, we have equated morale with the major individual-level measures of job-related well-being and burnout, we do also aim in this study to make some steps towards a greater understanding of the concept of morale. Alongside Maslach’s and Warr’s instruments, we have also measured general psychological distress using the GHQ-12 (Goldberg and Williams 1998), various dimensions of job satisfaction, and job involvement, the extent to which people identify with their work as a central aspect of these lives – these have all been used in previous investigations as indicators of morale. We explore how the patterns of morale as measured by these different indicators compare with each other, and how far the measures are correlated with one another. In the absence of measures directly operationalising the concept of morale, we decided to elicit from staff simple one item ratings of their own and the team’s morale, and to explore how far staff agree regarding the morale of the team as a whole and how far these measures correlate with the various proxy indicators of morale used in previous work. Finally, we explore how far ratings of individual and team morale and the various related indicators we have measured might be reducible to a simper set of underlying components.

These elements in our investigation are novel in mental health services research and should thus enhance understanding of the concept of morale and its measurement. A further limitation in understanding of morale identified in previous literature (Cahill et al., 2004) which we will address is that a very high proportion of investigations have been cross-sectional only. Thus we know


31

relatively little about the extent to which personal morale, or aggregate morale within a service, tends to remain stable over time.


32

2 Aims The currently available evidence on levels of morale among in-patient staff in England is limited, the evidence on factors which influence morale even more so.

The aims of the current proposal are:

a. To describe in-patient staff morale, measured by a cluster of indicators, in a representative sample of in-patient wards in England.

b. To investigate which factors are associated with variations in morale, using the Demand-Control-Support model as a starting point.

c. To compare morale between ward and community mental health team (CMHT) and crisis resolution team (CRT) staff and to investigate staff’s reported reasons for leaving wards.

d. To investigate prospectively whether good and poor morale persist over time and are associated with staff turnover and sickness rates.

The specific objectives are:

a. To conduct a survey of burnout, job satisfaction, well-being, team identification and job involvement, vacancy rates, staff turnover and sickness rates among staff on 100 in-patient wards in 19 Trusts within 4 regions.

b. To collect data in these 100 wards regarding demands on staff (including staffing ratios, casemix and bed occupancy), their control over their work (including autonomy and involvement in decision-making), and the support they receive (including clinical supervision and training, support from managers and colleagues, organisational climate), as well as the ward’s physical environment and adverse experiences among staff.

c. To collect data on morale from a comparison sample of 20 CMHTs and 20 CRTs.

d. To investigate reported reasons for leaving in-patient work among staff members on the 100 wards who leave over the year after the initial survey and among staff in the CMHTs and CRTs who previously worked on wards.

e. To measure morale again one year after the original survey in a sample consisting of the staff on 20 wards, selected to yield a range of initial scores on indicators of morale

f. To examine staff turnover and sickness rates on the 100 wards over the year following the initial survey and investigate how far indicators of morale predict these.


33

3 Methods The study was conducted in two phases. Phase 1 comprised a survey of staff morale in 100 inpatient wards and 38 community teams. Phase 2 included an in depth qualitative exploration of the mechanisms that may drive morale in a smaller number of wards sampled purposively based on data from Phase 1. Phase 2 also included 1 year follow up to investigate the persistence of high and low morale, its relationship to staff sickness and turnover, and inpatient staff’s reasons for leaving wards. Multicentre research ethics approvals were obtained: one application for Modules 1, 2 and 4 to 6 and a separate application for Module 3 were approved, and research governance approval was also obtained from each participating Trust.

An overview of the study is provided in Table 1.

Modules 1 and 2 used similar measures, procedures and analysis: their methods are therefore described together.

3.1 Modules 1 & 2: The 100 ward survey and Comparison with Community teams

3.1.1 Setting

Four universities (University College London (UCL), Warwick, Bristol and Sheffield) and three Hubs of the Mental Health Research Network (Heart of England, North London and West) were the bases for this survey, allowing inclusion of a wide variety of catchment areas. A total of 19 NHS Trusts were involved (1 fewer than originally planned because of the very large size of some merged Trusts – the CMHT and CRT sample is correspondingly reduced).

3.1.2 Sample

Data were collected from 100 wards. Questionnaires were sought from all clinical staff and the ward manager at each ward. This large sample was required in order to be able to model the associations with morale of a large number of potential aetiological factors. We included the maximum number of wards at which we anticipated we could obtain a high response rate given available resources. We sought to include 50 general acute wards and 50 wards divided evenly between the main sub-specialties (older adults, children and adolescents, forensic, rehabilitation and psychiatric intensive care units).

© Queen’s Printer and Controller of HMSO 2011 34 Project 08/1604/142

Table 1. Overview of the study

Description Setting and sample Measures

Module 1

The 100 ward survey

Questionnaires with ward staff and managers assessing indicators of morale and potential influences on morale. Ratings of ward design and details of the catchment area were collected by study researchers.

All inpatient ward staff and managers in 100 inpatient wards, within 20 NHS Trusts within 4 regions (North London, Birmingham/Heart of England, Bristol/South West, Sheffield)

Staff questionnaire_ward staff

Managers’ questionnaire_ward managers

Ward Design Questionnaire

Module 2

Comparison with community teams

Questionnaires with clinical staff and managers from Community Mental Health Teams (CMHTs) and Crisis Teams (CRTs) assessing indicators of morale and potential influences on morale

All clinical staff and managers in 1 CMHT and 1 CRT from each of the 20 NHS Trusts participating in Module 1

Staff questionnaire_commuity staff

Managers’ questionnaire_CMHT managers

Managers’ questionniare_Crisis Team managers

Module 3

Qualitative study

In selected teams, focus groups, individual interviews and analysis of ward policy and procedures were used to explore in depth mechanisms underlying morale

The sample was purposively drawn from 10 ward teams: 5 from the top 25% on indicators of morale from Module 1 questionnaires and 5 from the bottom 25%. The 10 participating wards were drawn from two regions: the Heart of England and North London.

At each ward, 2 focus groups were conducted with

Focus groups interview schedule (Appendix 1)

Staff interview schedule (Appendix 1)

Manager interview schedule (Appendix 1)


35

staff, 3 staff, 1 senior manager and 3 patients were interviewed and organisational and architectural analyses were conducted

patient interview schedule (Appendix 1)

Organisational analysis checklist (Appendix 1)

Module 4

Leavers’ survey

A questionnaire with staff who leave inpatient wards explored their reasons for leaving

Attempts were made to contact all staff leaving the 100 wards participating in Module 1 within 1 year of the initial survey. Questions were also included in the Module 2 questionnaire for CMHT and CRT staff, eliciting from those who had previously worked on inpatient wards their reasons for leaving.

Leavers’ questionnaire

Questions on reasons for leaving in Module 2 questionnaire

Module 5

Persistence of high and low morale

An abbreviated version of the Module 1 questionnaire exploring staff morale was repeated at 1 year follow-up to explore the persistence of morale over time.

Questionnaires were distributed to all staff from 20 wards which participated in Module 1, purposively sampled from North London and the Heart of England to ensure a range of types of ward and levels of morale

An abbreviated version of the Module 1 questionnaire was used.

Module 6

Staff sickness and turnover

The relationship between ward team morale, measured in Module 1, and staff sickness and turnover during the following year was investigated.

Staff sickness and turnover data were gathered from all 100 wards participating in Module 1.

Retrospective reports of sickness absence were also obtained from Module 1, allowing individual level analysis of the relationship between morale and sickness over the preceding year.

Staff sickness and turnover questionnaire


The community teams survey (Module 2) was conducted in Community Mental Health Teams (CMHTs) and Crisis Resolution Teams (CRTs) within the same NHS Trusts involved in Module 1. One CMHT and one CRT were selected from each participating NHS Trust, providing a sample of 19 CMHTs and 19 CRTs.

Inclusion criteria for the sample were relatively liberal, with all staff involved in direct contact with patients for purposes of care included, together with managerial staff based on the ward. Staff whose responsibilities were purely domestic or administrative were excluded. It should be noted that this led to the inclusion of staff from a range of disciplines, including some based solely on the ward, especially the core nursing establishment responsible for making up numbers on shifts, and others who might also identify themselves with differently constituted teams. For example, doctors included in the sample may in fact have been working at a number of sites, including both ward and community services, and occupational therapists or psychologists might in fact see their primary allegiance as to the hospital occupational therapy or psychology department.

3.1.3 Measures

The following measures were used, available from the authors subject to copyright restrictions.

a) Staff questionnaire_ward staff

The questionnaire for ward staff included i) indicators of morale; and ii) factors associated with morale., as well as demographic and occupational details. We largely used standardised and validated instruments, although where no appropriate published instruments were identified, measures were developed by the study team, drawing on previous work and consultation with stakeholders and experts. The following were the main contents of the questionnaire for ward staff. The initial draft of the questionnaire was piloted with 45 staff in 3 inpatient wards and 2 community teams, drawn from the London and Warwick centres. These staff were invited to comment on the questionnaire as well as to complete it, and various modifications were subsequently introduced. The following were the main contents of the questionnaire for ward staff.

A. Demographic and occupational details

Using the formats of the NHS Staff Survey wherever possible, staff were asked structured questions to elicit (a) socio-demographic details, including age, sex, ethnic background, where born, whether they have dependants, how far they need to travel to work, and what their marital status is; (b) their profession, length of services in mental health services on their current ward, working hours, seniority and shift pattern.

B. Measures of morale


37

(i) Maslach Burnout Inventory: Maslach’s (Maslach 1998) concept of burnout is examined by 22 items formed by three subscales: emotional exhaustion, personal accomplishment and cynicism (this term is adopted in our investigation in preference to the initial term depersonalisation, as discussed above). Levels of emotional exhaustion are measured by a nine-item subscale based on asking how often the respondent feels the following states: “emotionally drained from my work”, “used up at the end of the working day”, “fatigued when I get up in the morning”, “burned out from my work”, “frustrated by my job”, “like I’m at the end of my tether”, “working too hard on the job”, “working with people involves too much stress”, and “working with people all day is a strain”. Personal accomplishment is similarly designed as an eight-item subscale, asking about the extent to which the respondent: “can easily understand patients’ feelings”, “deals effectively with the patients’ problems”, “positively influences people's lives”, “feels very energetic”, “can easily create a relaxed atmosphere”, “feels exhilarated after working with patients”, “has accomplished worthwhile things in job”, and “deals with emotional problems calmly”. Finally, the cynicism subscale consists of five items: respondents were asked the extent to which they “treat patients as impersonal objects”, “become more callous toward people”, “worry that the job is hardening emotionally”, “don’t really care what happens to patients”, and “feel patients blame me for their problems”. For all three subscales, the response coding ranges from 0 = “never” to 6 = “everyday”, with 3 = “a few times a month” as the mid-point. We have followed other recent authors (Lasalvia et al. 2009) in adopting the term Cynicism rather than Depersonalisation. Depersonalisation is a confusing term to use to describe a component of burnout in a mental health context as it has a quite distinct psychopathological definition that is better known to those working in mental health. Cynicism seems to us a better description of this component of burnout, and we have therefore adopted this term throughout.

(ii) The Job-Related Well-Being Scale (Warr 1990) allows staff to report how their work makes them feel. It was used in its 10 item version and. It generates scores on two main subscales, conceptualised by Warr as distinct dimensions on which well-being at work varies: an anxiety-contentment axis, on which high scores represent states in which contentment predominates over anxiety and low scores the reverse, and a depression-enthusiasm scale in which enthusiasm predominates at high scores and depression at low scores. The scale can also be used to generate single pole measures of severity of depression and anxiety.

(iii) Job satisfaction: We used a combination of items from the 2004 Workplace Employment Relations Survey (Kersley et al. 2004) and NHS National Staff Survey (Healthcare Commission, 2006) to cover the ground we wished to include regarding satisfaction. This


38

generated a number of satisfaction sub-scores, all on five item scales, of which Intrinsic Satisfaction appeared the most important and psychometrically meaningful. It included satisfaction with aspects of their job such as “the sense of achievement I get from my work” and “the opportunities I have to use my abilities”.

(iv) The General Health Questionnaire – 12 item version (GHQ-12). (Goldberg and Williams 1988): This 12-item measure is very long established as a brief measure of overall psychological ill health. Items in the scale generate include measures of depression, anxiety, suicidal ideas, happiness and sleep disturbance over the previous 4 weeks. Each item is scored from 1 to 3, and a total of 4 or more is used as the threshold for indicating stress/potential psychiatric morbidity.

(v) The Job Involvement Scale (Tummers 2001): This scale identifies the extent to which staff identify with their work and feel committed to it.

(vi) Ratings of own and team morale: In the absence of a validated direct measure of morale, staff were asked to rate, using a five point scale, their own current morale and that of the team.

C. Job characteristics

The measures of the main components of Karasek’s triad were measured using the items designed by Haynes et al. (1999) to be applicable to health workers.

(i) Demand was measured by seven items asking respondents how often they met each of the following problems in carrying out their work: “I don't often have enough time to carry out my work”; “I cannot meet all the conflicting demands made on my time”, “I never finish work completing everything I should have”, “I am asked to do work without adequate resources to complete it”, “I cannot follow best practice in the time available”, “I am asked to do basic tasks stopping me completing more important ones”, and “varying levels of demands on my time”. Respondents answered the question on a five-point response coding, ranging from 1 = “not at all” to 5 = “a great deal”, with 3 = “moderate amount” as the mid-point.

(ii) Control was again measured by Haynes et al.’s six-item measure of this construct; the questions asked were to what extent the respondent could “determine the methods and procedures I use in my work”, “choose what work I will carry out”, “decide when to take a break”, “vary how I do my work”, “plan my own work”, and “carry out my own work in the way I think best”. In addition, an item that we designed on patient interaction was included: “To what extent do you choose how you interact with patients?” giving a seven-item scale. The response coding employed was the same as that for demand.


39

(iii) Ward or team manager support was based on three questions about the extent to which the individual can count on their ward or team manager to “listen when I need to talk about problems at work”, “help me with a difficult task”, and “provide effective leadership for the ward or team”. The five-point response coding used was 1 = “not at all” and 5 = “completely”, with 3 = “to a moderate extent” as the mid-point. For ward managers themselves, ‘your immediate manager’ was substituted for ward or team manager. Support items were also derived from Haynes et al. 1999.

(iv) Colleague support was measured by a four-item scale based on the degree to which the individual can count on colleagues to “listen when I need to talk about problems at work”, “help me with a difficult task”, “back me up at work”, and “help me in a crisis situation at work, even though they would have to go out of their way to do so”. The response categories were as for manager support.

D. Organisational context

Individual level variables reflecting organisational context were as follows:

i. Role clarity. The role clarity scale measures the extent to which individuals are clear about their work role (Haynes et al, 1999). Staff are asked to what extent they agree or disagree with the following five statements: I have clear planned goals and objectives for my job; I know that I have divided my time properly; I know what my responsibilities are; Explanation is clear of what has to be done; I know exactly what is expected of me. The response scale was amended to strongly disagree/ disagree/ neither agree nor disagree/ agree/ strongly agree in consultation with pilot study subjects and to bring responses in line with items from the other scales.

ii. Role conflict. The role conflict scale measures the extent to which individuals receive conflicting instructions from others about their own work requirements (Haynes et al, 1999). It asks ‘How often do you find these issues arising in carrying out your job?’ in relation to the following four statements: I receive conflicting instructions from two or more people; Professionals make conflicting demands of me; Managers make conflicting demands of me; I do things which are accepted by one person, but not by another. The response scale varied between not at all/ just a little/ moderate amount/ quite a lot/ a great deal.

iii. A further four questions were asked about Team Conflict, adapted from Jehn (1997). Items included the extent to which staff in the team disagree regarding the work to be done, how far there are personality conflicts in the team and how far there is tension among members of the team.

iv. Team identification: The Team Identity Scale (Priebe et al, 2005) uses 13 items to measure the extent to which staff identify


40

with and feel involved in the work of the team to which they belong, again rated on a 5 point scale.

v. Communication and decision making in the team. Measures were based on those used in the ECHO study (Burns et al. 2007). Participants were asked how effective they found: Decision making structures within the team; Communication within the team; Communication between team members and team leaders/managers; Communication between the team and senior management; Communication between the team and outside agencies. The response scale was very effective/ effective/ neither effective nor ineffective/ ineffective/ very ineffective.

vi. Perceived fairness and voice in the workplace. An amended version of the Substantive and Procedural Fairness scales developed by Moorman and colleagues (1991) was used. Participants were asked whether they strongly disagree/ disagree/ neither agree nor disagree/ agree/ strongly agree with the ten following statements: My work schedule is fair; I think my level of pay is fair; I consider my workload to be fair; Overall the rewards I receive here are fair; Decisions that affect my job are made by Senior Trust management in an unbiased manner; Senior Trust management makes sure all employees’ concerns are heard before making decisions that affect my job ; In making decisions that affect my job, Senior Trust management collect accurate and complete information; Senior Trust management clarify decisions and provide additional information when requested by employees; All decisions that affect jobs are applied consistently across all affected employees; Employees are allowed to challenge or appeal decisions made by Senior Trust management.

In relation to Voice, participants were asked, first, to what extent employees on their ward have a chance to put forward their views to Senior Trust Management when a change is introduced that affects the way they do their jobs and then to what extent employees’ views then influence Senior Trust Management’s decisions? Responses were to a five point Likert Scale (Not at all/ Just a little/ Moderate amount/ Quite a lot/ A great deal) (Wood and O’Creevy, 2005).

(vii) Human resource practices: Further structured questions elicited details of frequency of supervision, whether participants had had appraisals in the past year and whether they had personal development plans, how much statutory training they had completed (i.e. required training such as in fire safety and child protection) and how much non-statutory, and also what their views were about the usefulness of these, rated on a similar 1 to 5 scale to other items.

E. Adverse events

No fully suitable measure was found in this area. Structured question were therefore developed for the study, eliciting experiences of (a) having been bullied in the previous year, according to participants’


41

own definitions; (b) having been discriminated against, including the grounds for discrimination and whether the person perpetrating this discrimination was colleague, supervisor or manager, patient or visitor; (c) threats and verbal abuse, frequency rated on a 7 point scale; (d) number of incidents of violence experienced and sick leave or injury resulting. Regarding the adverse events, some items had previously been developed and piloted in the scale designed by Prosser et al. (1996), and then adapted for use in crisis teams by Nelson et al. (2009). Additions and refinements were introduced first through discussing the measure with the steering group, representing a range of stakeholder perspectives, and then through pilot testing with 45 staff in 3 inpatient wards and 2 community services, drawn from the London and Warwick sites.

F. Built environment

Three items in the staff questionnaire elicited staff ratings on a 1 to 5 scale of the fitness of their ward for its purpose. These items were designed for the study.

Questionnaires for managers and information and clinical governance departments

The manager of each participating ward was asked to complete a further questionnaire, eliciting:

(a) Organisational indicators potentially related to morale – including details of human resource practices such as systems for supervision, appraisal and staff training, staff sickness, vacancy rates, staff turnover and difficulties in recruiting over the preceding 6 months.

(b) Caseload details – including details of the ward’s speciality and entry criteria, the age, gender and ethnic group mix among the patients, and also of some indicators of severity of illness and complexity of needs e.g. the proportion of current in-patients compulsorily detained, admitted via police or courts or who had a diagnosis of psychosis and/or comorbid substance misuse.

(c) Ward activity and clinical population. At the time of each manager interview, data were obtained from them regarding the clinical and legal status of the patients on the ward the night before. These variables included the total number of patients on the ward/team as well as the number of these who were male, who had a primary diagnosis of psychosis, who had significant substance misuse problems, and how many were detained under the Mental Health Act. Ethnic origin for patients were categorised as White British, White other, Black, Asian, Other or unknown. Data were also obtained regarding the total number of admissions to the ward during the previous year.

(d) Staffing levels and shift patterns – including details of staff to patient ratios and qualified staff to patient ratios, mix of experience


42

and disciplines in the staff team, shift patterns and average number of bank or agency staff per shift.

(e) Organisational climate and leadership including details of supervision and appraisal systems and training opportunities for staff and the availability of lead consultants, modern matrons and practice development practitioners. The nature of the senior management support available to the ward, including Ward Managers was assessed using scales from the Corporate Climate Questionnaire (Gunther and Furnham 1996).

(f) Adverse events, including numbers of violent incidents on the ward, occasions on which restraint teams had been called, staff being off sick following violence, drug dealing or police having to be called to wards and disciplinary investigations. Where possible these data were obtained from clinical governance department with corroboration/filling of gaps by ward managers wherever possible.

(g) Details of catchment areas. Ward managers described the geographical catchment areas from which their wards admitted patients. These were used by research staff to derive indices of catchment area characteristics, to be used in analyses regarding potential geographical determinants of morale. For deprivation, we used indices of multiple deprivation (IMD) 2007. These were retrieved from the Office for National Statistics website for the local authority of each ward. Indices of multiple deprivation are available at local authority level and rank the 354 authorities in England; the highest ranked authority being the most deprived. IMDs are based on data from the latest Census. There are several different IMD scores available. For this study we utilised two IMD scores, namely 1) the overall rank of deprivation for the local authority and 2) the rank for employment deprivation in that local authority (once again, the ranks span from 1-354). Since some wards and teams may cover wide areas, we used two methods to assess the deprivation of the ward. First we retrieved the IMD scores for the local authority in which the unit was located, according to its post-code. This measure reflects the geographical area of the actual the ward/team workplace. We also derived a similar score for the catchment areas from which patients were admitted. The catchment area postcodes were provided by the ward manager. When the catchment area covered more than one local authority, we calculated the mean rank for all the authorities covered by the team. The catchment area deprivation may better reflect the social deprivation of the patients on a ward, compared with the team’s actual location.

C. The Ward Design Questionnaire

This measure was completed for each ward by a trained study researcher familiar with the ward. It is a 19 item instrument developed and validated by two of the applicants (Bartley Sheehan, Elizabeth Burton) based on their previous work in mental health residential settings and input from the study steering group. This was


43

followed by a pilot 333survey of staff of 6 in-patient psychiatric wards who rated which aspects of built environment they considered important. The items included: ward shape, size, openness, how bed spaces were provided, facilities provided for staff and patients, the nature of the nursing station, observation opportunities from a central point, gender separation, colours, flooring, links between the ward and outside, views, and outdoor facilities.

Module 2 Questionnaires for community teams: For Module 2 – the comparison with community teams - an adapted version of the staff questionnaire (Appendix 1) was given to clinical staff and the manager of participating CMHTs and CRTs. Questions were largely unchanged, but the term ward was changed to team throughout, and a number of questions of limited relevance to community teams were removed. Details of physical environment and clinical population were not obtained for community teams. Community staff were additionally asked for reasons (where relevant) for moving from working in an inpatient setting to a community setting. An adapted version of the manager questionnaire (Appendix 2) was given to the managers of participating CMHTs and CRTs.

3.1.4 Procedures

Once participating wards had been identified, ward staff were recruited to the study in the following way. The final procedure was defined in consultation with the ethics committee approving the study to balance concerns regarding confidentiality and anonymity with the need to ensure maximum response rate.

i) The study researcher contacted the ward manager and obtained a list of the names of staff working on the ward. Arrangements were made with the ward manager to publicise the study to ward staff before data collection began (for example, by a study researcher attending a ward staff meeting).

ii) Each staff member was assigned a study ID number by the study researcher. A list linking staff names and ID numbers was kept separately from the data by researchers.

iii) All ward staff were then given a pack including: the staff questionnaire (Appendix 1), marked with a unique staff ID number; an information sheet about the study and a stamped envelope addressed to the study researcher. Packs were distributed to staff via the ward manager, in person by the study researcher during ward visits or left in staff pigeon holes on the ward, as arranged locally on each ward.

iv) Written consent to participation was not sought from participants but assumed by their completion of the form. The information sheet directed staff to tear of the ID number from the corner of their questionnaire before returning it to study researchers if they preferred to participate anonymously.


44

v) Staff were reminded about the study and encouraged to participate through site visits by the study researchers and via reminders from the ward manager. Individual staff who let the ward manager or researchers know they did not wish to participate were not approached about the study again.

vi) Participants returned completed forms to the study researcher in person or using the stamped addressed envelope provided. Forms from all study sites were then delivered to the study researchers at University College London for data entry.

The data were entered at the University of Sheffield onto a secure electronic database. In this staff member was identified only by their study ID number; staff names were not included on the database.

Procedures for recruiting staff and managers from community teams were similar to those described above for ward staff.

3.1.5 Analysis

This section summarises the overall approach, with further details given in each section of the report. Prior to embarking on the main analyses, the following steps were taken (a) data cleaning, including examining frequencies for all variables and checking or, where necessary, censoring, any unexpected values; (b) computation of and examination of the internal consistency and, for the morale indicators and job characteristics, the distinctness on factor analysis of the main scales to be used in the analysis. Cronbach’s alpha was used as a measure of internal consistency. Where some of the items on a scale were missing, the mean was calculated from the available items provided more than half of those required to compute the scale mean were available. (c) plotting of histograms of all the main continuous variables, checking the extent to which they were normally distributed.

Description of levels of morale and comparison of team types (4.1)

Initial analyses examined the levels of morale on all the main indicators both for the sample as a whole and by type of ward or team. Our original plan was to make an overall comparison between ward and community staff, testing the hypothesis that inpatient staff would have poorer morale than those in the community. However, descriptive statistics indicated large variations between types of ward or team, both across types of inpatient team and between CRTs and CMHTs. We therefore decided not to aggregate inpatient and community teams, but to explore differences by inpatient ward or community team type throughout. At this stage in the analysis, we also used multilevel regression (see below), to examine what proportion of the variance in morale scores was at ward level and what proportion at individual level.


45

Examination of relationship between morale indicators (4.2)

We began our examination of the relationship between morale indicators by examining pairwise Pearson’s correlations. The morale indicators included in this analysis were (a) the three Maslach burnout inventory sub-scores; (b) Warr’s job-related well-being scales; (c) the GHQ score, log transformed to achieve a more normal distribution; (d) the Job Involvement Scale; (e) Intrinsic Satisfaction; and (f) staff’s one item rating of their own and their team’s morale.

Following this, we conducted a Principal Components Analysis to investigate whether components onto which several indicators load could be identified in the data.

Examination of factors associated with morale

The next step was to examine for six main morale indicators (the Maslach burnout inventory sub-scales, the anxiety-contentment and depression-enthusiasm scales, and intrinsic job satisfaction) the factors associated with higher or lower morale. Separate analyses were conducted for each morale indicator. The fundamental approach was multilevel regression analysis: this is an extrapolation of usual regression techniques that accommodates nested data, as in our data set where individuals are nested within wards and teams. Effects both at individual and ward level could be simultaneously modelled through this approach by taking into account of auto-correlation at each level (Snijders, 1999). List-wise deletion of missing variables meant varying numbers the data set for each analysis. The mixed effects multilevel regression procedures in STATA 10 (command xtmixed) or the equivalent commands in SPSS were used for these analyses.

The following steps were taken in examining associations with morale

(a) Associations with demographic and occupational characteristics (4.1)

The initial step in our multilevel regression analyses involved investigation of associations between morale and the main demographic (e.g. age, sex, ethnic group, place of birth, marital status) and occupational (e.g. profession, length of service, hours worked) characteristics, and also ward or team type. Aims were to investigate associations between each morale indicator and these variables, and to establish which variables need to be used as control variables at future stages in the analysis. First we examined the association between each of these independent variables and morale individually by putting them into an otherwise empty multilevel regression model with the morale indicator as the dependent variable and individuals nested within wards. Initially significant variables were then entered together to derive an adjusted model of the variables significantly associated with each morale indicator.


46

(b) Testing of the Demand-Support-Control Model (4.3 and 4.4)

The next stage involved building models to test the additive and interactive versions of the Karasek model for the effects of support, demands and control upon each outcome in turn. For each outcome, sets of variables were entered hierarchically. Having first partitioned the variance into within and between service unit components, the proposed control variables were entered as predictors, with those exhibiting significant effects upon at least one outcome retained for the further steps of the modelling process. These steps were first a test of the additive model, via the simultaneous assessment of the main linear effects of demands, control and support, and the investigation of potential curvilinear effects. Following this, the interactions between demands, control and support were added to test the interactive version of the Karasek model.

We then investigated the effects of adding the Demand, Support and Control variables to the adjusted models obtained in Step A, presenting overall models with these variables and also major demographic and occupational variables included.

(c) Investigation of the effects of adding Built Environment, Geographical context and Clinical Population, Adverse Events and Organisational Context variables to the models

Our next step was to investigate the effects of incorporating in multilevel models for each outcome indicator each of the following groups of variables. Some of the variables e.g. for built environment and ward population, were measured only for inpatient and not for community teams: where models included these, they were based only on the inpatient part of our sample. The process of building models for each type of variable was essentially similar – we used multilevel regression models, as above, first to test the results of adding variables individually, then as a block with adjustment for the demographic and educational control variables identified in step A above.

Adverse events (Section 4.6): several steps were used to examine associations with both individual level and ward level reports of adverse events. We began by entering each relevant variable into an empty multilevel regression model, testing associations with each outcome measure. Where there were significant associations at this stage between an adverse events variable and a morale indicator, we then went on to add to the model the control variables identified at step A above as significantly associated with the relevant outcome measure. For each outcome, we then constructed a model containing all adverse events variables found to be individually significant, together with the relevant control variables. Finally, we added the Demand-Support-Control variables to the model as a block in order to test whether the adverse events variables had an effect on morale that was additional to rather than subsumed in the Karasek model.


47

Built environment (Section 4.5): Mean scores for each built environment variable (examining the 3-item staff rating and a final 6-item researcher rating scale) were used in the construction of a multilevel model to establish the effect of the built environment variables on the morale outcomes. For the ward design questionnaire, individual items were categorized by consensus for this analysis – for example allowing the effect of green versus non-green views, or hard versus carpeted floors, or open versus sealed versus no nursing stations. As with adverse events, we built models to test the additive effects of built environment variables upon each morale outcome in turn. Adjusted models included all relevant demographic (e.g. age/gender/ethnic status) and occupational variables established in univariate and multilevel analyses as influencing morale in the wider study.

Geographical context and clinical population (Section 4.7): Using the Index of Multiple Deprivation scores obtained for catchment areas (see above), we created four categories/quartiles of deprivation. Those in the highest quartile of deprivation in the country (ranks 1-88), second quartile (ranks 89-177), third (ranks 178-258) and least deprived quartile (ranks 259-354). For case-mix, we determined the proportion of patients on the ward who met each of the criteria above, namely male, the proportion who were not white-British, the proportion of psychotic patients, the proportion with significant substance misuse problems and the proportion detained under the Mental Health Act. As with other groups of variables, we then used multi-level modelling to assess whether each of our variables of interest was associate with the main morale outcomes. Our modelling strategy again followed the methods described above, in that we first built a simple model only including the “exposure of interest” (e.g. IMD quartiles or number of admissions) and the morale outcomes. We noted which of the variables were associated with each outcome at a significance level of p<0.05. We then went on to test the effects of adjusting for the demographic ad occupational variables identified at Step (a) as significantly associated with the outcome of interest, and finally observed the effects of including together all relevant variables in an adjusted model.

IMDs were initially included in the model as quartiles, for both the total IMD and the employment IMD score, relating to both the catchment area and also the geographical location of the unit. As a sensitivity analysis, the overall IMD score was also tested as a continuous variable with each outcome. Casemix variables, including number of admissions and the percentages meeting different clinical and/or demographic criteria, were entered as continuous variables.

Organisational context: Finally, the same strategy was adopted for the main organisational context variables, including at an individual level aspects of definition of individual jobs (role clarity and role conflict), team functioning (team cohesion and team conflict), human


48

resource practices (experience of and attitudes to appraisal, supervision, training and personal development plans) and perceived fairness and voice within the organisation, and at a ward level support for ward managers from senior management, human resource practices on ward such as implementation of supervision and appraisal and mechanisms for supporting staff in their work or for improving the quality of work on the ward, including staff support groups, away days, ward meetings, support from Personal Development Practitioners, modern matrons and designated lead consultants, and adoption of specific models or philosophies of care and/or of Protected Engagement Time.

(d) Construction of final exploratory models (Section 4.9)

The final stage of the modelling process involved exploring the effects of entering the significant variables from the steps above together into a multilevel regression for each outcome of interest. We built models in this way both for the sample as a whole and for ward staff only – variables collected only for wards, such as those regarding built environment and catchment area, were included only in the latter models.

3.2 Module 3: Qualitative study

3.2.1 Setting

Seven wards from among those which participated in Module 1 formed the bases for this survey. Wards were drawn from the North London and Heart of England regions.

3.2.2 Sample

The wards were purposively selected to reflect a range of Trusts and ward types and to include wards which fell into the top 25% of our sample for indicators of morale in the initial Module 1 survey and wards which fell into the into the bottom 25%. The following sampling and data collection procedures were followed on each ward.

a) Two focus groups were assembled, intended to consist of 6 to 8 staff each. In practice, the intended number of participants was often difficult to obtain owing to the constraints of ward staffing: we have therefore described these groups as group interviews in this report, reflecting the fact that usual numbers for focus groups were not always obtained. On each ward, one group interview was held with more junior staff from a range of professional backgrounds, including ward nurses, junior doctors, nursing assistants and other ward staff without major management responsibility for others on the ward. The other consisted of senior staff who worked on the ward and also had some managerial responsibility for other staff on the ward, including the ward manager and deputy ward managers, consultant psychiatrists with responsibility for beds on the ward, and, where


49

relevant, senior members of other professions such as consultant clinical psychologists and Head Occupational Therapists.

b) Three staff on each were interviewed individually, sampled so as to include one senior member of staff with managerial responsibility, one junior ward nurse and one member of another profession who did not have managerial responsibility. Where several staff were eligible for interview in a particular category, we chose at random between them.

c) Three current or recently discharged patients were interviewed. We selected the patients who were deemed by staff to be closest to the point of discharge. Patients with a range of diagnoses and both compulsorily and voluntarily admitted patients were included, but we only included those who were capable of giving informed consent to study participation. Capacity was assessed initially by clinicians; researchers also confirmed before taking written consent that participating patients had a clear understanding of what they were asked to do and of their freedom to refuse if they wish.

d) One senior service manager was interviewed who had managerial responsibility for the ward but is not based on it, such as a unit director or manager or a Modern Matron.

e) Two available staff, pragmatically chosen, were asked to guide the study researcher round the ward on an ‘accompanied walk’, pointing out key features of the ward that they feel impact on their morale and the effectiveness and safety of their work.

The study researcher also gathered material about the organisation of the wards from available written materials, including all operational policies and timetables and protocols for aspects of ward work such as admissions and discharges and individual work with patients.

3.2.3 Measures

Interview guides developed for the study can be found in Appendix 1 of this report. They were as follows:

a) Group Interview Guide: The groups were asked initially to undertake a participatory exercise to elicit their views on what were the main positive influences and negative influences on morale on the ward. This was a ‘talking wall’ exercise in which all participants were asked to write their ideas about what influences morale on wards on Post It notes and stick them onto paper covered walls that were headed “negative factors” and “positive factors” and “ideas for improving and sustaining morale”. Two facilitators conducted a focus group discussion of the ideas generated, ensuring that some specific areas of interest such as leadership, ward design, and ward policies and procedures were included whether or not they are on the ‘talking wall’.


50

b) Individual Staff Interview Guide: Whereas the aim in the focus groups was to explore views about what impacts on the morale of the ward team as a whole, these semi-structured interviews focused on people’s individual experiences at work and views about what has made them feel happy or unhappy at work and satisfied or dissatisfied with their jobs.

c) Individual Patient Interview Guide: Patients’ experience of the ward environment and of staff interactions with patients was explored. Participants were asked to comment on the aspects of staff behaviour that impact most on patient experiences and on their views about improvements in staff working practices and ward organisation that they believe would have a favourable impact on the ward environment.

d) Senior Manager Interview Guide: This interview explored senior managers’ views about the level of morale on the ward in question and what factors had the most important impact on this.

e) Organisational analysis checklist: We obtained information on the checklist from available written materials and from managerial and administrative staff details of the organisational systems in place on each ward. This included details of supervision systems, systems for allocating staff to patients and for prioritising particular tasks (e.g. protected time), timetables for ward activities, and details of any care pathways and protocols relating to the care of patients on the ward, audit data collected and criteria for admission and discharge. Full analyses of these data are yet to be concluded.

3.2.4 Procedures

Ward selection: High morale and low morale wards were purposively sampled. This was based on computation of a summary morale score, computed by scaling each of the morale indicators from 0 to 100 and summing them all. The sample of wards was selected by a study researcher who was not involved in collecting data for the second phase of the study. Both the ward teams and the researchers collecting data were blind to the wards’ morale status, to reduce risk of this knowledge affecting results in the second phase of the study.

Recruitment: Staff identified as potential participants for focus groups, individual interviews or the architectural guided walk were given an information sheet by the study researcher and an opportunity to ask questions. Written consent was obtained from participants in advance of interviews.

Data management: Interviews were taped using a digital recorder and transcribed using professional transcription services. Interview transcripts were uploaded to the qualitative software programme Nvivo7 for analysis. Digital recordings of interviews were then deleted and written transcripts were stored in a locked filing cabinet


51

in a private office at UCL and archived as soon as the study was completed.

3.2.5 Analysis

Interviews were recorded and transcribed verbatim and data analysis was conducted using the software package NVivo 7. A collaborative thematic-analysis approach was taken. A preliminary template of lower order descriptive categories was established by four of the research team (Johnson, Hundt, Paul and Totman). Each member of the team read the same selection of interviews independently and generated a list of major themes. The group then met to discuss the themes and agreed on a coding framework. One researcher (Totman) thereafter coded the data using this framework, which was elaborated and refined during the analysis. Ongoing consultation with the co-authors was maintained during this process. The researcher then undertook an exploratory analysis of the data set using NVivo software devices. The software allows one to explore the relative frequency with which themes occur across the data set. The data presented here focuses on the main themes that emerged. These were: staffing levels, peer relationships/teamwork, leadership and management, relations with senior managers, clarity and confidence, consistency of structures, support and supervision, training, being with patients and the physical environment. The analysis conducted so far and reported here focuses principally on the factors affecting staff morale. For this reason it draws heavily on the staff interviews. The data obtained from patients were less immediately relevant to this discussion and pertained more to the perceived impact of staff morale.

3.3 Module 4: Leavers’ Survey

3.3.1 Setting

This survey was based on the same 100 wards which participated in Module 1.

3.3.2 Sample

All clinical staff leaving a participating ward in the year following data collection for Module 1 were eligible for this survey.

3.3.3 Measures

The Leavers’ Questionnaire was given to participants. This questionnaire elicited their reasons for leaving and the type of work setting to which they had moved.


52

3.3.4 Procedures

The ward manager or relevant Trust Human Resources department for each ward was contacted by the study researcher and asked to send a copy of the Leavers’ Questionnaire and an information sheet to staff who had left the ward within the last year. Questionnaires were sent by post to staff’s forwarding address and, if they remained employed by the same Trust, electronically to their work email address.

Further data regarding leavers were also obtained in Module 2 by asking CMHT and CRT staff questions about whether they had ever worked on a ward and, if so, why they had left: results from this are reported above.

3.3.5 Analysis

Descriptive data were reported from Leavers’ Questionnaires, showing the number and proportion of respondents rating different reasons for leaving as important.

3.4 Module 5: Persistence of high and low morale

3.4.1 Setting

Wards from the North London or Heart of England areas which had participated in Module 1 formed the basis for this survey.

3.4.2 Sample

Data were collected from 20 wards, 10 from North London and 10 from the Heart of England. Wards were purposively sampled to reflect a range of ward types and initial morale levels from Module 1. All clinical staff working in participating wards at a timepoint one year after data collection for Module 1 were asked to participate in this survey.

3.4.3 Measures

An abbreviated version of the staff questionnaire was given to all staff at participating wards. The main content was the principal measures of morale used in the study, together with demographics.

3.4.4 Procedures

Procedures for recruiting participants and collecting, managing and storing data were as described for Module 1 in Section 3.1.4.


53

3.4.5 Analysis

Analysis conducted so far with these data is essentially descriptive. Mean scores for all respondents were reported at baseline and follow-up for the following variables: Maslach Burnout Inventory emotional exhaustion, depersonalisation and personal accomplishment scores; Warr scale anxiety-contentment and depression-enthusiasm scores. The number of staff at baseline meeting criteria for high burnout on Maslach Burnout Inventory variables and for being a stressed case on General health Questionnaire total score was reported. The stability of burnt out or stressed case status was assessed by comparison with staff’s scores at one year follow-up.

Mean scores for all 20 wards participating in Module 5 were presented at baseline and follow up for Maslach Burnout Inventory and Warr scale variables. The proportion of staff on each ward meeting high burnout or stressed case status at baseline and one year follow-up were compared.

3.5 Module 6: Staff sickness and turnover

3.5.1 Setting and sample

Data were collected from the same 100 wards in Module 1.

3.5.2 Measures

The Staff Sickness and Turnover Questionnaire was completed with one respondent for each participating ward, providing information about staff turnover and sickness rates in the one year period following data collection for Module 1.

3.5.3 Procedures

The study researcher asked the ward manager initially then, if necessary, the Trust Human Resources Department to provide data for this Module.

3.5.4 Analysis

Descriptive data were reported for staff turnover and sickness rates. Their association with indicators of morale were examined using correlative tests.


54

4 Results Results from Modules 1 and 2, the 100 ward survey and comparison with community teams, will be reported together in Sections 4.1–4.9. Results from Modules 3–6 are reported in sections 4.10–4.13 respectively.

4.1 A description of staff morale and its associations with ward type and demographic characteristics

4.1.1 Summary points

Mean morale scores varied substantially between different types of inpatient ward and community service.

Community Mental Health Team (CMHT) staff emerged as highly stressed on several measures (high emotional exhaustion scores, a high proportion of stressed cases (39%) and the only team type where anxiety predominated over contentment).

Acute wards also produced a high burnout score for emotional exhaustion. CMHTs, Crisis Resolution Teams (CRTs), PICUs and Child and Adolescent wards had high personal accomplishment scores. All teams scored within an average range on other measures.

Mean satisfaction scores for all types of service tended towards satisfaction rather than dissatisfaction.

Satisfaction with colleagues was usually highest; satisfaction with training and career opportunities was usually lowest.

Respondents tended to rate their personal morale higher than they rated team morale.

Working in CAMHS, Rehabilitation or CRT teams and being Black or Asian rather than White were consistently associated with higher morale in multivariate analysis.

Nursing Assistants reported lower emotional exhaustion and higher contentment than qualified colleagues. Ward managers reported higher personal accomplishment scores. Bank and agency staff were less anxious and more satisfied than permanent staff.

4.1.2 Survey recruitment and responses

One hundred wards in 19 mental health Trusts were recruited to the study, comprising 50 general acute wards, 10 child and adolescent


55

mental health inpatient services (CAMHS), 9 rehabilitation wards, 9 mental health of older people wards, 12 forensic wards and 10 psychiatric intensive care units (PICUs). Nineteen rather than 20 Trusts were included as in the South West in particular recent mergers had created some very large Trusts, which in practice consisted of several largely managerially distinct catchment areas. Recruiting the planned four Trust in the South-West would therefore have required excessively long researcher travelling times, and we decided that 19 was an adequate number. Data collection took place in the 2007 and early 2008.

The general acute wards were all first-line catchment area acute wards for adults of working age. The PICUs were based on the same sites as general acute wards, admitting patients considered too risky or disturbed to be managed on the acute wards. The CAMHS wards primarily served adolescents up to 17 years old. The forensic wards formed part of regional medium secure units, with patient populations mainly admitted via courts following significant criminal offences. The mental health of older people wards were acute admission wards serving older adults, generally from around 65 years upwards.

A community team comparison sample was also recruited. This consisted of 19 crisis resolution teams (CRTs) and 19 community mental health teams (CMHTs). CRTs are specialist teams dedicated to assessment and home treatment of people experiencing a mental health crisis of sufficient severity for admission to be considered. CMHTs are generic community teams serving mentally ill adults of working age in local catchment areas defined by geographical boundaries or by clusters of general practices.

A total of 3,545 questionnaires were distributed, of which 2,258 valid responses were returned, a response rate of 63.7%. At Trust level, the response rate varied from 51.9% to 71.8%, with a median of 60%. At ward level, the rate varied from 22.0% to 100%, median level 62.3%.

4.1.3 Sample characteristics

Table 2 summarises the characteristics of the sample. Just over a third were male, three quarters were from a White ethnic background and a similar proportion were UK-born, and the mean age was around 40. Two thirds were married or cohabiting and around half had dependants, either children under 18 living with them or caring responsibility for a disabled adult.

Nurses made up very much the largest professional group, accounting for just under half the responses received. Just over a quarter of participants were nursing assistants, healthcare assistants or others without a professional mental health qualification. All the other main mental health professions were represented in much smaller groups. Five per cent of respondents were ward managers or


56

community or crisis team leaders, the majority also qualified nurses. Staff who had worked on the ward in a locum, bank or agency capacity for at least a month were encouraged to complete the questionnaire, but made up only 2% of the final sample. Staff were classified for the purposes of our analysis as being senior if they were graded at Band 7 or above in Agenda for Change, or if they were consultant psychiatrists; 12% met these criteria.

Length of service on current wards was substantial, with a mean of 4.3 and median of 3 years. Average total time working in mental health care was just under 12 years. Just over a third of respondents reported working more than 40 hours per week.


57

Table 2. Sample characteristics

Characteristics N=2258*

Sex

Males (%)

Females (%)

Missing

803 (36%)

1421 (64%)

34

Ethnic group

White groups (%)

Black African, Caribbean or British (%)

Asian groups (%)

Other or mixed (%)

Missing

1606 (75%)

329 (15%)

177 (8%)

30 (1%)

116

Mean age (SD)

Missing

40.7 (10.4)

102

Marital status

Single (%)

Married/cohabiting (%)

Divorced/widowed/separated (%)

Missing

502 (23%)

1463 (67%)

234 (11%)

59

Place of Birth

Born in the UK

Missing

1664 (76%)

54

Whether has any dependants (as parent or carer)

Has dependants

Missing

1066 (49%)

65

Professional group

Nurses (%)

Doctors (%)

Psychologists (%)

Occupational therapists (%)

Nursing assistant/support worker/others without professional qualifications (%)

1054 (47%)

135 (6%)

44 (2%)

82 (4%)

640 (29%)


58

Characteristics N=2258*

Social Workers (%)

Ward managers and team leaders

Other occupations

Missing

86 (4%)

111 (5%)

93 (4%)

13

Whether at senior grade

Agenda for Change Band 7 or above** or consultant psychiatrist

251 (12%)

Whether working on a locum basis

On a locum, bank or agency contract***

Missing

52 (2%)

13

Tenure in current service

Mean time working on current ward or in current team (years) (S.D,)

Median

Missing

4.3 (4.6)

3.0

71

Length of time working in mental health services

Mean length of time working in mental health service (years) (S.D.)

Median

Missing

11.5 (8.8)

8.9

71

Hours worked

Less than 35 hours per week

Between 35 and 40 hours per week

More than 40 hours per week

Missing

318 (15%)

1082 (51%)

714 (34%)

144

* Total: n= 2269 – but 11 excluded as from an ineligible ward and community mental health team so n=2258

** Agenda for Change refers to a reform of pay scales in the NHS intended to introduce a uniform set of pay scales across all professions except medicine. Band 7 is a relatively senior grade which in an inpatient ward usually implies managerial responsibilities of some form.

*** These are staff hired as temporary cover for vacant posts. They may work in a service in this capacity for anything between a few hours and several months. They were included in our study only if they had been working on the relevant ward for at least a month.


59

4.1.4 Levels of morale

Table 3 shows mean scores and standard deviations for the main indicators of staff well-being measured in the 100 ward survey and in the comparison CMHTs and Crisis Resolution Teams. A number of questionnaires were returned with the identifying number removed: this prevented us knowing which ward they originated from, although the type of questionnaire filled in indicated whether they worked in a ward or a community setting. Data for these anonymous respondents are shown in Table 3 and 4, but in subsequent multilevel analyses where individuals are clustered within wards, we were not able to include them as their cluster could not be identified.

Shown first in Table 3 are results for each group of staff for the Maslach Burnout Inventory. Mean totals for each of the three main components, Emotional Exhaustion, Cynicism and Personal Accomplishment are shown, as well as the proportion of staff in each group who fall in the ‘high burnout’ category on the relevant dimension according to the norms given by the authors for mental health staff

Emotional Exhaustion is the most prominent form of burnout, though the mean for the sample as a whole is within the ‘average burnout’ category, as are means for both the other dimensions of burnout. Two important groups, however, have mean scores in the ‘high burnout’ range, acute admission wards, although the mean here is only just above the ‘high burnout’ cut off of 21, and CMHTs, where the mean is well into this range and considerably above all other types of team. This is reflected in a majority of CMHT staff (60%) falling in the high emotional exhaustion category, as do almost half (49%) of acute ward staff. Emotional exhaustion scores for the specialist sub-types of ward are lower, all within the 14 to 20.9 range classified as ‘average burnout’ on this component of burnout. CRTs and rehabilitation units have the lowest mean emotional exhaustion scores at 17.7 and 16.1 respectively.

For the second component of burnout we have adopted the term Cynicism, used in some more recent papers (Lasalvia et al. 2009) rather than the original name of Depersonalisation. This avoids confusion as Depersonalisation has a different technical sense in the field of mental health. For Cynicism, scores of 8 or more indicate burnout. No mean for any type of service reaches this level, the highest being the mean for PICUs at 7.0. All means for team types fall into the ‘average burnout’ range, between 4 and 7.9. Rehabilitation services again have the lowest levels of burnout with a mean of 4.1, close to the ‘low burnout’ threshold. For CMHTs, high levels of Emotional Exhaustion are not matched by high Cynicism: the mean of 5.7 falls in the mid-range and is very close to the score for CRTs.


60

The final component of the Maslach Burnout Inventory, Personal Accomplishment, is scored in the opposite direction from the others, with low scores indicating a lack of feeling of efficacy and accomplishment at work and thus high burnout. The profile of the sample on this dimension of burnout again looks relatively benign. High burnout is indicated by a score of 28 or less on Personal Accomplishment: no type of team approaches this threshold. Rehabilitation units, CMHTs, CRTs, PICUs and CAMHS wards are all in the low burnout range on this dimension, with scores exceeding 34, the rest are around the higher end of the ‘average’ burnout range. Nonetheless between 17% (in CRTs) and 30% (on Forensic wards) of staff do fall into the high burnout range, as do between 18% (on Rehabilitation units) and 36% (on PICUs and Forensic wards) on Cynicism.

Below the Maslach Burnout Inventory data in Table 3 are the proportions in each group reaching the level defined as indicating that someone is stressed on the GHQ-12. A striking finding is that 39% of CMHT staff reach this level. Between 22% (wards for older people) and 31% (CAMHS wards) reach this in other types of ward.

Table 3 also presents data for the more specifically job-related measures of depression-enthusiasm and anxiety-contentment. On the depression-enthusiasm scale, scores above 3.0 indicate that enthusiasm predominates over depression and vice versa. On the anxiety-contentment scale, contentment prevails above 3.0 and anxiety below this level. On this scale, most types of service have means just over 3.0, indicating that contentment is reported at a slightly higher level than anxiety, although CMHTs fall just below this threshold. Similarly to Emotional Exhaustion and the GHQ-12, the most positive reports are from Rehabilitation wards, followed by wards for older people, forensic units and CRTs.

For the depression-enthusiasm scale, mean scores are somewhat more towards the positive pole than for anxiety-contentment, ranging from 3.32 in CMHTs and 3.37 on acute wards to 3.6 or more in CAMHS, Rehabilitation and older adults’ inpatient units and CRTs.

Statistical tests are not reported in Tables 3 and 4 as the value of multiple unadjusted comparisons may be somewhat limited, but when ANOVA and chi squared tests were used to compare all groups, a highly significant between groups test was found in every test, reflecting in part the large numbers available for these analyses.

4.1.5 Levels of satisfaction and morale

Table 4 presents data on levels of satisfaction, again displayed by type of service. Four components of satisfaction were derived: intrinsic satisfaction, reflecting overall satisfaction derived directly from doing the job, satisfaction with career and training opportunities, satisfaction with colleagues and satisfaction with how useful and meaningful the work is. A score of 3 on any scale


61

Table 3. Levels of staff well-being in wards, crisis teams and community mental health teams across England

General acute wards

N =721

CAMHS

wards

N=189

Forensic

Wards

N=219

Older adult

wards

N=157

Rehab.

wards

N=137

PICUs

N=148

Community

Mental

Health

Team

N=258

Crisis

Resolution Team

N=216

Ward staff unknown specialty

N=194

Community staff unknown team type

N=19

All

MBI Emotional Exhaustion

Mean (SD)

N=2224

Max score 54

21.1*

(12.7)

18.3

(10.1)

19.0

(10.8)

19.3

(13.0)

16.1

(11.3)

20.0

(11.1)

23.8

(11.0)

17.7

(10.7)

20.3

(12.5)

21.1

(12.3)

20.1 (12.0)

Average burnout

Number (%) in ‘high burnout’ group

347 (49%)

67 (36%)

86 (40%)

63 (41%)

39 (29%) 64 (44%)

153 (60%)

80 (38%)

82 (43%) 11 (58%)

992 (45%)

MBI Cynicism

Mean (SD)

N=2202

Max score 30

6.2

(6.0)

4.4

(4.1)

5.8

(5.9)

4.8

(4.9)

4.1

(4.7)

7.0

(5.9)

5.7

(5.2)

5.8

(5.2)

5.6

(5.7)

4.2

(4.7)

5.7 (5.6)

Average burnout

Number (%) in high burnout category ‘

226 (32%)

35 (19%)

77 (36%)

31 (20%)

24 (18%) 52 (36%)

73 (29%)

57 (27%)

55 (29%) 5 (26%) 635 (29%)


62

MBI

Personal accomplishment

N=2216

Max Score 48

33.1

(8.4)

34.8

(8.1)

32.1

(8.9)

33.1

(8.7)

35.1

(7.9)

34.7

(8.1)

34.3

(7.4)

35.0

(7.7)

32.7

(8.6)

34.2

(9.3)

33.7 (8.3)

Average

burnout

% ‘Burnt out’ 193 (28%)

33 (18%)

65 (30%)

45 (29%)

32 (24%) 35 (24%)

49 (19%)

34 (17%)

57 (30%) 4 (21%) 547 (25%)

Number (%) reaching threshold for GHQ ‘caseness’ (%)

N=2140

199

(29%)

57

(31%)

47

(23%)

33

(22%)

30

(24%)

37

(27%)

98

(39%)

46

(23%)

43

(24%)

5

(31%)

559 (28%)

Warr

Anxiety-contentment

N=2156

3.08

(0.71)

3.27

(0.68)

3.26

(0.60)

3.36

(0.70)

3.36

(0.67)

3.10

(0.66)

2.91

(0.75)

3.27

(0.64)

3.18

(0.73)

3.10

(0.62)

3.16 (0.70)

Max 5

Warr

Depression-enthusiasm

N=2158

3.37

(0.78)

3.63

(0.68)

3.55

(0.70)

3.61

(0.79)

3.60

(0.78)

3.42

(0.78)

3.32

(0.79)

3.61

(0.69)

3.34

(0.76)

3.51

(0.61)

3.46 (0.76)

Max 5

*Results presented in bold in this table fall above the threshold for “high burnout”


63

indicates a neutral attitude, with higher scores indicating overall satisfaction and lower ones dissatisfaction.

In general, the picture is encouraging, with mean scores for almost all types of service tending towards satisfaction rather than dissatisfaction on almost all components. Scores for satisfaction with colleagues are especially high; those for satisfaction with training and career opportunities somewhat lower than the rest.

Job involvement, a scale for which respondents endorse items indicating how far their work is the most important aspect of their life, tended not to attract very high scores, with participants in most types of service tending not to endorse such items.

We also elicited on a 1 to 5 scale single item ratings both of participants’ own morale and the perceived morale of their teams. On this scale, 3 again indicated neutrality, with morale neither good nor poor. Despite the apparent similarity between the concept of morale and personal accomplishment and job satisfaction, ratings on this item tended to be somewhat less positive, Means for acute wards and CMHTs were a little worse than the neutral point, those for other types of services a little better, with Rehabilitation wards again having the most benign profile. For all types of service except rehabilitation units, there was a pervasive tendency for the morale of the team to be rated lower than individual morale.

4.1.6 Multivariate analyses of the relationship between morale indicators and demographic and occupational indicators

Subsequent to these descriptive analyses, we carried out a set of multilevel analyses for which our aims were:

a) To explore relationships between individual characteristics and basic descriptive characteristics of work and indicators of moral.

b) To identify potential confounders to be adjusted for in further analyses.

c) To identify how much of the variance in morale is at Trust, ward and individual levels.

d) To examine which differences between types of ward and service persist when adjustment is made for potential confounders.


64

Table 4. Levels of satisfaction and morale in wards, CMHTs and CRTs in England

Acute

N =721

CAMHS

N=189

Forensic

N=219

Older adult

N=157

Rehabilitation

N=137

Psychiatric Intensive care

N=148

Community

Mental

Health

Team

N=258

Crisis

Resolution Team

N=216

Ward staff unknown specialty

N=194

Community staff unknown team type

N=19

All

Intrinsic job satisfaction

N=2212

3.31

(0.84)

3.51

(0.78)

3.40

(0.83)

3.38

(0.74)

3.41

(0.75)

3.39

(0.79)

3.39

(0.74)

3.56

(0.71)

3.35

(0.80)

3.34

(0.79)

3.40 (0.80)

Max 5

Satisfaction with meaningfulness of job

N=2196

3.50

(0.86)

3.94

(0.66)

3.68

(0.76)

3.57

(0.80)

3.76

(0.66)

3.75

(0.63)

3.68

(0.75)

3.88

(0.64)

3.52

(0.79)

3.72

(0.73)

3.65 (0.78)

Max 5

Satisfaction with training / career opportunities

N=2209

3.00

(0.96)

3.11

(1.00)

3.14

(0.86)

3.22

(0.90)

3.33

(0.81)

3.11

(1.00)

2.95

(0.92)

3.02

(0.90)

3.26

(0.98)

3.04

(0.93)

3.07 (0.94)

Max 5

Satisfaction with colleagues

N=2225

3.82

(0.79)

4.00

(0.71)

3.70

(0.80)

3.85 (0.82)

4.00

(0.68)

3.83

(0.75)

4.05

(0.73)

3.95

(0.75)

3.72

(0.78)

4.00

(0.68)

3.87 (0.78)

Max 5


65

Job involvement

N=2224

2.54

(0.79)

2.55

(0.78)

2.57

(0.85)

2.52

(0.82)

2.49

(0.70)

2.57

(0.80)

2.49

(0.70)

2.44

(0.78)

2.56

(0.87)

2.21

(0.67)

2.52 (0.79)

Max 5

Rating of own morale

2.88 (1.11)

3.05

(1.04)

3.20

(0.99)

3.05

(1.04))

3.30

(1.05)

3.01

(1.04)

2.78

(1.07)

3.11

(1.06)

2.82

(1.15)

3.00

(1.00)

2.98 (1.08)

Max 5

Rating of team morale

2.65

(1.07)

2.95

(1.03)

3.06

(0.94)

2.92

(1.10)

3.29

(0.88)

2.88

(0.98)

2.53

(1.03)

2.90

(1.02)

2.69

(1.06)

3.00

(1.05)

2.80 (1.04)

Max 5


66

Our initial aim was thus to examine variables at three levels – Trust, ward and individual. However, when multilevel regression was carried out with both Trust and ward levels, very little variance appeared to be at Trust level. This might be because Trust-level characteristics are unimportant to morale, or it might reflect low power for this level of analyses. For all the outcome variables examined (Emotional Exhaustion, Cynicism, Personal Accomplishment, Intrinsic Satisfaction, Depression-Enthusiasm and Anxiety-Contentment), less than 1% of variance was at this upper level. It was therefore omitted in subsequent analyses, which included two levels, ward and individual.

The following steps were followed in the analysis for each outcome variable

a) An empty ‘multilevel regression model (using the xtmixed command in Stata 10) was examined, with individuals nested within wards.

b) The main individual-level demographic and job variables were added one by one to the regression. These were gender, age (categorised as in Table 1 because of lack of a linear relationship), ethnic group (categorised to avoid small numbers into White groups, Black groups including African and Caribbean, Asian groups, other or mixed), whether born abroad, marital status, whether any dependants, profession, time in mental health services and on current ward (each again categorised because of lack of linearity on some indicators), hours worked in total, whether in a senior post (defined as Agenda for Change Band 7 or above), whether employed on a temporary, bank or agency basis, and type of service.

c) For each outcome variable, explanatory variables significantly associated with the outcome when entered alone were entered together into a multilevel regression model.

Tables 5, 6 and 7 report which of the explanatory variables emerged as significant from multilevel regression analyses with all the individually significantly associated variables, as described in (c).

Table 5 reports on variables associated in a multilevel regression analysis with the components of the Maslach Burnout Inventory. For Emotional Exhaustion, a relationship between working in a CMHT and high levels of burnout is confirmed, as well as between lower levels and working in a rehabilitation or CAMHS unit or crisis team. Nursing assistants also tended to be less burnt out than their professionally qualified colleagues. There was a highly significant relationship between total hours worked and Emotional Exhaustion, and staff who had worked for less than a year on a particular ward were less burnt out than others. In terms of length of service in mental health services overall, the highest levels of burnout were associated with


67

having between 5 and 9 years total service. The only significant relationship with socio-demographic characteristics was with ethnic background: staff from Asian groups tended to be less burnt out on this component. Most variance was between individuals rather than between wards, with just under 6% of variance with an empty model at ward level and just over 3% of unexplained variance at a ward level once adjustment was made for the variables above.

With regard to Cynicism, CAMHS and rehabilitation work settings were again associated with lower burnout, this time along with older people’s wards. Socio-demographic variables were considerably more prominent in explaining variance in this component of burnout: women showed lower levels of Cynicism than men, people from Asian and Black groups than White people and people who were married or cohabiting than single people. People over 55 showed significantly less Cynicism than the reference group, people under 25. Nursing assistants and other unqualified staff again showed lower levels of burnout, as did occupational therapists and psychologists.

On the third dimension of burnout, Personal Accomplishment, CAMHS and crisis team staff showed lower levels of burnout than acute ward staff, the reference group. Being Black or Asian was again associated with lower burnout, and this time Ward managers rather than unqualified staff emerged as less burnt out. Those employed in mental health services for 10 or more years reported significantly lower levels of burn out.


68

Table 5. Multilevel models for Maslach Burnout Inventory variables – demographic and occupational variables significantly associated with outcome in final model

Variables significantly associated with the outcome variable when entered together in multilevel regression model

Outcome variable Ward/team type

(coefficient, (95% CI) p=)

Demographic variables


Occupation

(coefficient(95% CI) p=)

Other job-related variables

(coefficient (95% CI) p=)

Proportion of variance at ward level

Emotional Exhaustion

Negative value of coefficient indicates association with lower burnout

N = 1779 for model

Characteristics shown in bold on the right are significantly associated with burnout

Reference category with which others compared: Acute wards

PICU -1.9 (-4.5 to 0.6), p =0.14

CAMHS* -2.4 (-4.8 to 0.0), p = 0.05

Forensic -2.0 (-4.2 to 0.3), p=0.09

Rehabilitation -5.1 (-7.7 to -2.4, p <0.0005

Older adult -1.3 (-3.9 to 1.3), p= 0.33

CMHT 2.2 (0.03 to 4.3) p = 0.05

Crisis team -3.1 (-5.4 to -0.9), p = 0.006

Ethnic group (Reference category: White groups)

Asian groups -3.2 (-5.2 to -1.2), p = 0.002

Black African/Caribbean groups -0.20 (-1.8 to 1.5), p = 0.85

Other or Mixed groups -2.7 (-7.0 to 1.5), p = 0.21

Reference category: Nurses

Nursing assistant/support worker -3.2 (-4.6 to -1.9) p < 0.005)

Occupational therapist 0.2 (-2.6 to 3.0), p=0.86

Psychiatrist 0.4 (-1.9 to 2.7), p=0.73

Psychologist -2.1 (-5.9 to 1.6), p=0.26

Social worker -1.4 (-4.4 to 1.5), p=0.34

Ward manager -1.2 (-3.7 to 1.2), p=0.32

Other occupations** -3.4 (-5.6 to -1.1), p=0.004

Total hours worked: 0.11 per hour (0.06 to 1.16) p<0.0005

Time on ward (Reference category: < 1 yr)

1-2 years: 1.8 (0.12 to 3.4) p=0.04

3-4 years: 2.1 (0.3 to 3.8) p=0.02

More than 5 years: 2.3 (0.5 to 4.1) p=0.01

Time in mental health services

Reference category: <2 yrs

2-4 years: 2.2 (-0.2 to 4.6) p=0.07

5-9 years: 3.3 (0.9 to 5.7) p=0.007

10-14 years: 2.1 (-0.5 to 4.6) p = 0.12

15 or more years: 2.4 (-0.1 to 5.0) p=0.06

5.8% with ‘empty’ model

3.3 % with ‘full’ model


69






Occupation





Cynicism


N = 1849 for model

Reference category with which others compared: Acute

PICU: 0.7 (-0.5 to 1.8), p =0.25

CAMHS: -2.1 (-3.1 to -1.0), p<0.0005

Forensic: 0.0 (-1.0 to 1.0), p=0.99

Rehab: -2.0 (-3.1 to -0.8, p=0.001

Older adult -1.6 (-2.7 to -0.4), p= 0.007

CMHT -0.8 (-1.7 to 0.2) p = 0.12

Crisis team -0.8 (-2.8 to 0.2), p = 0.12


Asian groups -1.9 (-2.8 to -1.0), p<0.0005

Black African/Caribbean groups -1.8 (-2.6 to -1.1), p<0.0005

Other or Mixed groups -1.6 (-3.7 to 0.4), p = 0.12

Male sex 1.6 (1.0 to 2.1) p<0.0005

Marital status (Reference category: single)

Married/cohabiting -0.8 (-1.4 to -0.2), p=0.01

Divorced, separated or widowed 0.9 (-0.02 to 1.8) p=0.06

Age category (Reference category: Under 25)

25-34 years 0.3 (-0.9 to 1.5) p=0.61

35-44 years -0.5 (-1.7 to 0.7) p=0.39

45-54 years -1.2 (-2.4 to 0.02) p=0.05

55 years or older -2.7 (-4.0 to –1.3) p<0.0005


Nursing assistant or support worker -1.5 (-2.1 to -0.9) p < 0.005

Occupational therapist -1.4 (-2.6 to -0.1), p=0.04


Psychologist -1.7 (-3.5 to -0.02), p=0.05

Social worker 0.3 (-1.0 to 1.6), p=0.67

Ward manager -1.1 (-2.2 to -0.04), p=0.06

Other occupations -1.3 (-2.3 to -0.3), p=0.01

None significant 3.6% with ‘empty’ model

2.5% with ‘full’ model


70






Occupation





Accomplishment

N = 1875 for model

Positive value of coefficient indicates association with lower burnout


PICU 1.2 (-4.5 to 0.6), p =0.18

CAMHS 2.0 (-4.8 to 0.0), p= 0.01

Forensic -1.3 (-4.2 to 0.3), p=0.19

Rehab 0.8 (-7.7 to -2.4, p=0.36

Older adult 0.1 (-3.9 to 1.3), p= 0.88

CMHT 1.3 (0.03 to 4.3) p = 0.07

Crisis team 2.5 (-5.4 to -0.9), p = 0.001


Asian groups 1.6 (0.2 to 3.0), p = 0.02

Black African/Caribbean groups 2.2 (-1.8 to 1.5), p<0.0005

Other or Mixed groups 0.6 (-2.4 to 3.6), p = 0.68


Nursing assistant/support worker -0.7 (-1.7 to 0.2) p-0.13



Psychologist 2.4 (-0.2 to 5.0), p=0.07


Ward/team manager 3.1 (1.5 to 4.8, p<0.0005

Other occupations 1.0 (-0.6 to 2.6), p=0.21

Time in mental health services (Reference category: <2 yrs)

2-4 years -1.3 (-2.9 to 0.3) p=0.12

5-9 years -1.4 (-3.0 to 0.2) p=0.08

10-14 years -2.3 (-4.0 to -0.7) p = 0.006

15 or more years -2.2 (-3.7 to =0.6) p=0.007

4.0% with ‘empty’ model

2.6% with ‘full model’

*Categories/linear variables significantly associated with each outcome appear in bold. Only variables with a significant association with the relevant outcome in the final multilevel model are included in the table.

**This was a mixed category of occupations containing small numbers. Therapists of various types, including art, music and drama and activity workers predominated.


71

Table 6 shows a similar set of multilevel regressions for anxiety-contentment and depression-enthusiasm. There are some resemblances with the patterns of associations described above, but again with somewhat distinctive models for each indicator. Crisis teams, CAMHS units, rehabilitation services and older adults have more positive outcomes than the reference category, general acute wards, on both these scales, and the tendency for Asian groups to report greater well-being is also repeated. Tendencies are found for people who have worked for longer on specific wards and in mental health services in general to report lower well-being, but the relationships are not linear.

Finally, Table 7 shows a similar set of analyses for intrinsic job satisfaction. Some similar patterns emerge to Table 6, with Rehabilitation and CRT staff and Bank or temporary staff and senior staff tending to report higher levels of satisfaction. Psychiatrists and Occupational Therapists also reported higher levels of satisfaction. Staff from Black and from Asian groups again gave more positive reports regarding their work.


Table 6. Multilevel models for measures of work-related well-being– demographic and occupational variables significantly associated with outcome in final model



coefficient, (95% CI) p=

Demographic variables coefficient, (95% CI) p=

Occupation Coefficient (95% CI) p=

Other job-related variables Coefficient (95% CI) p=


Anxiety-contentment scale

Positive coefficient means characteristic is associated with higher contentment, lower anxiety

N=1822


PICU 0.00 (-0.16 to 0.16), p =0.99

CAMHS* 0.18 (-0.04 to 0.33), p= 0.02

Forensic 0.17 (0.03 to 0.31), p=0.02

Rehab 0.30 (0.14 to 0.46) p<0.0005

Older adult 0.26 (0.09 to 0.41), p= 0.002

CMHT -0.12 (-0.25 to 0.01) p = 0.08

Crisis team 0.22 (0.08 to 0.36), p = 0.002


Asian groups 0.13 (0.004 to 0.25), p = 0.04

Black African/Caribbean groups 0.06 (-0.03 to 0.16), p=0.21



Nursing assistant/support worker 0.13 (0.05 to 0.21) p-0.001




Social worker -0.07(-0.24 to 0.09), p=0.38

Ward/team manager 0.07 (-0.07 to 0.21, p=0.32

Other occupations 0.23 (0.10 to 0.37), p=0.001

Time in mental health services (Reference category: <2 yrs)

2-4 years -0.12 (-0.23 to 0.04) p=0.10

5-9 years -0.21 (-0.36 to -0.08) p=0.002

10-14 years -0.19 (-0.34 to -0.04) p =0.013

15 or more years -0.16 (-0.31 to -0.02) p=0.028


1-2 years -0.13 (-0.23 to -0.04) p=0.007

3-4 years -0.16 (-0.25 to -0.6) p=0.003

More than 5 years -0.11 (-0.22 to -0.00) p=0.05

Bank or temporary staff 0.28 (0.07 to 0.49) p=0.009

7.5 % with empty model

4.3% with full model


73




Demographic variables coefficient, (95% CI) p=

Occupation Coefficient (95% CI) p=

Other job-related variables Coefficient (95% CI) p=


Depression-enthusiasm scale

N=1669


PICU 0.02 (-0.16 to 0.19), p =0.84

CAMHS 0.21 (0.04 to 0.38), p= 0.01

Forensic 0.14 (-0.02 to 0.30), p=0.08

Rehab 0.23 (0.05 to 0.41) p=0.01

Older adult n0.20 (0.02 to 0.37), p= 0.03

CMHT -0.11 (-0.26 to 0.04) p = 0.16

Crisis team 0.25 (0.09 to 0.41), p = 0.002


Asian groups 0.17 (0.04 to 0.31), p = 0.01

Black African/Caribbean groups 0.05 (-0.06 to 0.16), p=0.36


Age category (Reference category: Under 25)

25-34 years 0.01(-0.16 to 0.18) p=0.88

35-44 years 0.01 (-0.17 to 0.19) p=0.91

45-54 years 0.08 (-0.10 to 0.27) p=0.39

55 years or older 0.36 (0.15 to 0.57) p=0.001


Married/cohabiting 0.04 (-0.04 to 0.13), p=0.31

Divorced, separated or widowed -0.14 (-0.27 to -0.005) p=0.04

Not associated Time in mental health services (Reference category: <2 yrs)

2-4 years -0.22 (-0.38 to -0.07 ) p=0.004

5-9 years -0.30 (-0.46 to -0.15) p<0.0005

10-14 years -0.31(-0.48 to -0.14) p <0.0005

15 or more years -0.40 (-0.57 to -0.24) p<0.0005


1-2 years -0.25 (-0.36 to -0.14) p<0.0005

3-4 years -0.27 (-0.38 to -0.15) p<0.0005

More than 5 years -0.24 (-0.36 to -0.12) p<0.0005


In a senior position 0.33 (0.23 to 0.44) p<0.0005

7.4 % with empty model


*Significant associations appear in bold. Only variables with a significant association in multilevel model are shown


74

Table 7. Multilevel model for intrinsic job satisfaction – demographic and occupational variables significantly associated with outcome in final model






Occupation

Coefficient (95% CI) p=


Coefficient (95% CI) p=


Intrinsic satisfaction

N=1769

A positive coefficient indicates that a category is associated with greater intrinsic job satisfaction


PICU 0.05 (-0.15 to 0.24), p =0.62

CAMHS 0.15 (-0.03 to 0.34), p= 0.11

Forensic 0.05 (-0.12 to 0.23), p=0.54

Rehab* 0.20 (0.00 to 0.40), p=0.05

Older adult 0.12 (-0.07 to 0.32), p= 0.21

CMHT 0.07 (-0.10 to 0.23) p = 0.41

Crisis team 0.25 (0.08 to 0.42), p = 0.003


Asian groups 0.23 (0.09 to 0.36), p = 0.001

Black African/Caribbean groups 0.26 (0.15 to 0.37), p<0.0005



Married/cohabiting 0.10 (0.02 to 0.29), p=0.022

Divorced/separated or widowed 0.00 (-0.13 to 0.14) p=0.96


Nursing assistant/support worker -0.01(-0.10 to 0.08) p-0.79

Occupational therapist 0.23 (0.04 to 0.42), p=0.02

Psychiatrist 0.19 (0.04 to 0.34), p=0.02



Ward/team manager 0.02 (-0.19 to 0.24) p=0.81

Other occupations 0.14 (-0.02 to 0.31), p=0.08


In a senior position 0.30 (0.14 to 0.46) p<0.0005

6.9% with empty model


* Significant associations appear on bold. Only variables with a significant association in multilevel model are shown.


75

4.2 The relationship between indicators of morale


All the main morale indicators used in the study were significantly correlated.

Most variation in ratings of team morale was between individuals rather than teams: people’s own well-being and attitudes seem to influence their perception of team morale.

Two main components of morale emerged from principal components analysis, which can be conceptualised as an emotional strain or distress component and an engagement and sense of achievement component.

In the absence of clear operationalisations of the concept of morale, we have chosen to use in our main analyses in the study well-established measures of staff well-being and satisfaction that allow comparison with many other studies. However, further aims of the study were to gain a better understanding of the measurement of staff well-being and attitudes to work by examining (a) the relationships between the main scales that are relevant to morale and (b) the relationship between these indicators and staff ratings of their own and their team’s morale. Progress in these investigations is summarised in this section.

4.2.2 The relationship between indicators of morale

As a starting point, we examined the relationships between the full set of potential indicators of morale examined in the study. Table 8 shows the correlations between these scores. Every correlation is highly significant (p<0.0005): significance levels are thus omitted from the table to avoid monotony. Numbers for these correlations range from 2104 to 2204, depending on numbers of missing values for each item. For the analyses in this section we have used the full data set, including respondents whose ward was unknown and who were therefore removed from most analyses in other sections, The high levels of significance throughout reflect large numbers, but the size of correlations varies from weak to fairly strong. The job-related well-being scales, intrinsic satisfaction and the Maslach burnout inventory sub-scales tended especially to have substantial correlations with one another.

Ratings of own morale had moderate to fairly strong correlations with all other variables, correlating more highly with scores based on concepts involving emotional strain or distress. The correlation between own morale and team morale was very strong at 0.73. Team morale scores also had substantial correlations with all other individual morale indicators, but generally these were slightly weaker than those between rating of own morale and other morale indicators. In order to explore level of agreement between staff on the same ward regarding team morale, we calculated


76

intra-class coefficient for this measure, comparing it with intra-class correlation for individual morale. The intra-class correlation for team morale was 0.26 (95% CI 0.16 to 0.36), compared with 0.09 (0.07 to 0.13) for individual morale, Intra-class correlations for other individual morale indicators ranged from 0.04 (GHQ score and cynicism) to 0.07 (depression-enthusiasm and contentment-anxiety). Thus as one would predict, ratings of team morale seem to produce greater within-ward agreement than those of individual morale. However, most variation in ratings of team morale is between individuals rather than between teams, suggesting people’s own well-being and attitudes influence this more than the state of the team as a whole.

Table 8. Pearson’s Correlations between indicators of morale – all correlations in table highly significant (p<0.0005)

Emotio

nal

exhaus

tion

Cynic

ism

Person

al

accom

plishm

ent

Anxie

ty-

conte

ntme

nt

Depre

ssion-

enthus

iasm

GHQ -

12

(Log)

Intri

nsic

satisf

actio

n

Job

involve

ment

Own

moral

e

Tea

m

mor

ale

Emotional

exhaustion

1.0 0.52 0.35 -0.64 -0.66 0.65 -0.47 -0.14 -0.57 -0.45

Cynicism

0.52 1.0 -0.21 -0.35 -0.40 0.40 -0.30 -0.12 -0.31 -0.23

Personal

accomplish

ment

-0.21 -0.21 1.0 0.25 0.35 -0.24 0.32 0.15 0.22 0.15

Anxiety-

contentmen

t

-0.66 -0.35 0.25 1.0 0.76 0.58 0.53 0.15 0.57 0.46

Depression-

enthusiasm

-0.64 -0.40 0.35 0.76 1.0 0.62 0.63 0.25 0.68 0.52

GHQ 12

score (log

transformed

)

0.65 0.40 -0.24 0.58 0.62 1.0 -0.49 -0.14 -0.60 -0.45

Intrinsic

satisfaction

-0.47 -0.30 0.32 0.53 0.63 -0.49 1.0 0.29 0.56 0.48

Job

involvement

-0.14 -0.12 0.15 0.15 0.25 -0.14 0.29 1.0 0.27 0.25

Rating of

own morale

-0.57 -0.31 0.22 0.57 0.68 -0.60 0.56 0.27 1.00 0.73

Rating of

team

morale

-0.45 -0.23 0.15 0.46 0.52 -0.45 0.48 0.25 0.73 1.0


77

4.2.3 Exploration of common factors underlying morale indicators

The second step in our analysis was to conduct a principal components analysis, including all the variables in the table above, with the aim of assessing how far these variables load onto a smaller number of components. Tables 9 and 10 show the outcome of this analysis and Figure 1 the loading plot demonstrating the relationship of each variable to the two factors derived. Principal components analysis was used with an oblimin rotation, appropriate where correlations between the included variables are substantial. An eigenvalue of 1.0 was pre-specified as the threshold level for retention of components.

Two components were derived from this analysis. The first accounted for almost half (49%) of the total variance in scores on the morale variables. Not surprisingly given the large amount of variance explained, most of the morale variables were highly correlated with this component, with the only correlations below 0.5 for job involvement and personal accomplishment. The second component only just met the criteria for inclusion, explaining 11% of variance. Job involvement loaded very highly onto this, with personal accomplishment and intrinsic satisfaction loading next most highly and emotional exhaustion and cynicism having very low correlations with it. These two components might be conceptualised as representing an emotional strain or distress component of overall morale and a second component representing engagement with and sense of achievement from work. The rating of own morale had substantial correlations with both, suggesting it is potentially appropriate as a very simple summary score for morale.


78

Table 9. Variance explained through principal components analysis

Total Variance Explained

Initial Eigen values

Extraction Sums of Squared

Loadings

Rotation

Sums of

Squared

Loadingsa

Component Total

% of

Variance

Cumulative

% Total

% of

Variance

Cumulative

% Total

1 4.937 49.371 49.371 4.937 49.371 49.371 4.767

2 1.057 10.573 59.944 1.057 10.573 59.944 2.056

3 .968 9.677 69.621

4 .786 7.863 77.484

5 .609 6.088 83.572

6 .480 4.802 88.374

7 .429 4.285 92.659

8 .286 2.856 95.515

9 .257 2.571 98.085

10 .191 1.915 100.000

Extraction Method: Principal Component Analysis.

a. When components are correlated, sums of squared loadings cannot be added to obtain a total variance.


79

Table 10. Structure matrix from Principal Components Analysis of main morale variable

Structure matrix

Component

1 2

MEAN SCALE SCORE - Warr anxiety <-

> contentment (hi score - hi cont)

.819 .284

MEAN SCALE SCORE - Warr depression

<-> enthusiasm (hi score - hi enth)

.861 .438

MEAN SCALE SCORE - Intrinsic Job

Satisfaction

.684 .551

Log of MEAN SCALE SCORE - GHQ

Likert scale

-.804 -.209

How would you rate your morale at

work over the past few weeks?

.792 .488

How would you rate the morale of the

Ward team over the past few weeks?

.648 .490

MEAN SCALE SCORE - Job Involvement .151 .815

MEAN SCALE SCORE - Burnout -

Personal accomplishment (hi score -

low accomplishment)

.303 .527

MEAN SCALE SCORE - Emotional

Exhaustion (hi score - hi exhaustion)

-.851 -.119

MEAN SCALE SCORE -

Depersonalisation (hi score - hi depers)

-.596 -.025


Rotation Method: Oblimin with Kaiser Normalization.


80

Table 11. Structure matrix from Principal Components Analysis of main morale variable

Structure matrix

Component

1 2

MEAN SCALE SCORE - Warr anxiety <-

> contentment (hi score - hi cont)

.819 .284

MEAN SCALE SCORE - Warr depression

<-> enthusiasm (hi score - hi enth)

.861 .438

MEAN SCALE SCORE - Intrinsic Job

Satisfaction

.684 .551

Log of MEAN SCALE SCORE - GHQ

Likert scale

-.804 -.209

How would you rate your morale at

work over the past few weeks?

.792 .488

How would you rate the morale of the

Ward team over the past few weeks?

.648 .490

MEAN SCALE SCORE - Job Involvement .151 .815

MEAN SCALE SCORE - Burnout -

Personal accomplishment (hi score -

low accomplishment)

.303 .527

MEAN SCALE SCORE - Emotional

Exhaustion (hi score - hi exhaustion)

-.851 -.119

MEAN SCALE SCORE -

Depersonalisation (hi score - hi depers)

-.596 -.025


Rotation Method: Oblimin with Kaiser Normalization.


81

Figure 1. Loading plot for the two components derived from principal components analysis of the main morale variables


82

4.3 Testing the demand, support, control model of morale


Job control, work demand, support from manager and support from colleagues all had significant linear effects in the expected directions on anxiety, depression, emotional exhaustion and intrinsic satisfaction.

Only support from colleagues and job control showed a significant effect on personal accomplishment.

The effects of demand, control and support are applicable across all service types.

Analysis of curvilinear relationships suggest there is a saturation point beyond which additional support ceases to add value to morale and a floor effect for control, which only improves morale once it reaches moderate levels.

Tests of three way interactions suggest support and control can both serve to mitigate the negative impact of work demands on staff anxiety and depression, but not to the same extent on emotional exhaustion and personal accomplishment.

Our first step in investigating factors that may be associated with mental health staff morale was to test in this sample of mental health staff the dominant model in occupational psychology, Karasek’s Demand-Control-Support model, investigating whether and in what form it is applicable to this group.

The analyses reported in this section, though not elsewhere in the report, are based on a sample that excludes ward or team managers as their responses to the item on manager support were to a slightly different question from the remainder of the sample: whereas other staff were asked to rate support from their ward or team manager, ward managers were asked to rate support from their immediate manager. Also excluded from this section for reasons of having categories that are excessively heterogeneous are the ‘other’ group of staff, the majority specialist therapists or activity workers. These exclusions resulted in a working sample for the analyses in this section of 1,870 staff, denuded for some analyses by the list-wise deletion of missing cases. Apart from the exclusion of these occupational groups, the sample for these analyses was essentially very similar in its demographic and occupational characteristics to the main sample, and we have not therefore presented these characteristics separately in this section.

The main morale indicators tested in this section for both additive and multiplicative associations with demands, support and control were emotional exhaustion and personal accomplishment from the Maslach


83

Burnout Inventory, anxiety and depression from Warr’s job-related well-being scales, used in this instance in their single pole form, and intrinsic satisfaction. To avoid collinearity issues with testing of multiplicative models (see below) all measures were standardised to a 1 to 5 scale before use in the analyses reported below, and the personal accomplishment measure reversed so that here, unlike elsewhere in the report, high scores indicate high burnout and vice versa.

Ethnic group, service type and occupational group were found to be significantly associated with most of the outcomes when entered in an empty multilevel regression with individuals nested within wards, as in Section 4.1 These were therefore retained as control variables throughout the modelling process testing the demand-support-control model.

The initial step in testing Karasek’s model in this group was to enter the job demands, control, colleagues support and manager support to test the model of additive effects. This was repeated for each main outcome. Table 12 shows the resulting model, including control variables, while the model deviance statistics and variance explained at each stage in building the model appear in Table 14.

Table 12 shows that the main effects: each of job control, ward or team manager support, and colleague support had negative linear effects upon each of anxiety, depression, and emotional exhaustion. Similarly, significant positive effects of work demands were also found for each of these outcomes. Likewise, both support variables and control had a positive impact upon intrinsic satisfaction, with demands having a negative impact. Only for personal accomplishment were the effects of our antecedents less powerful, with only colleague support and job control attaining significant positive effects at the p < 0.005 level. The estimated coefficients for fixed and random effects for each outcome at this stage of the analysis are given in Tables 12 and 14 respectively. Note that demands, support and control together reduced both the initial unexplained within-service unit and between service unit variance for each outcome by a further substantial amount on top of that already explained by the control variables (between 9% and 42%, and between 29% and 48%). The model deviance statistics, given by the -2*log likelihood statistic, were also dramatically reduced, though due to the estimation method applied (residual maximum likelihood) and the non-nested nature of these models with respect to those (control variables only) emerging from the first stage of the model building process, formal tests of the reduction in deviance (i.e. the improvement in the fit of the model as a whole) were not possible.

Allowing slope variation for each predictor in turn (i.e. the effect of each predictor to vary by service) did not produce statistically significant slope variance coefficients, nor significantly improve the fit of the model for any outcome, indicating that the effects of demands, control and support described above were applicable across the wards and teams within the sample.


84

Table 12. Additive testing of Karasek model: Main effects of demand, support and control

B coefficients for support, demands and control fixed effect upon each Outcome

Predictors INTSAT

(N = 1694)

ANX

(N = 1664)

DEP

(N = 1664)

EE

(N = 1688)

PA

(N = 1672)

(Occupational Group: Total fixed effect) F = 1.048 F = 0.845 F = 1.674 F = 0.787 F = 1.147

(Service Unit Type: Total fixed effect) F = 1.370 F = 3.444* F = 2.075 F = 1.750 F = 3.395*

(Ethnic Group: Total fixed effect) F = 8.797* F = 1.716 F = 3.362 F = 3.121 F = 5.985*

Work Demands (standardised) -0.144* 0.275* 0.258* 0.768* -0.056*

Colleague Support (standardised) 0.110* -0.059* -0.115* -0.115* 0.191*

Ward or Team Manager Support (standardised) 0.133* -0.049* -0.084* -0.148* 0.051

Job Control (standardised) 0.442* -0.118* -0.192* -0.257* 0.250*

* P < 0.005


85

We then added squared effects of each of these predictors to test for any curvilinear relationships with each outcome. This is reported in Table 13.

Table 13 shows that the only statistically significant effects at the p < 0.005 level were for colleague support on intrinsic satisfaction and depression, and of job control on personal accomplishment. In the first two instances the curvilinear relationship reflected a ceiling or basement effect. In other words, the benefit of support diminished in strength as the amount of support increased and was not apparent for very high levels of support, suggesting that there comes a saturation point at which giving further support is no longer worthwhile. Conversely, the benefits of control on personal accomplishment only became apparent once control reached moderate levels, suggesting that a very small amount of control is no better than none at all.

Finally, we examined the multiplicative effects between demands, support and control. These consisted of the five two-way interactions and the two three-way interactions between either support variable, and one or both of demands and control. Following the advice of Edwards (2008), we retained the squared effects of each variable within the model to ensure that any interactions effects detected were not caused by underlying polynomial effects of a single variable.

In contrast to the additive model, the support for the multiplicative Karasek model, according to which the interactions amongst demands, control and support impact upon well-being, is less strong. Table 13 reports the models that include the key interaction terms; for these analyses we have also indicated results at the p < 0.05 level due to the increased measurement error and reduced power inherent in testing such multiplicative effects.

In general interaction effects are small in comparison with the main effects reported above. Across the different measures of well-being the pattern of statistically significant interaction effects varies. However, for four of the outcomes the combined effects of the multiplicative predictors do reduce the unexplained within-service unit variability by a small amount (1%) on top of that already explained by the control variables and main effects – further details for each outcome are given in Table 14.


86

Table 13. Testing of Multiplicative Karasek model: Main and curvilinear effects, and interaction effects of demands, support and control, having controlled for retained control variables

B coefficients for support, demands and control fixed effects upon each

outcome

Predictors INTSAT

(N = 1694)

ANX

(N = 1664)

DEP

(N = 1664)

EE

(N = 1688)

PA

(N = 1672)

(Occupational Group: Total fixed effect) F = 1.209 F =0.858 F = 1.476 F = 0.837 F = 1.558

(Service Unit Type: Total fixed effect) F = 1.311 F = 3.498* F = 2.092 F = 1.761 F = 3.803*

(Ethnic Group: Total fixed effect) F = 8.795* F = 1.597 F = 3.695 F = 3.460 F = 6.262*

Work Demands (standardised) -0.144* 0.274* 0.249* 0.752* -0.064

Colleague Support (standardised) 0.106* -0.047 -0.096* -0.114* 0.191*

Ward or Team Manager Support (standardised) 0.130* -0.036 -0.075* -0.130* 0.063

Job Control (standardised) 0.441* -0.122* -0.185* -0.247* 0.262*

Work Demands (standardised) squared -0.029† -0.018 0.012 0.025 0.009

Colleague Support (standardised) squared -0.044* 0.027 0.055* 0.034 0.007

Ward or Team Manager Support (standardised) squared 0.003 0.022 0.010 0.022 0.025

Job Control (standardised) squared 0.015 -0.021 0.002 0.022 0.100*

Interaction: Work Demands (standardised) * Job Control (standardised) 0.044† -0.026 -0.045† -0.060 0.002

Interaction: Work Demands (standardised) * Colleague Support (standardised) -0.011 0.001 0.005 0.036 -0.056

Interaction: Work Demands (standardised) * Ward or Team Manager Support

(standardised) -0.016 -0.032† 0.003 -0.016 -0.043


87

B coefficients for support, demands and control fixed effects upon each

outcome

Predictors INTSAT

(N = 1694)

ANX

(N = 1664)

DEP

(N = 1664)

EE

(N = 1688)

PA

(N = 1672)

Interaction: Job Control (standardised) * Colleague Support (standardised) 0.036 -0.003 -0.047† -0.063 0.015

Interaction: Job Control (standardised) * Ward or Team Manager Support

(standardised) -0.048* 0.011 0.035 0.026 -0.047

Interaction: Work Demands (standardised) * Job Control (standardised) * Ward or

Team Manager Support (standardised) 0.019 -0.017 -0.025 -0.025 0.007

Interaction: Work Demands (standardised) * Job Control (standardised) * Colleague

Support (standardised) 0.023 0.036† 0.050† 0.048 -0.012

* p < 0.005

† p < 0.05 (marked for multiplicative effects only)


88

Table 14. Variance explained by Karasek model - Random effects and model deviance statistics for models at each stage of the model building process

INTSAT

(N= 1694)

ANX

(N= 1664)

DEP

(N= 1664)

EE

(N= 1688)

PA

(N= 1672)

Baseline σ2ε 0.602 0.522 0.664 2.134 1.152

Baseline σ2u 0.040 0.034 0.042 0.140 0.051

Baseline (Unconditional)

variance components model

with no predictor variables

Baseline -2LL (Deviance)‡ 4032 3722 4121 6153 5044

σ2ε 0.590 0.518 0.657 2.071 1.138

% baseline σ2ε explained at this step 2% 1% 1% 3% 1%

σ2u 0.034 0.018 0.031 0.097 0.031

% baseline σ2u explained at this step 15% 47% 26% 31% 39%

Model with control variables

that are significantly associated

with only

-2LL 4023 3718 4120 6097 5029

σ2ε 0.335 0.420 0.514 1.367 1.036

% baseline σ2ε further explained at this

step

42% 19% 22% 33% 9%

σ2u 0.015 0.008 0.012 0.031 0.014

Retained control variables and

main linear effects of demands,

support and control

% baseline σ2u further explained at

this step

48% 29% 45% 47% 33%


89

INTSAT

(N= 1694)

ANX

(N= 1664)

DEP

(N= 1664)

EE

(N= 1688)

PA

(N= 1672)

-2LL 3086 3375 3710 5393 4874

σ2ε 0.329 0.416 0.508 1.364 1.027

% baseline σ2ε further explained at this

step

1% 1% 1% 0% 1%

σ2u 0.015 0.008 0.012 0.031 0.012

% baseline σ2u further explained at

this step

0% 0% 0% 0% 0%

Retained control variables,

main

linear, curvilinear and

multiplicative effects of

demands,

support and control

-2LL 3117 3418 3747 5436 4905

σ2u = unexplained variance between wards -2LL, F = model deviance on F degrees of freedom

‡ Models fitted using Residual Maximum Likelihood, hence precluding direct comparison/testing of change in model deviance between competing non-nested models (i.e.

those containing different predictors)#


90

The only support from the tests for two-way interactions at the p < 0.005 level is for the combination of ward or team manager support and control on intrinsic satisfaction; specifically, the importance of having control in terms of boosting satisfaction diminishes when ward or team manager support is high. There is weaker evidence that the importance of job control in increasing satisfaction is amplified when work demands are high. This effect is repeated when the outcome variable is depression, i.e. the importance of job control on reducing depression is enhanced when work demands are high. Likewise, the predicted positive impact of work demands on anxiety diminishes slightly as ward or team manager support increases. Taking these results together, it appears that high levels of support and control respectively can mitigate the worst effects of demands on anxiety and depression.

Finally, for both anxiety and depression, similar three-way interactions between demands, control and colleague support were found. These both indicated that, as colleague support decreases, the importance of job control in mitigating the impact of demands upon well-being (both anxiety and depression) is enhanced. This suggests that support and control are, to an extent, interchangeable buffers against the negative impact of demands.

In the cases of personal accomplishment and emotional exhaustion, none of the interaction effects were found to be statistically significant.

4.4 The relationship between Demand, Support and Control and type of service


There are significant differences between types of service in perceived demand, control, and support from managers and control.

Among inpatient ward types, general acute wards are characterised by high levels of perceived demands and relatively low levels of control. Rehabilitation ward and PICU staff report the lowest work demands, rehabilitation and CAMHS staff the greatest control, and rehabilitation wards and PICU staff the highest levels of support from ward managers. Colleague support is rated highly across ward types.

CMHT staff have a distinctive profile which goes some way towards accounting for the pattern of high emotional exhaustion combined with high personal accomplishment. They score substantially higher than any other group for work demands and lowest for support from managers, but also highest for job control and support from colleagues.


91

CRT staff reports suggest a rather benign profile with relatively low job demands, high support from colleagues and high job control.

Some, though not all, of the associations with demographic, job and service type variables disappear with adjustment for demand, support and control variables.

Results from Section 4.1 demonstrated the relationships between measures of morale and the type of service in which people work, as well as with various job and demographic characteristics. Section 4.3 has demonstrated the robustness of the Demand-Support-Control model of work stress, the dominant model in occupational psychology in this sample. Subsequent chapters will test whether a number of additional types of variable, relating for example to built environment, adverse events and experiences, organisational aspects of wards and demographic and geographical characteristics, add to the explanatory power of the Demand-Support-Control model.

Before going on to examine these additional explanatory variables, this brief chapter will integrate Sections 4.1 and 4.3 by examining how far variations in Demand, Support and Control can account for the variations between service types described in Section 4.1. Section 4.3 presented analyses with ward and team managers removed from the sample because their responses regarding the supportiveness of managers, where provided, related to more senior managers in the Trust rather than to themselves. Given the broader focus of this and subsequent chapters and the importance of this group, we include them wherever data are available, but their somewhat anomalous status in relation to this item should be noted. Thus this Chapter relates to a different and slightly larger sample than the preceding chapter.

Table 15 describes the variation between types of service in the main Demand, Support and Control variables. For each, there are statistically significant between group variations. CMHTs show a very distinctive pattern, with the highest mean score for both job demands and control, and the highest score for support from colleagues but the lowest for manager support. Rehabilitation wards, found in Section 4.1 to have a particularly benign profile in terms of indicators of well-being, have both relatively low levels of demands and relatively high levels of control, as well as high levels of support from both managers and colleagues. Thus on initial inspection, these patterns appear to fit well with the variations in well-being and satisfaction previously described.

In order to explore how far the associations with service type and with demographic and occupational variables that were described in Section 4.1 are accounted for by a Demand-Support-Control model, we explored the effects of adding the Karasek variables to the models obtained derived in Section 4.1 for the Maslach Burnout Inventory variables. Table 16 shows results from this.


92

Table 15. Perceived demand, control and support in wards, crisis teams and community mental health teams across England

General

acute

wards

N =721

CAMHS

wards

N=189

Forensic

Wards

N=219

Older

adult

wards

N=157

Rehab

wards

N=137

PICUs

N=148

Community

Mental

Health

Team

N=258

Crisis

Resolution

Team

N=216

All

ANOVA

Between

groups

difference

F p

Work Demands: Mean (s.d.)

N=2012 Max score=5

2.91

(0.99)

2.58

(0.92)

2.73

(1.00)

2.66

(1.05)

2.47

(0.94)

2.47

(0.87)

3.36

(1.03)

2.55

(0.91)

2.80

(1.01)

21.7 0.001

Job Control: Mean (s.d.)

N=2009 Max score=5

2.99

(0.89)

3.25

(0.87)

3.03

(0.94)

3.04

(0.79)

3.36

(0.80)

3.04

(0.82)

3.65

(0.76)

3.31

(0.76)

3.17

(0.87)

20.3 0.001

Support from Ward Manager

Mean (s.d.) N=1993

Max score=5

3.31

(1.21)

3.55

(1.06)

3.40

(1.18)

3.64

(1.13)

3.65

(1.09)

3.50

(1.09)

3.28

(1.14)

3.41

(1.16)

3.41

(1.16)

3.4 0.001

Support from Colleagues

Mean (s.d.) N=2009

Max score=5

3.60

(0.95)

3.63

(0.86)

3.45

(0.91)

3,59

(0.90)

3.56

(0.83)

3.52

(0.92)

3.79

(0.94)

3.68

(0.92)

3.61

(0.92)

2.7 0.008


93

With regard to Emotional Exhaustion, associations with job related variables such as occupation and hours worked are no longer independently significant following the addition of job demands, work control, support from colleagues and support from managers, and the association with ethnic group has changed, so that now being a member of a Black group appears associated with higher emotional exhaustion. The effect of job demands is particularly large, with a mean increase of more than 6 points on the emotional exhaustion scale (used in these analyses in its original form which is scored from 0 to 54) associated with a one point increase on the perceived job demands scale (scored from 1 to 5). With all variables entered, a relatively small significant tendency for an association between being a CMHT member and higher Emotional Exhaustion persists, but all other associations with ward type are now not significant, suggesting that the Demand-Support-Control variables largely account for the associations found between service type and Emotional Exhaustion.

The same process was followed for Cynicism. Unlike with Emotional Exhaustion, most of the job and, in particular, socio-demographic associations previously found remained intact once the demand, support and control variables had been added (Table 16).

Modelling Personal Accomplishment in the same way, previously found associations with working in a CRT, being a ward manager, shorter overall length of service and Black or Asian ethnic background remained intact once the Demand-Support-Control variables were added to the model.

Table 17 describes the results of adding demand, control and support variables to the models obtained in Section 4.1 for the depression-enthusiasm and anxiety-contentment scales. Most relationships between demographic variables and these scales remained, suggesting these associations are not wholly explicable in terms of variations in demand, control and support, but associations between working in CAMHS, forensic and older adult units and in crisis teams and more positive scores were no longer present in a model including the demand, control and support variables.

Finally, Table 18 examines the effects of adding Demand, Control and Support variables in the same way to a multilevel regression with intrinsic satisfaction as the outcome variable. Working in a crisis team or on a rehabilitation ward and being a senior or a bank agency staff member no longer have independent associations with satisfaction, suggesting that the differences in demand support and control variables shown in Table 15 explain these differences in satisfaction and in staff status.


94

Table 16. Multilevel regression analyses for Maslach Burnout Inventory variables with Demand, Support and Control variables included.

Variables significantly associated with outcome in multilevel regression Outcome variable

Ward/team type variables


Demographic variables significantly associated with outcome


Occupation Other job-related variables

Demand-support-control variables

Exhaustion

Negative value of

coefficient indicates

association with

lower burnout

N = 1701 for model

Reference category with

which others compared:

Acute wards

PICU: 1.2 ((-0.8 to 3.2), p

=0.24

CAMHS: 0.8 (-1.1 to 2.7), p

= 0.40

Forensic: -0.7 (-2.4 to 1.1),

p=0.45

Rehabilitation:-1.5 (-3.6 to

0.6) p=0.17

Older adult: 0.1 (-2.0 to 2.1),

p= 0.95

CMHT: 1.8 (0.02 to 3.5) p

= 0.05

Crisis team: 0.6 (-1.2 to 2.4),

p = 0.50

Ethnic group (Reference

category: White groups)

Asian groups: -1.0 (-2.7 to

0.6), p = 0.22

Black African/Caribbean

groups: 1.8 (0.4 to 3.1),

p = 0.01

Other or Mixed groups: -2.2

(-5.7 to 1.4), p = 0.23

Not now significant Total hours worked:

Not now significant

Time on ward: Not

now significant

Time in mental

health services: Not

now significant

Work demands: 6.5

per point on scale

(6.0 to 7.0):

P<0.0005

Job control: -1.7 per

point on scale (-2.3

to -1.1) P=0.002

Support from

manager: -1.1 per


to -0.6) P<0.0005

Support from

colleagues: -0.9 per


to -0.3) P<0.0005

Cynicism Reference category with Ethnic group (Reference Occupation – not None significant Work demands: 1.7


95

Negative value of


association with

lower burnout

N = 1849 for model


Acute

PICU: 1.4 (0.4 to 2.4) p

=0.009

CAMHS: -1.3 (-2.3 to -0.3),

p=0.01

Forensic: 0.3 (-0.6 to 1.3),

p=0.47

Rehab: -0.8 (-1.9 to -0.3),

p=0.14

Older adult: -1.1 (-2.1 to -

0.0), p= 0.05

CMHT: -0.8 (-1.7 to 0.1) p =

0.09

Crisis team: 0.3 (-0.6 to 1.2),

p = 0.48


Asian groups: -1.3 (-2.2

to -0.4), p=0.005


groups: -1.3 (-2.0 to -

0.5), p=0.001

Other or Mixed groups: -1.4

(-3.3 to 0.6), p = 0.17

Male sex: 1.6 (1.1 to 2.1)

p<0.0005

Marital status: Not now

significant

Age category (Reference

category: Under 25)

25-34 years: 0.2 (-0.9 to

1.3) p=0.72

35-44 years: -0.9 (-2.0 to

0.3) p=0.13

45-54 years: -1.7 (-2.9 to

0.5) p=0.004

55 years or older : -2.7 (-

4.0 to –1.4) p<0.0005

now significant per point on scale

(1.4 to 1.9)p<0.0005

Job control: -1.7 per


to -1.1) p<0.0005

Support from

manager: -0.3 per


to -0.1) p=0.006

Support from

colleagues: -0.1 per

point on scale (-0.4 to

0.2) P=0.40

Personal Reference category with Ethnic group (Reference Reference category: Time in mental Work demands: -0.4


96

Accomplishment

N = 1788 for model

Positive value of


association with

lower burnout


Acute

PICU: 0.6 (-1.0 to 2.2), p

=0.47

CAMHS: 1.5 (-0.0 to 2.9), p=

0.05

Forensic: -1.2 (-2.6 to 0.2),

p=0.09

Rehab: -0.3 (-1.9 to 1.3),

p=0.70

Older adult: -0.2 (-1.8 to

1.3), p= 0.77

CMHT: 0.4 (-1.0 to 1.7) p =

0.59

Crisis team: 1.5 (0.1 to

2.9), p = 0.04


Asian groups: 1.6 (0.3 to

3.0), p = 0.02


groups: 2.2 (1.1 to 3.3),

p<0.0005

Other or Mixed groups: 0.0

(-2.9 to 3.0), p = 0.98

Nurses

Nursing assistant: -

0.8 (-1.7 to 0.2) p-

0.12

Occupational

therapist: -0.6 (-2.5

to 1.3), p=0.29

Psychiatrist: 0.6 (-

0.9 to 2.1), p=0.15

Psychologist: -0.0 (-

2.6 to 2.6), p=0.99

Social worker: -0.7

(-2.6 to 1.2),

p=0.54

Ward/team

manager: 1.7 (1.5

to 4.8, p=0.04)

Other occupations:

0.1 (-1.6 to 1.4),

p=0.21

health services

(Reference

category: <2 yrs)

2-4 years: -0.8 (-2.3

to 0.8) p=0.33

5-9 years: -0.9 (-2.4

to 0.7) p=0.27

10-14 years: -1.9

(-3.6 to -0.3) p =

0.02

15 or more years:

-1.9 (-3.4 to - 0.3)

p=0.02

per point on scale (0.8

to 0.04) P=0.08

Job control: 2.1 per

point on scale (1.6 to

2.6) p<0.0005

Support from

manager: 0.5 per


0.9) p= 0.002

Support from

colleagues: 1.2 per


1.6) P<0.0005


97

Table 17. Multilevel regression analyses for Depression-enthusiasm and Anxiety-contentment scales with Demand, Support and Control variables included.

Variables significantly associated with outcome in multilevel regression


variables


Demographic

variables

coefficient, (95%

CI) p=

Occupation Other job-related variables Demand-support-

control variables

Anxiety-

contentment scale

Positive coefficient

means characteristic

is associated with

higher contentment,

lower anxiety

N=1737



Acute

PICU: -0.12 (-0.25 to 0.01),

p =0.06

CAMHS: 0.05 (-0.07 to

0.16), p= 0.44

Forensic: 0.13 (0.02 to

0.24), p=0.02

Rehab: 0.11 (-0.02 to 0.24)

p=0.10

Older adult: 0.16 (0.04 to

0.29), p= 0.011

CMHT: -0.18 (-0.29 to

0.09) p = 0.001

Crisis team: 0.05 (-0.06 to

0.16), p=0.39

Ethnic group

Not now significant

Occupation not

now significant


Not now significant

Time on ward (Reference

category: < 1 yr)

1-2 years: -0.12 (-0.21 to -

0.04) p=0.005

3-4 years: -0.15 (-0.25 to -

0.6) p=0.001

More than 5 years: -0.08 (-0.22

to -0.00) p=0.10

Bank or temporary staff: 0.19

(0.01 to 0.37) p=0.04

Work demands: -

0.24 per point on

scale (-0.28 to -

0.21) p<0.0005


point on scale (0.14

to 0.21) P<0.0005

Support from

manager: 0.08 per


to 0.10) P<0.0005

Support from

colleagues: 0.09 per


to 0.14) P<0.0005


98

Depression-

enthusiasm scale

N=1594



Acute

PICU: -0.10 (-0.25 to 0.04),

p=0.15

CAMHS: 0.09 (-0.04 to

0.22), p= 0.19

Forensic: 0.12 (0.00 to

0.25), p=0.05

Rehab: 0.04 (-0.11 to 0.19)

p=0.58

Older adult: 0.12 (-0.02 to

0.26), p= 0.10

CMHT: -0.21 (-0.33 to -

0.08) p = 0.001


0.20), p = 0.26

Ethnic group: Not

now significant

Age category

(Reference category:

Under 25)

25-34 years: 0.01 (-

0.15 to 0.14) p=0.97

35-44 years: 0.02 (-

0.13 to 0.18) p=0.80

45-54 years: 0.08 (-

0.08 to 0.23) p=0.35

55 years or older:

0.24 (0.06 to 0.43)

p=0.01

Marital status


single)

Married/cohabiting:

0.02 (-0.06 to 0.09),

p=0.65

Divorced,

separated or

widowed: -0.12 (-

0.24 to -0.01)

p=0.04

Occupation not

now significant

Time in mental health

services (Reference category:

<2 yrs)

2-4 years: -0.11 (-0.24 to 0.03 )

p=0.12

5-9 years: -0.15 (-0.28 to -

0.1) p=0.04

10-14 years: -0.17(-0.32 to -

0.02) p=0.03

15 or more years: -0.23 (-

0.38 to -0.08) p=0.002

Time on ward (Reference

category: < 1 yr)

1-2 years: -0.23 (-0.32 to -

0.13) p<0.0005

3-4 years: -0.26 (-0.36 to -

0.16 p<0.0005

More than 5 years: -0.22 (-

0.33to -0.11 p<0.0005

Bank or temporary staff: 0.23

(0.02 to 0.45) p=0.03

In a senior position: 0.22

(0.12 to 0.32) p<0.000

Work demands: -

0.20 per point on

scale (-0.23 to -

0.16) p<0.0005



to 0.27) P<0.0005

Support from

manager: 0.09 per


to 0.12) P<0.0005

Support from



to 0.12) P<0.0005


99

Table 18. Multilevel regression analysis for Intrinsic Job Satisfaction with Demand, Support and Control variables included

Variables associated with outcome in multilevel regression


variables


Demographic

variables

coefficient, (95% CI)

p=

Occupation Other job-

related

variables

Demand-support-

control variables

Intrinsic

satisfaction

N=1688



Acute

PICU: -0.09 (-0.23 to 0.05),

p=0.20

CAMHS: -0.02 (-0.15 to

0.11), p= 0.76

Forensic: 0.02 (-0.10 to

0.14), p=0.78

Rehab: -0.06 (-0.21 to

0.08), p=0.38

Older adult: -0.01 (-0.14 to

0.13), p= 0.95

CMHT: -0.17 (-0.29 to

0.05) p = 0.006


0.14), p = 0.76

Ethnic group


White groups)

Asian groups: 0.16

(0.05 to 0.27), p =

0.004

Black

African/Caribbean

groups: 0.18 (0.09 to

0.27), p<0.0005

Other or Mixed groups:

0.08 (-0.16 to 0.32), p =

0.51

Marital status: Not now

significant


Nursing assistant or

support worker : -0.09 (-

0.16 to -0.01) p<0.02

Occupational therapist: -

0.06 (-0.21 to 0.09), p=0.42

Psychiatrist: 0.02 (-0.10 to

0.14), p=0.71

Psychologist: -0.21 (-0.44 to

0.01), p=0.06

Social worker: -0.17 (-0.22

to 0.11), p=0.17

Ward/team manager: -0.06

(-0.22 to 0.11) p=0.48

Other occupations: -0.09 (-

0.21 to 0.04), p=0.17

Bank or

temporary

staff: Not now

significant

In a senior

position: Not

now significant

Work demands: -0.16

per point on scale (-

0.19 to -0.12)

p<0.0005



0.47) P<0.0005

Support from

manager: 0.14 per


0.16) P<0.0005

Support from



0.14) P<0.0005


100

4.5 The built environment and morale

4.5.1 Summary of results

There was variation in wards’ physical characteristics. On average, 60% of patient rooms could be observed from an optimal viewing point. 68% of wards were built with a spoke design. Shared rooms were more common than single rooms in about a third of wards. About a quarter of wards had bedrooms with ensuite facilities and about a quarter had direct access to outside space.

A minority of staff rated wards positively for design, fitness for purpose or safety.

Small ward size was positively associated with several morale indicators but tended no longer to be significant once adjustment was made for control variables.

Staff ratings of the built environment were independently highly associated with morale indicators.

4.5.2 Physical characteristics of the ward sample

Data were collected for 99 of the 100 wards participating in Module 1. Ward size ranged from 307 to 2789 metres squared (mean 718.5, S.D. 368.5). Density (metres squared per patient) ranged from 14-139 (mean 43.2, S.D. 22.5). From the optimal viewing point, a mean of 60.2% (range 0-100, S.D. 30.0) of patient room doors could be directly observed. Corridor designs were found in 25.5% of wards, with spoke designs (L-shaped, cruciform or V shaped) in 67.8%. With respect to openness, 15.1% of wards had a predominantly open design, 55.6% were not open/mostly cellular in design, with 29.3% neither open nor cellular in design. Single bedrooms were the majority accommodation in 65.6% of wards. Shared accommodation was the majority type found in 34.3% of wards. En-suite bedrooms were available in 23.9% of wards. Just under a quarter of wards were directly linked to outdoor spaces (23.2%). Most (88.9%) had a formal nursing station. The majority of nursing stations (69.3%) had a glass box or windowed room design. A minority (30.7%) had a counter or desk design. Just under a tenth (9.9%) of wards provided green/country views from the bedrooms, with 41.8% providing courtyard or garden views from the bedrooms, and 48.3% primarily views of other buildings. In bedrooms, pastel colours were found in 52.0%, with neutral colours in 29.6%. In common areas, neutral colours were most common (46.4%) followed by pastels (33.0%). In bedrooms, the predominant flooring type was carpet in 47.5% of wards, hard flooring in 52.5%. In common areas, carpet was the predominant flooring in 35.7%, hard flooring in 64.3%.

Staff ratings of the physical environment of their ward are reported in Table 19.


101

Table 19. Staff ratings of ward physical environment

Staff rating Design (%)

n=1725

Fitness of purpose (%)

n=1676

Ensuring safety (%)

n=1698

Very poor 101 (5.9) 105 (6.3) 84 (4.9)

Poor 330 (19.1) 286 (17.1) 249 (14.7)

Average 710 (41.2) 657 (39.2) 640 (37.7)

Good 488 (28.3) 500 (29.8) 582 (34.3)

Very good 96 (5.6) 128 (7.6) 143 (8.4)

Comparing ward types, mean ratings were highest for forensic wards (mean score 3.5 on a 1 to 5 scale where 3 represents a neutral point of view) and for PICUs and older people’s wards (both 3.4). They were lowest for acute wards (mean 2.9). The intra-class correlation for the extent of agreement between staff on the same ward was moderate at 0.26.

Table 20 shows adjusted and unadjusted associations between the main morale indicators and those physical environment variables that are significantly associated with them. Multilevel regression models were used as in preceding sections.

Ward design questionnaire (20-item instrument) Multi-level modelling of the effects of objective features of the built environment (WDQ) showed only one significant relationship, with ward size (measured in square metres) showing an independent effect on morale. Smaller wards were positive for all morale measures except emotional exhaustion and cynicism, where the association just failed to reach statistical significance. The association with personal accomplishment was the strongest. In most cases, however, these relationships were no longer significant with adjustment for demographical and occupational characteristics, although the associations with personal accomplishment and anxiety-contentment came very close to reaching significance and are therefore shown in the table.

Researcher rating of ward Multi-level modelling showed no independent relationships were established between any of the items in this rating scale and the five morale measures.

Staff rating of built environment A single factor was confirmed in this 3-item scale and used in analysis. Multi-level modelling showed multiple strong positive relationships with morale outcomes, including positive relationship of staff rating of the built environment with personal accomplishment and intrinsic satisfaction, and a negative relationship (positively rated environment linked to improved morale) with anxiety/depression and with emotional exhaustion and cynicism. In most cases, these associations remained highly significant with adjustment for demographic and occupational variables and also following addition to the model of Demand, Control and Support variables, albeit with reduced coefficients (Table 20).


102

Table 20. Physical environment variables significantly associated with morale indicators

Associations between physical environment variables and morale indicators at each stage: significant results only listed

Outcome variable

Stage 1 Physical environment variables that are significant in

simple model*, entered individually and unadjusted

(coefficient, (95% CI) p= )

Stage 2 Physical environment variables that remain significant with adjustment for demographic and occupational characteristics from Chapter 3 models


Stage 3 Physical environment variables that remain significant when demand, control and support variables also entered into model


Emotional exhaustion

N=1261 final model

Individual level

Staff rating of built environment: less exhaustion with better environment

-4.5 (-5.2 to -3.7) p<0.0005

Individual level

Staff rating of built environment

-4.2 (-5.0 to -3.5) p<0.0005

Individual level


-1.6 (-2.3 to -0.9) p<0.0005

Cynicism

N=1295 final model

Individual level

Staff rating of built environment: less cynicism with better environment

-1.3 (-1.7 to -1.0) p<0.0005

Individual level


-1.2 (-1.5 to -0.8) p<0.0005

Individual level


-0.4 (-0.8 to -0.8) p=0.02

Personal Accomplishment

N=1324 final model

Individual level

Staff rating of built environment: more accomplishment with better environment

1.7 (1.2 to 2.2) p<0.0005

Ward level

Size of ward (in hundreds of metres squared): more accomplishment on smaller wards

-0.2 (-0.4 to -0.1) p=0.008

Individual level


1.6 (1.1 to 2.2) p<0.0005

Ward level

Size of ward (in hundreds of metres squared)

-0.1 (-0.3 to -0.0)

p=0.06**

Individual level


0.7 (0.1 to 1.3 ) p=0.02

Anxiety-contentment

N=1290 final model

Individual level

Staff rating of built environment: more contentment/less anxiety with better environment

0.34 (0.30 to 0.38) p<0.0005

Ward level

Size of ward (in hundreds of metres squared): more contentment/less anxiety

Individual level


0.31 (0.17 to 0.36) p<0.0005

Ward level

Size of ward (in hundreds of metres squared)

-0.1 (-0.3 to -0.0) p=0.05

Individual level


0.17 (0.12 to 0.21) p<0.0005


103

on smaller wards

-0.1 (-0.3 to -0.1) p=0.02


N=1225 final model

Individual level

Staff rating of built environment: more contentment/less anxiety with better environment

0.37 (0.32 to 0.41) p<0.0005

Ward level

Size of ward (in hundreds of metres squared): more contentment/less anxiety on smaller wards

-0.2 (-0.3 to -0.0) p=0.02

Individual level


0.33 (0.28 to 0.38) p<0.0005

Individual level


0.19 (0.14 to 0.24) p<0.0005


N=1285 final model

Individual level

Staff rating of built environment: greater satisfaction with better environment

0.39 (0.34 to 0.44) p<0.0005

Ward level

Size of ward (in hundreds of metres squared): more contentment/less anxiety on smaller ward

-0.2 (-0.04 to -0.00) p=0.02

Individual level


0.38 (0.33 to 0.43) p<0.0005

Individual level


0.16 (0.12 to 0.21) p<0.0005

* As in Section 4.1, throughout multilevel regression has been used to explore associations with outcome variables, using the xtmixed command in Stata 10. The two levels are individual and ward, with individuals nested within wards.

** Adjusted results for ward level with personal accomplishment and anxiety-contentment failed because they missed significance just marginally at p<0.06.

4.6 The prevalence of adverse events and their relationship with indicators of morale


Just under a quarter of survey respondents reported that they had been bullied in the past year, and just over a third reported they had witnessed bullying. Prevalence was highest in acute settings (general adult wards, CRTs and PICUs).


104

Self-reported experiences of discrimination were also frequent at just over a quarter of the sample.

Ethnic group was the most frequent basis for discrimination. Patients were the most frequent source of discrimination, followed by colleagues and then supervisors and managers.

54% of Black African, Black Caribbean or Black Other staff reported some form of discrimination, compared with 33% of Asian staff and 20% of White staff.

Inpatient staff are much more likely to report having been attacked than community staff, with rates of staff reporting a physical attack by a patient in the past year ranging from 45% for rehabilitation staff to 76% for staff on older adult wards. 10% of CMHT and 20% of CRT staff report at least one attack.

Ward level data confirmed that older people’s wards had the highest rates of violence against staff. However, serious incidents resulting in a staff member being off sick for at least a week were had more often occurred on forensic wards and PICUs.

60% of general acute wards reported drug dealers operating on the ward in the past year.

Individual reports of having been bullied, discriminated against and verbally abused/threatened with violence were strongly associated with most morale outcomes apart from personal accomplishment, both in unadjusted models and with adjustment for demographic and occupational characteristics.

When Demand-Support-Control variables are also entered into a model, bullying and threats remained significantly associated with most morale outcomes. Discrimination loses its significance in this final model, probably reflecting its close association with bullying and job control and perceived support from ward manager and colleagues.

Little evidence was found of association between reports at ward level of adverse events and morale.

Negative events of various kinds were investigated in both the individual staff and the service manager level. Individual staff were asked to report their experiences of being bullied and discriminated against, and to estimate the frequency of various types of assault and threat. Ward and community team managers were asked to report the recent frequency in their service of a number of types of adverse event, including violence towards staff, suicides by patients and disciplinary hearings. Where possible, corroborating data were also sought from clinical governance departments, but the ability and willingness of such departments to provide such data was limited. Thus most of the ward level reports are from the 131 (out of 140) ward or team managers who were interviewed and provided relevant information.


105

4.6.2 Bullying and discrimination

Table 21 describes staff’s reports of their experiences of being bullied. Just under a quarter (23.5%) reported experiencing bullying. No operational definition was provided, so that these reports are based on their own understanding of what constitutes bullying. More than a third described witnessing someone else being bullied. Within wards, the proportion of people reporting that they had been bullied varied from 0% to 61.5% and the proportion reporting that someone else had been bullied was between 0% and 71.7%. We did not elicit further details of the nature of bullying.

Self-reported discrimination also appears in Table 21. Patients emerged as the most frequent source of discrimination, followed by colleagues and then supervisors or managers. Ethnic group was the most frequently identified basis for discrimination. Breaking this down by broad group, 155 of the 288 respondents from any Black Caribbean or African group reported discrimination of some form (54%) compared with 53 of the 159 Asian respondents (33.3%) and 289 of the 1445 White respondents (20.0%)

Table 21. Experience of bullying and discrimination in the past year among mental health staff (N=2045)

Yes n(%)

Experienced bullying (N=2003) 471 (23.5%)

Witnessed bullying (N=1980) 718 (36.3%)

Experienced any discrimination (N=2009) 542 (27.0%)

Discrimination by patients (N=2009) 288 (14.3%)

Discrimination by patients’ relatives or visitors (N=2009)

117 (5.8%)

Discrimination by colleagues (N=2009) 212 (10.6%)

Discrimination by manager or supervisor (N=2009) 176 (8.8%)

Discrimination by other perpetrator (N=2009) 29 (1.4%)

Discrimination on grounds of ethnicity (N=2009) 271 (13.5%)

Discrimination on grounds of religion (N=2009) 33 (1.6%)

Discrimination on grounds of disability (N=2009) 36 (1.8%)

Discrimination on grounds of gender (N=2009) 137 (6.8%)

Discrimination on grounds of sexual orientation (N=2009)

41 (2.0%)

Discrimination on grounds of age (N=2009) 113 (5.6%)

4.6.3 Threats and violence

Table 22 describes staff reports on violence and verbal threats. Only a small handful of respondents reported that they had not experienced any verbal


106

abuse or threats of violence over the past year, and 27.1% reported that these occurred more than once a week. Eight hundred and thirty of the 1692 who responded to a question about how often they had been physically attacked in the past year (49.1%) reported at least one incident of physical violence, 10.3% reporting that they had been attacked by six or more perpetrators. A minority reported attacks from visitors.

Table 23 summarises these data by type of service. General acute and forensic wards and CRTs had the highest reported rates of bullying, while discrimination was reported most from general acute and forensic wards and from PICUs. There were large variations between service types in reports of having experienced violence, ranging from 10.4% of CMHT staff to 75.5% of staff on inpatient wards for older adults reporting experiences of violence. In general, inpatient ward staff were much more likely to report violence than community staff. More than 80% of staff in every service type reported that they had experienced violence or verbal threats in the past year.


107

Table 22. Experience of threats and violence in the past year among mental health staff (N=2045)

None A few times or less

once a month or less

A few times a month or less

Once a week or less

A few times a week or less

Every day

Experienced verbal abuse or threats of violence from patients (N=2012)

116 (5.8%)

631

(31.4%)

248

(12.3%)

351

(17.4%)

120

(6.0%)

374

(18.6%)

172

(8.5%)

Experienced threats while working with patients who were intoxicated (N=2000)

291

(14.6%)

506

(25.3%)

240

(12.0%)

407

(20.4%)

150

(7.5%)

287

(14.4%)

119

(6.0%)

Experienced threats while taking part in physical restraint

(N=1523: ward staff only)

294

(19.3%)

331

(21.7%)

259

(17.0%)

337

(22.1%)

84

(5.5%)

184

(12.1%)

34

(2.2%)

Experienced threats from patients or visitors bringing drugs or alcohol onto the ward

(N=1521: ward staff only)

396

(26.0%)

498

(32.7%)

193

(12.7%)

260

(17.1%)

50

(3.3%)

101

(6.6%)

23

(1.5%)


108

Number of different perpetrators

One Two Three Four Five Six+

Experienced any violence from patients (N=1692)

862 (50.9%)

220

(13.0%)

203

(12.0%)

119

(7.0%)

68

(4.0%)

46

(2.7%)

174

(10.3%)

Experienced any violence from visitors (N=1596)

1497

(93.8%)

48

(3.0%)

29

(1.8%)

10

(0.6%)

5

(0.3%)

2

(0.1%)

5

(0.3%)


109

Table 23. Experience of threats, violence, bullying and discrimination in the last year in different service types

General acute wards

N =721

CAMHS

wards

N=189

Forensic

Wards

N=219

Older adult

wards

N=157

Rehab

wards

N=137

PICUs

N=148

Community

Mental

Health Team

N=258

Crisis

Resolution

Team

N=216

Experienced bullying

189/706

(26.8%)

34/186

(18.3%)

56/212

(26.4%)

23/155

(14.8%)

22/134

(16.4%)

37/145

(25.5%)

53/252

(21.0%)

57/213

(26.8%)

Witnessed Bullying

294/703

(41.8%)

66/186

(35.3%)

73/209

(34.9%)

39/151

(25.8%)

27/133

(20.3%)

50/142

(35.2%)

89/247

(36.0%)

80/209

(38.3%)

Experienced discrimination

229/709

(32.3%)

27/188

(14.4%)

68/214

(31.8%)

34/153

(22.2%)

30/135

(22.2%)

47/146

(32.2%)

55/254

(21.7%)

52/210

(24.8%)

Experienced any violence from patients

385/609

(63.2%)

80/165

(48.5%)

105/184

(57.1%)

74/98

(75.5%)

48/107

(44.9%)

79/125

(63.2%)

23/221

(10.4%)

36/183

(19.7%)

Experienced any violence from visitors

61/548

(11.1%)

4/160

(2.5%)

4/169

(2.3%)

4/112

(3.6%)

6/102

(7.8%)

14/118

(11.9%)

1/216

(0.5%)

3/171

(1.8%)


110

Experienced any verbal abuse or threats of violence from patients

699/712

(98.2%)

178/188

(94.7%)

205/211

(97.2%)

151/155

(97.4%)

127/135

(94.1%)

146/146

(100%)

202/251

(80.5%)

188/214

(87.9%)

Experienced any threats while working with patients who were intoxicated

690/710

(97.2%)

119/188

(63.3%)

141/210

(67.1%)

52/151

(34.4%)

127/135

(94.1%)

132/144

(91.7%)

242/252

(96%)

206/210

(98.1%)

Experienced any threats while taking part in physical restraint (wards only)

579/697

(83.1%)

139/186

(74.7%)

173/209

(82.8%)

130/153

(85%)

82/134

(61.2%)

126/144

(87.5%) n/a n/a

Experienced any threats from patients or visitors bringing drugs or alcohol onto the ward

646/695

(92.9%)

86/187

(46%)

124/211

(58.8%)

32/151

(21.2%)

113/133

(85.0%)

124/144

(86.1%) n/a n/a


111

4.6.4 Service managers’ reports regarding adverse events

Ward and team managers’ reports of adverse events are reported in Table 24. These show a relatively similar pattern to staff reports, with much high levels on the ward than in community teams and wards for older adults having the highest rates of assaults against staff. PICUs had high rates of violent incidents, but were less of an outlier for violence against staff, suggesting that much violence on these wards is between patients rather than against staff. On forensic wards, relatively few assaults against staff had occurred, but on 7 of the 12 wards, a member of staff had been away sick for more than a week following an assault, suggesting an incident of considerable severity. Such events were rarely reported from wards for older people, despite the high rate of violence overall on these wards. Rates reported from CMHTs and CRTs were lower than individual staff’s reports had suggested, suggesting at there may be some under-reporting at management level in these settings.

Relatively few patient suicides were reported, with the highest rates for the caseloads of community teams. Drug dealers operating on wards appeared to be predominantly a difficulty in general acute wards (reported on 60% of wards) and on forensic wards (50%).

4.6.5 Associations between individual level reports of adverse experiences and morale indicators

Our next step was to investigate relationships between a set of indicators of adverse events on wards and the main morale indicators investigated in the early sections of this report: the three components of the Maslach Burnout Inventory, the two job-related well-being scores and intrinsic job satisfaction. The adverse events variables investigated in these analyses were:

a) At individual level: Whether staff reported that they had been bullied in the past year, whether they reported any discrimination, and their report of how frequently they had experienced verbal abuse or threats of violence. Data were collected on several variables relating to reports of violence which were strongly associated with one another: the variable relating to threats was selected as the most suitable for further analyses primarily because it approximated relatively closely to a normal distribution.

b) At ward level: Number of violent incidents, number of assaults by patients against staff, whether any suicide had taken place among the team’s patients, whether there had been a serious incident enquiry involving the team, whether a member of staff had been off sick for a week or more following an attack by a patient, and whether drug dealing was reported on the ward were the variables investigated at this level.


112

Table 24. Adverse events in different service types – service managers’ reports

General acute wards N=48

CAMHS Wards N=10

Forensic Wards N=12

Older adult wards N=9

Rehab wards N=9

PICUs N=10 Community Mental Health Team N=17

Crisis resolution team N=16

All N=131

mean number for the team (standard deviation), Median

How many

violent incidents

were recorded

on the ward in

the last year?

(N=75)

42.3

(40.2)

26.0

9.3

(8.2)

7.5

50.5

(60.0)

26.5

79.7

(78.5)

61.5

13.8

(17.6)

6.5

79.6

(52.6)

104.0

n/a

n/a

45.0

(50.0)

25.0

How many

times has a

restraint using a

restraint team

occurred in the

last month?

(N=94)

2.4

(3.3)

2.0

7.8

(10.0)

0.0

1.2

(1.7)

0.0

3.9

(9.3)

0.5

4.3

(9.8))

0.0

4.3

(3.5)

3.5

n/a

n/a

3.4

(8.3)

1.0

How often have

staff been

assaulted in the

last year?

(N=119)

15.5

(15.8)

11.0

5.8

(4.3)

6.5

13.5

(12.7)

7.5

66.9

(79.8)

37.0

3.8

(5.5)

1.5

17.6

(13.3)

17.0

0.9

(1.6)

0.0

0.5

(1.1)

0.0

13.4

(27.3)

5.0

Has a member

of staff been off

22/47 3/10 7/12 1/7 4/9 7/10 3/17 0/16 47/128


113


CAMHS Wards N=10

Forensic Wards N=12


Rehab wards N=9



All N=131

sick for more

than a week

following an

assault

(n/N, %)

(47%) (30%) (58%) (14%) (44%) (70%)

(18%) (82%)

Has a current

patient

committed

suicide during

the past year

(n/N, %)

4/47

(9%)

0 1/12

(8%)

0 0 0 n/a n/a 5/97

(5%)

Has a patient

committed

suicide on leave

from the ward

in the last year

(n/N, %)

(for community

teams: has a

current patient

committed

suicide?)

9/46 (20%) 0 0 0 0 0 12/17

(71%)

9/16

(56%)

30/129

(23%)

Has there been

a serious

incident enquiry

30/47

(64%)

4/10

(40%)

8/12

(67%)

5/8

(63%)

2/9

(22%)

1/10

(10%)

12/17

(71%)

10/16

(63%)

72/129

(56%)


114


CAMHS Wards N=10

Forensic Wards N=12


Rehab wards N=9



All N=131

in the past year

(n/N, %)

Has there been

a disciplinary

hearing

involving staff in

the last year?

(n/N, %)

20/46

(44%)

2/10

(20%)

5/12

(42%)

4/8

(50%)

3/9

(33%)

3/10

(30%)

3/17

(18%)

4/16

(25%)

44/128

(34%)

Have drug

dealers been

found to be

operating on

your ward in the

last year (n/N,

%)

28/47

(60%)

1/10

(10%)

6/12

(50%)

0 1/9

(11%)

1/10

(10%)

n/a n/a 38/96

(40%)


115

Table 25 shows the results of exploring the associations between adverse events and the three Maslach Burnout Inventory scores. Multilevel regression in STATA 10 was used, following the same procedures as in previous chapters. The first column in the table shows the results of entering each adverse event indicator as the sole independent variable in a model with the relevant burnout score as the dependent variable. For those significantly associated with burnout at this stage, the demographic and job characteristics identified in Section 4.1 as significantly associated with the relevant score were then also added to the model: the second column shows the adverse events still significantly associated with burnout indicators when the initially significant adverse events significant at stage 1 were added into model together with this adjustment for the relevant demographic and occupational variables. Finally, Stage 3, shown in the final column, involved also adding the Demand-Control-Support variables to this model. For simplicity, Table 25 and the subsequent tables in this section show only the significant adverse events variables. Tables A1 to Tables A3 in Appendix 1 show fuller versions of these tables with other types of variables also included.

A limitation that needs to be noted where discrimination and bullying have been added to a model together is that these were relatively closely associated: 272/532 (51%) of those who said they had been discriminated against also reported bullying, while 272/458 (59%) of those who said they had been discriminated against also said that they had been bullied. Bullying was strongly associated with emotional exhaustion, with a mean difference of 8.0 points on this scale between those who had been bullied and those who had not in the unadjusted analysis, and an adjusted mean difference of 6.0 when other adverse events and demographic and job characteristics were adjusted for. Although the effect size fell with addition of the Demand-Support-Control variables, it remained substantial and strongly significant, suggesting an effect that cannot be subsumed under this model.

Frequency of verbal abuse and threats similarly had an association with emotional exhaustion that was to some extent independent of the Demand-Support-Control model. The effect of discrimination was reduced when in a model with bullying and was not significant in the final model: the close association between bullying and discrimination needs to be borne in mind. Also, job discrimination was strongly associated with job control, support from ward manager and support from colleagues, with a mean differences of -0.35 (-0,42 to -0.27) in job control measured on a five point scale, -0.48 (-0.62 to -0.34) in ward manager support and – 0.47 (-0.57 to -0.36) between those who reported discrimination and those who did not. Thus a potential causal pathway is that discrimination may increase job strain via a reduction in perceived autonomy and support.

Cynicism was similarly associated with bullying, discrimination and threats of violence in the initial stages in the analysis, but once the Demand-Control-Support variables were added, frequency of threats was the only adverse events variable to remain significant. Personal accomplishment


116

showed no association with any of the adverse events: this has therefore not been shown in Table 25.

Ward level-variables were largely non-significant, even when conceptually apparently closely related to significant individual-level variables. A possible explanation might be in terms of lower power given the smaller numbers of units. However, the effect sizes were generally much smaller than for the significant individual-level variables, suggesting that this was unlikely to be the case.

Table 26 shows the results from a similar modelling process involving the depression-enthusiasm and anxiety-contentment job-related well-being scores. A fairly similar pattern emerges for each of these scores to the burnout variables. In unadjusted models and models adjusted for demographic and occupational characteristics, individual experiences of bullying and discrimination and frequency of threats and verbal abuse were all significant. Discrimination was no longer significant in the final model including all the adverse events and the demand-support-control models: again this is likely to be explained in terms of a close relationship with bullying and with control and support variables. The effect sizes for bullying and frequency of threats diminished markedly with addition of the Demand-Support-Control variables, but they clearly remained independently significant.

Table 27 shows the same modelling process for intrinsic job satisfaction, with a similar model again obtained. Prior to addition of the Demand-Support-Control variables, all three individual adverse events variables are significant with substantial effect sizes. In the final model with these variables added, bullying is once again still independently significant, but this time discrimination but not threats remains significant.


117

Table 25. Associations between adverse events and burnout variables

Outcome variable Stage 1 Adverse events variables in

simple model*, entered individually

and unadjusted

(coefficient, (95% CI) p=

Stage 2 Adverse events entered together

in model adjusted for demographic and

occupational variables***

Stage 3 Significant variables in final

model that includes adverse events and

demand, support control variables


Negative value of coefficient

indicates association with

lower burnout

N=1797 final model

Individual reports:

Bullied in last year: 8.0 (6.8 to 9.2)P<0.0005

Discriminated against: 5.1 (4.0 to 6.3)

P<0.0005

Frequency of threats of violence: 1.9

(1.6 to 2,2) P<0.0005

Individual reports:

Bullied in last year: 6.0 (4.7 to 2.3) P<0.0005

Discriminated against: 2.2 (0.9 to 3.4) P=0.001

Frequency of threats of violence: 1.8 (1.5 to

2.1) P<0.0005

Bullied in past year: (3.6 (2.6 to 4.6)

p<0.0005

Frequency of threats of violence: (1.2 (1.0 to 1.5) p<0.0005

Cynicism

Negative value of coefficient

indicates association with

lower burnout

N=1720 final model

Individual reports:

Bullied in last year: 1.7 (1.1 to 2.3)

P<0.0005

Discriminated against: 1.5 (1.0 to 2.1)

P<0.0005

Frequency of threats of violence: 0.8

(0.6 to 0.9) P<0.0005

Team manager reports:

Any staff member sick for more than a

week after assault: 0.7 (0.0 to 1.3)

P=0.05

Individual reports:


Discriminated against: 0.8 (0.2 to 1.4) P=0.012

Frequency of threats of violence: 0.7 (0.6 to

0.9) P<0.0005

Frequency of threats of violence: (0.6 (0.4 to

0.7) p<0.0005)

**Entered in each model alongside the individual adverse events variables are the demographic and occupational variable that were found to be significant in models for the outcome measure in question as presented in Table 7.


118

Table 26. Associations between adverse events and job-related well being variables

Outcome variable Stage 1 Adverse events variables

in simple model, entered

individually and unadjusted


Stage 2 Adverse events entered

together in model adjusted for

demographic and job-related variables



demand, support and control variables

Anxiety-contentment scale

Positive coefficient means

characteristic is associated with

higher contentment, lower anxiety

N=1693 for final model

Individual reports:

Bullied in last year: -0.42 (-0.49 to -

0.35) P<0.0005

Discriminated against: -0.31 (-0.38

to -0.24) P<0.0005

Frequency of threats of violence: -

0.08 (-0.11 to - to -0.07) P<0.0005


Drug dealing on ward -0.13 (-0.23 to

-0.03) P=0.012

Individual reports:

Bullied in last year:-0.30 (-0.38 to -0.22)

P<0.0005

Discriminated against: -0.20 (-0.27 to -

0.12) P<0.0005

Frequency of threats of violence: -0.08 (-

0.10 to -0.06) P<0.0005

Bullied in past year: -0.13 (-0.20 to -0.06)

p<0.0005


0.07 to -0.03) p<0.0005

Depression-enthusiasm scale



higher enthusiasm lower depression

Individual reports:

Bullied in last year: -0.51 (-0.59 to -

0.44) P<0.0005

Discriminated against: -0.37 (-0.44

to -0.29) P<0.0005

Frequency of threats of violence: -

0.09 (-0.10 to - to -0.07) P<0.0005

Individual reports:

Bullied in last year: - 0.37 (-0.45 to -0.28)

P<0.0005

Discriminated against -0.21 (-0.30 to -

0.13) P<0.0005


0.08 to -0.04) P<0.0005


p<0.0005


0.04 to -0.00) p<0.000


119

Table 27. Associations between adverse events and intrinsic satisfaction

Outcome variable Stage 1 Adverse events variables in

simple model, entered individually and

unadjusted (coefficient, (95% CI) p=)

Stage 2 Adverse events entered

together in model adjusted for

demographic and job-related

variable







higher job satisfaction

N=

Individual reports:

Bullied in last year: -0.46 (-0.54 to -0.38)

P<0.0005


0.31) P<0.0005


0.10 to - to -0.06) P<0.0005


Number of violent incidents against staff: -

0.01 (-0.02 to -0.00) P=0.01

Individual reports:

Bullied in last year: - 0.33 (-0.43 to -

0.23): P<0.0005


0.21) P<0.0005


0.07 to -0.02) P=0.001


p=0.001

Discriminated against: -0.12 (-0.19 to -0.04)

p=0.002


120

4.7 The relationship of social deprivation of area and patient population to morale

4.7.1 Summary Points

There was some evidence that higher levels of Employment Deprivation predicted lower morale in terms of exhaustion, cynicism and depression among staff. These associations were significant after adjusting for other predictors of morale from our study.

An overall summary measure of deprivation, did not predict morale outcomes.

Case-mix variables were not consistently associated with morale in our final multi-level models. However, in more simple models, the morale outcomes of exhaustion, cynicism, anxiety and depression were all individually associated with higher annual admission rates on wards.

Teams with higher proportions of detained and BME patients were more exhausted, while teams with more psychotic, male , substance misuse and detained patients had more cynicism.

These case-mix variables lost significance in our fully adjusted models; which may reflect co-linearity of variables in the final model, and the fact that relatively small amounts of overall variance in the models were explained by ward-level characteristics

4.7.2 Hypotheses

We hypothesised that mental health work in more socially deprived areas of the country would be more stressful for staff and therefore participants from such units would show lower levels of morale. We also hypothesised that staff working on wards/teams with greater proportions of patients who were male, psychotic, detained under the Mental Health Act, or presenting with significant substance misuse problems would show lower levels of morale on our main morale outcome measures. Finally we hypothesised that staff working on wards with greater numbers of admissions would report lower levels of morale.

4.7.3 Results

Tables 28 and 29 describe the IMD scores and patient casemix for the workplace of each respondent, according to the type of ward/team that they worked within. Units were more likely to be located within the most deprived half of the country and the catchment areas showed a similar distribution. The case-mix and number of admissions were consistent with clinical expectations. For instance rehabilitation wards had the lowest numbers of annual admissions, PICU units had the highest numbers of detained patients and patients who were psychotic.


121

Table 28. Index of multiple deprivation of respondents’ team base (postcode) & catchment area: quartiles, by team type

Acute

N=721

PICU

N=148

CAMHS

N=189

Forensic

N=219

Rehab

N=137

Older adult

N=157

Community Mental Health Team N=258

Crisis Resolution Team N=216

All

IMD ward postcode*

Numbers of wards in each quartile (highest deprivation at top)

406 (56.3)

205 (28.4)

57 (7.9)

53 (7.4)

127 (85.8)

21 (14.2)

0 (0)

0 (0)

144 (76.2)

14 (7.4)

0 (0)

0 (0)

123 (56.3)

0 (0)

96 (43.8)

0 (0)

66 (48.2)

71 (51.8)

0 (0)

0 (0)

75 (47.8)

29 (18.5)

39 (24.8)

14 (8.9)

64 (24.1)

130 (50.4)

36 (14.0)

28 (10.9)

85 (39.4)

68 (31.5)

14 (6.8)

49 (22.7)

1090 (53.3)

538 (26.3)

263 (12.9)

154 (7.5)

IMD catchment area


332 (40.1)

213 (29.5)

144 (20.0)

32 (4.4)

81 (63.8)

46 (36.2)

0 (0)

0 (0)

38 (20.1)

79 (41.8)

72 (38.1)

0 (0)

48 (21.9)

69 (31.5)

102 (46.6)

0 (0)

35 (29.4)

31 (26.1)

41 (34.5)

11 (10.1)

35 (22.3)

35 (35.0)

33 (21.0)

34 (21.7)

64 (24.8)

121 (46.9)

45 (17.4)

28 (10.8)

88 (40.7)

59 (27.3)

59 (27.3)

10 (4.6)

721 (35.9)

673 (33.5)

496 (24.7)

116 (5.8)

Employment Deprivation: ward postcode


429 (59.5)

165 (22.9)

117 (65.2)

10 (1.4)

110 (74.3)

38 (25.7)

0 (0)

0 (0)

147 (77.8)

11 (5.8)

31 (16.4)

0 (0)

112 (51.1)

26 (11.9)

81 (37.0)

0 (0)

66 (48.2)

48 (35.0)

10 (7.3)

13 (9.5)

55 (35.0)

53 (33.8)

49 (31.2)

0 (0)

78 (30.2)

92 (35.7)

70 (27.1)

18 (7.0)

93 (43.1)

32 (14.8)

63 (29.2)

28 (13.0)

1090 (53.3)

465 (22.7)

421 (20.6)

69 (3.4)

Employment Deprivation: catchment area


375 (52.0)

172 (23.9)

164 (22.7)

10 (1.4)

90 (60.8)

37 (25.0)

21 (14.2)

0 (0)

38 (29.2)

41 (31.3)

52 (39.7)

0 (0)

48 (23.5)

34 (16.7)

112 (59.8)

0 (0)

35 (25.6)

0 (0)

102 (74.5)

0 (0)

35 (22.3)

73 (46.5)

49 (31.2)

0 (0)

78 (30.2)

108 (41.9)

63 (24.4)

9 (3.5)

96 (44.4)

44 (20.2)

76 (35.2)

0 (0)

795 (40.3)

509 (25.8)

649 (32.9)

19 (1.0)

* Highest IMD quartile = 1-88, next = 88-177, next = 178-258, lowest = 259-354


122

Table 29. Case-mix, and number of admissions and mean index of multiple deprivation score for respondents teams, by team type

Acute

N=721

PICUs

N=148

CAMHS

N=189

Forensic

N=219

Rehab

N=137

Older adult

N=157

Community Mental Health Team N=258


All

Number of admissions in year

Mean (s.d)

196.6

(87.6)

99

(39.8)

30.9

(51.1)

24.3

(34.5)

10.8

(9.2)

66.2

(28.9)

NA

NA

97.7

(91.9)

Patients male

Mean % (s.d)

57.5

(23.7)

93.5

(13.1)

24

(22.1)

87.2

(30.8)

63.4

(11.3)

34.0

(18.0)

NA 42.9

(11.1)

56.4

(29.2)

BME: Patients from ethnic background other than white British Mean % (s.d)

29

(26.3)

61.1

(31.8)

32.2

(29.1)

40.3

(29.6)

26.5

(25.1)

4.0

(6.1)

NA

22.2

(23.4)

30.1

(28.9)

Patients Psychotic

Mean % (s.d)

68.6

(20.4)

90.9

(13.3)

25.9

(10.7)

70.9

(32.9)

75.3

(25.6)

16.2

(10.1)

NA 43.8

(18.7)

58.7

(29.7)

Patients detained

Mean % (s.d)

56.2

(14.4)

100

0)

12.1

(11.7)

93.4

(15.5)

39.9

(17.8)

17.6 (

9.2)

NA 3.3

(5.2)

48.3

(33.2)

Patients with significant alcohol/drug problems

Mean % (s.d)

15.0

(16.3)

47.0

(30.2)

10.5

(14.7)

46.0

(23.9)

25.0

(17.4)

3.6

(3.9)

NA

13.9

(0.9)

20.4

(22.2)

IMD postcode area:

Mean % (s.d)

104.7

(81.8)

68.2

(41.9)

75.2

(90.8)

138.0

(93.8)

90.3

(58.2)

129.0

(92.1)

132

(81.6)

142

(103.6)

111

(87.1)

IMD catchment area:

Mean % (s.d)

112.2

(80.0)

99.2

(81.1)

158.4

(65.5)

155.2

(55.1)

181.6

(89.1)

152.6

(92.4)

135.3

(81.8)

121.6

(80.4)`

131.8

(81.8)


123

Table 30 explores which IMD and case-mix variables were associated with individuals’ morale on each of the three Maslach Burnout Inventory outcomes. Table 31 relates to the two Warr measures of morale and Table 32 describes any associations with intrinsic job satisfaction. In Tables 30–32, the IMD and case-mix variables which were significantly associated with an outcome (p=0.05) in a single model are listed. Then any of these variables which remained significant in a final multi-variate model are tabled, as well as the other covariates which were significant in the final model.

4.7.4 Burnout outcomes

For exhaustion, a number of variables were significantly associated in a single variable multi-level model (Table 30), including increased employment deprivation of the catchment area, increased number of admissions, greater percentages of patients from BME ethnic backgrounds and higher percentages of patients detained under the Mental Health Act. The overall IMD of the ward/team postcode also significantly predicted exhaustion/burnout, (this was true for overall IMD as a continuous variable, but not when included as quartiles) In multivariate models (including the variables associated with exhaustion in Section 4.1), only the percentage of patients from BME backgrounds and the employment IMD of the catchment area remained significantly associated with exhaustion. In step 3, when both ethnic mix and employment deprivation were added together, only employment IMD remained significantly associated with exhaustion.

For MBI cynicism, in single variable multi-level models, the employment deprivation of the postcode was associated with higher burnout, as well as greater percentages of male, psychotic, substance misusing and detained patients. When other predictors of cynicism from Section 4.1 were included, only the employment deprivation of the ward location remained significant, and this was true whether it was entered as a categorical variable or when examining a trend across increasing quartiles of deprivation, coefficient -0.44(-.77;-.11) p=0.01.

For MBI personal accomplishment, only the percentage of patients who were male predicted higher burnout, and this remained a significant association after adjustment in the final model


124

Table 30. Multilevel models for Maslach Burnout Inventory variables – deprivation and case-mix variables significantly

associated with outcome in multi-level model

Outcome variable Ward variables in simple

model


Ward variables in adjusted

model (single ward variable

adjusted for significant

variables from chapter 3)

Other variables significantly associated

with outcome in final model (p<0.05)

Proportion of

variance at

ward level

Exhaustion

Negative value of


association with lower

burnout

N=1444 in final model

Employment deprivation of

catchment area (reference

category: highest

deprivation) Cat 2:-1.1 (-2.8;-

.72) p=.25 Cat 3:-2.3(-4.0;-

0.6)p=0.01 Cat 4:-0.33 (-

6.9;0.2)p=.92

IMD ward postcode (continuous

variable) -0.1(-0.02;0.0)p=.03

Admissions (mean number in yr)

0.01 (0.00-0.02) p=0.01

BME: mean% 0.03

(.2;5.3)p=.04

Detained: mean %

(0.0;0.4)p=.05


catchment area (reference

category: highest

deprivation) Cat 2:-2.6 (-4.7;-

.52) p=.01 Cat 3:-1.8(-3.9;-

0.6)p=0.10 Cat 4:-1.8 (-

9.5;5.9)p=.65

Ward type (Rehabilitation and crisis teams

significantly lower exhaustion )

Ethnic category: Asians less exhausted

Longer time on ward and in mental health

services

Longer hours worked

Occupational groups: health assistants less

exhausted

2.03/126

Cynicism

Negative value of



ward postcode (reference

category: highest

deprivation) Cat 2:0.29 (-.45;-



category: highest

deprivation) Cat 2:0.11 (-.62;-

Ward type

CAMHS, Rehabilitation and older people

wards less cynicism

0.49/27


125


burnout

N=1849 final model

1.04) p=.44 Cat 3:-.8(-1.6;-

.01)p=.05 Cat 4: -.43 (-

2.0;1.2)p=.60


0.01 (0.00-0.01) p=0.001

Male: mean% 0.02 (.01;.03)

p=.009

Detained: mean %

.02(.01;0.3)p<.001

Psychotic: mean % 0.02

(.01;.03)p=.001

Alcohol/drugs:mean% .02(.00-

.03) p=.05

1.0483) p=.29 Cat 3:-.1.1(-

1.9;-.36)p=.004 Cat 4: -.76 (-

2.3;.77)p=.33

Ethnic group: Black and Asian groups less

cynicism

Male sex

Older age groups

Accomplishment

Positive value of



burnout

N=1533 final model

Male: Mean% -0.02(-.04;-.06)

p=.008

Male: mean % -0.03(-.05;-.01)

p=.015 Ward type

PICU and Crisis teams Greater

accomplishment

Black and Asian groups higher scores

Longer time in mental health services,

lower scores

Occ group 7 higher scores

1.23/27


126

4.7.5 Job-related well-being and intrinsic satisfaction

For anxiety/contentment (Table 31), only higher numbers of admissions predicted worse contentment/greater anxiety, but this associated was not significant in a fully adjusted model.

For the depression/enthusiasm scale (Table 31), greater employment deprivation of the team postcode, higher percentages of detained patients and greater number of admissions predicted higher depression/lower enthusiasm when they were included as single predictors of the outcome. However, only employment deprivation (of ward postcode) predicted depression/enthusiasm in a final model adjusted for the other significant variables from Section 4.1

Although teams whose patients were from more diverse ethnic backgrounds were associated with greater intrinsic satisfaction in a single variable model, this was not a significant association after adjustment (Table 32).


127

Table 31. Multilevel models for measures of work-related well-being– ward deprivation and case-mix variables significantly associated with outcome in multi-level model


model


Ward variables in final model Other variables included in final model Proportion of

variance at

ward level

Anxiety-contentment

scale


means characteristic is

associated with higher

contentment, lower

anxiety


-0.01 (-0.01;-0.0)p=.005

None Bank staff

Ward type

Ethnic group


Occupational group

NA



means characteristic is

associated with higher

enthusiasm, lower

depression

N=1544 final model



category: highest

deprivation) Cat 2:-.01 (-.12;-

.10) p=.83 Cat

3:0.13(.01;.26)p=0.04 Cat 4:

0.18 (-.01;.42)p=.14


-0.01 (-0.01-0.0) p=0.01

Number detained: mean% 0.2 (-

.35;-.05) p=.009



category: highest

deprivation) Cat 2:.12 (-.01;-

.23) p=.040 Cat

3:0.18(.05;.30)p=0.005 Cat

4: 0.26 (-.03;.49)p=.029

If senior, If bank, Older age, Shorter time on

ward, higher score

Ward type: CAMHS, Older people,

Rehabilitation, crisis teams all greater scores.

CMHT scores lower

Ethnic groups: Asians higher

Longer in mental health services and marital

status – divorced, widowed, separated: lower

scores

0.02/0.48


128

Table 32. Multilevel models for Intrinsic Satisfaction– ward deprivation and case-mix variables significantly associated with outcome in multi-level model


model


Ward variables in final model Other variables included in final model Proportion of

variance at ward

level

Intrinsic satisfaction BME

mean% 0.22 (.03;.41) p=.03

None Senior , rehabilitation and crisis team,

black and Asian groups, and occupational

therapist higher scores

NA


129

4.8 Organisational context and morale

4.8.1 Summary of results

On average, staff ratings of role clarity and conflict (how clearly defined their jobs were) and team cohesion and conflict were moderately positive and similar for all service types.

Staff ratings for fairness and voice were less positive and least favourable for CMHTs. Staff typically reported little opportunity to express their views to or be listened to by senior management.

Supervision, appraisals and training were all positively received by staff and most frequently provided in CMHTs.

49% of wards reported using a specific therapeutic model, most commonly the Tidal Model, Solution-Focused Therapy or the Recovery Model. 40% were using Protected Engagement Time.

Multi-level regression identified the following variables as independently associated with indicators of morale:

Emotional exhaustion: Role conflict, team communication, substantive fairness

Cynicism: Role conflict, team communication

Personal accomplishment: Role clarity

Anxiety – contentment: Role conflict, team communication, procedural and substantive fairness

Depression – enthusiasm: Role clarity, role conflict, team communication, procedural fairness, having a personal development plan

Intrinsic job satisfaction: Role clarity, role conflict, team communication, substantive fairness and procedural fairness


130

4.8.2 Internal consistency of scales used to rate organisational context

Table 33 shows Cronbach’s alpha, a measure of internal consistency, for the main scales used by individual staff members to rate organisational context. These results indicate that all these scales achieved a very acceptable level of internal consistency, with items closely related to one another.

Table 33. Cronbach’s alpha for main scores used to rate organisational context

Construct n Items Q. No Alpha*

Role Clarity 2210 5 1a-e** 0.87

(0.85, 0.88)

Role Conflict 2221 4 2a-d 0.87

(0.86, 0.88)

Team conflict 2177 4 4a-d 0.89

(0.88, 0.89)

Team communication 2018 5 6a-e 0.88

(0.87, 0.89)

Substantive Justice 2204 4 11a-d 0.80

(0.79, 0.81)

Procedural Justice

2170 6 11e-j 0.91

(0.91, 0.92)

Senior management

support for ward or team manager 125 7

Manager questionnaire

Item 18

0.93

(0.91, 0.95)

*Cronbach’s alpha with 95% confidence intervals based on 1000 bootstrap samples, except for fairness which is not based on bootstrap method.

** Numbers of questionnaire items refer to the team working and role clarity section of the questionnaire

4.8.3 Individual staff ratings of organisational context

Table 34 shows descriptive data for staff ratings of the organisational context within which they work, shown by service type and then for the sample as a whole.


131

Role clarity and role conflict are the measures reflecting how clearly defined participants’ immediate jobs are within their team and organisation. Responses to these and all other items in the table are on a 1 to 5 scale, with 3 representing a neutral neither agree nor disagree position. Responses regarding role tended to be somewhat positive. The mean for role clarity falls slightly closer to “agree” (point 4 on the 5 point scale) than to “neither agree nor disagree” (the midpoint) in response to a series of items about roles and responsibilities being clearly defined. For role conflict overall mean is close to the halfway point between agreeing “a little” and “a moderate amount” to a series of items about receiving conflicting demands. For role clarity, responses for all service types were within a narrow range of half a standard deviation, with CMHTs scoring lowest by a fairly small margin. For role conflict, most service types again had similar scores, but with rehabilitation wards recording particularly low levels of conflict.

With regard to the two variables assessing team cohesion and conflict, responses again tended towards the positive, though remaining fairly close to the neutral point. Respondents from rehabilitation wards reported the lowest levels of team conflict and those from forensic wards, with all service types within half a standard deviation of one another.

Ratings for fairness and voice were less positive. For substantive fairness, a measure of how fairly treated participants felt in terms of their working terms and conditions, the mean response was very close to the neutral ‘neither fair nor unfair’ point. CRT and rehabilitation ward staff were most likely to feel that these aspects of their jobs were fair; CMHT and general acute ward staff were least likely to think so. Procedural fairness attracted poorer ratings than substantive fairness, with respondents in all types of service tending to rate decision making processes affecting them as unfair rather than fair. Procedural fairness was perceived as especially poor by CMHT staff.

A picture of mistrust of senior management decision making processes emerged even more strongly from the two items assessing employee voice. The mean for the extent to which employees were able to put their views forward to Senior Management regarding decisions that are important to them fell closer to ‘just a little’ than to ‘a moderate amount’. Ratings of the extent to which Senior Management are influenced by these views were particularly negative, with a mean below ‘just a little’. Overall, 64% of respondents selected ‘not at all’ or ‘just a little’ in response to the item on how much they could put their views forward to Senior Management, and 77.2% selected “not at all” or “just a little” in response to the item regarding how much senior management listen to them. Responses from CMHTs were again particularly negative regarding this aspect of employee voice.


132

Table 34. Staff ratings of organisational context

General acute wards N =721

CAMHS wards

N=189

Forensic Wards

N=219


Rehab wards

N=137

PICUs

N=148

CMHTs

N=258


All

N= 2045

Role Clarity (N=2017) 3.63 (0.74)

3.66 (0.71)

3.65 (0.73)

3.70 (0.67) 3.66 (0.68)

3.78 (0.66)

3.49 (0.75) 3.75 (0.68) 3.65 (0.72)

Role Conflict (N=2014) 2.59 (1.01)

2.38 (0.85)

2.48 (1.09)

2.38 (1.07) 2.12 (0.85)

2.41 (0.93)

2.48 (0.92) 2.53 (0.98) 2.48 (0.99)

Team Conflict (N=1977) 2.53 (0.85)

2.57 (0.69)

2.65 (0.85)

2.36 (0.85) 2.23 (0.70)

2.35 (0.76)

2.47 (0.82) 2.57 (0.79) 2.50 (0.82)

Effectiveness of Ward Team communication structures (N=2008)

3.17 (0.85)

3.28 (0.81)

3.30 (0.83)

3.36 (0.79) 3.44 (0.77)

3.48 (0.73)

3.20 (0.75) 3.35 (0.82) 3.27 (0.82)

Substantive Fairness (N=2010) 2.88 (0.83)

3.11 (0.77)

2.92 (0.74)

3.02 (0.82) 3.25 (0.69)

2.89 (0.67)

2.95 (0.82) 3.25 (0.75) 2.99 (0.80)

Procedural Fairness (N=1992) 2.49 (0.85)

2.60 (0.80)

2.63 (0.79)

2.70 (0.75) 2.72 (0.75)

2.53 (0.78)

2.21 (0.79) 2.42 (0.77) 2.51 (0.81)

Do you have a chance to put views to Senior Management when changes affect your jobs? (N=1975)

2.11 (0.94)

2.35 (1.05)

2.41 (1.01)

2.39 (1.05) 2.43 (1.09)

2.29 (0.99)

2.13 (0.95) 2.18 (0.93) 2.23 (0.99)

To what extent do your views then influence Senior Management's decisions? (N=1944)

1.72 (0.88)

1.90 (0.98)

2.06 (0.96)

1.96 (1.02) 1.92 (0.99)

1.85 (0.91)

1.59 (0.81) 1.71 (0.84) 1.80 (0.91)


133

4.8.4 Individual staff reports regarding human resource practices

Table 35 shows staff reports regarding human resource practices such as appraisal, supervision and training. These are again shown by service type.

Regarding supervision, staff reported between 0 and 90 supervision sessions in the previous 6 months, the mean number being 3.4, suggesting supervision is received on average just slightly more than once every two months. Of the 1934 respondents giving valid answers to this item, 470 (24.3%) reported that they had had no supervision sessions at all in the previous month. Substantially more supervision seemed to be taking place in CMHTs and CAMHS wards than elsewhere, with both of them having means over 5, suggesting that supervision was taking place almost monthly. For those who had received any supervision, satisfaction with this tended to be good: the mean rating was 3.5 on a 1 to 5 scale (standard deviation 1.2), with 1069 of 1852 respondents (58%) satisfied or very satisfied with their supervision.

Just under two thirds (63%) had had an appraisal in the preceding year, and almost as many reported having a current personal development plan. CMHTs papered to be performing best and CAMHS and PICU services least well in this area. Satisfaction again was better than neutral, though not very much so: mean satisfaction was 3.3 for both appraisal and personal development plan (standard deviation also 1.2 for each).

Respondents has attended an average of 4 days of statutory and 4 of non-statutory training days each, with CMHT staff receiving the most non-statutory training and PICU and older adult ward staff the least. Mean rating for the usefulness of non-statutory training was 3.6 (standard deviation 0.9).

4.8.5 Team level reports regarding organisational context

Table 36 shows reports from team managers regarding organisational context and interventions whose purpose may be to improve morale, such as staff support groups and team away days.

The first item on support from senior ward management is based on Haynes’s (1999) leader support items, adapted to ask ward or team managers how much support they receive from more senior managers in the Trust. Responses for all service types were close to the mid-point on the 1 to 5 scale, indicating support ‘to a moderate extent’. Managers of rehabilitation wards reported the most support, those of PICUs and of CAMHS wards the least.

Items on some forms of staff support discriminated little between wards as they were reported as present on most: these included ward meetings, social events and the presence in the Trust of an acute care forum dedicated to development of the local acute care services.


134

Table 35. Frequency of training, supervision and personal appraisals by ward type

General acute wards

N =721

CAMHS wards

N=189

Forensic Wards

N=219

Older adult wards

N=157

Rehab wards

N=137

PICUs

N=148

Community Mental Health Teams

N=258

Crisis Resolution Teams

N=216

All

N= 2045

How many days of statutory training have you had in the past year? (N=1903)

Mean number of days (s.d)

4.27 (3.64) 3.45 (3.46) 5.75 (5.03) 3.91 (2.97) 3.72 (2.85)

4.84 (4.07)

3.21 (3.34) 2.95 (3.40)

4.06 (3.76)

How many days of non-statutory training have you had in the past year? (N=1794)


3.58 (6.49) 4.74 (8.16) 3.74 (6.10) 3.20 (5.72) 4.29 (7.77)

3.16 (6.63)

5.09 (7.46) 4.57 (8.20)

4.00 (7.02)

How many times have you received supervision in the last 6 months? (N=1934)


2.87 (4.52) 5.67 (8.93)

3.02 (3.29) 2.28 (3.77) 3.76 (5.80)

2.57 (3.88)

5.09 (4.51) 2.99

(3.28) 3.43 (4.99)

Have you had a Personal Development discussion or review in the last year? (N=2000)

n/N (%)

430/704 (61.1%)

103/183 (56.3%)

136/217 (62.7%)

96/154 (62.3%)

91/132 (68.9%)

83/146 (56.8%)

168/255 (65.9%)

130/209 (62.2%)

1237/2000 (61.9%)

Have you had an appraisal in the last 12 months? (N=1999)

n/N (%)

438/700 (62.6%)

109/184 (59.2%)

135/217 (62.2%)

98/154 (63.6%)

92/132 (69.7%)

82/146 (56.2%)

186/257 (72.4%)

122/209 (58.4%)

1262/1999 (63.1%)


135

Regarding supervision and appraisal, all wards and teams reported that their policy was to have these in place for all staff, but 45% acknowledged this goal was not achieve in the case of appraisal and 41% in the case of supervision. CMHTs reported the best and general acute wards the worst coverage for supervision, and CRTs the best and PICUs the worst for appraisal. There were large variations in whether a staff away day had been held and whether there was a regular staff support group, with overall 41% having had an away day and 57% holding a staff support group.

Table 37 shows that Modern Matrons/lead nurses and, to a lesser extent, lead consultant psychiatrists, were in place in the majority of wards. Practice Development Practitioners working regularly with the team were available for a minority of services.

For inpatient wards, we enquired whether any specific model or philosophy of care was in place and 47 wards (49% reported such approaches). A range of models were reported, the most frequent being the Tidal Model (N=9), Solution Focused Therapy (N=8) and the Recovery Model (N=6). Models also represented included Star Wards, Hildegard Peplau’s nursing model, person centred care and therapeutic mealtimes. Forty wards reported the adoption of Protected Engagement Time, with time during the ward day ring-fenced for staff-patient contact and protected from any other demands such as visits, phone calls or administrative work. Of these forty, 9 wards had been 1 and 5 hours of protected time each week, 19 had between 50 and 10 and the remaining 12 more than 10 hours, with just one ward reporting more than 20 hours a week of protected time.

4.8.6 Context and morale

Following our initial descriptive analyses, multilevel regressions were again used to explore the associations between the indicators of organisational context discussed above and the 6 main morale indicators explored in the study – the three components of the Maslach Burnout Inventory, Warr’s two two-pole job-related well-being scales and intrinsic satisfaction.

Table 38 shows effects of organisational context variables when their relationships with the three components of burnout were assessed in three stages. For simplicity, these tables show only the significant organisational context variables, as do 4.8.7 and 4.8.8. Fuller versions of the tables with other types of variables also shown are to be found in Appendix 1, Tables A4 to A6.

The first stage for each measure was to enter each organisational variable individually as an independent variable into a multilevel regression with the relevant burnout indicator as the dependent variable and individuals nested within wards. The first column of results shows which organisational variables were significant. The second column reports stage 2 of the modelling process: all the significant outcome variables from Stage 1 were entered into a multilevel regression together with the relevant demographic and occupational control variables found in Section 4.1 to be significantly associated with the outcome variable. This column shows the organisational


136

Table 36. Support and supervision by ward type – service manager reports

General acute wards

N=48

CAMHS wards

N=10

Forensic Wards

N=12

Older adult wards

N=9

Rehab wards

N=9

PICUs

N=10

Community Mental Health Teams

N=17

Crisis Resolution Team

N=16

All

N=131

All-round support from Trust Senior Management (N=130)

Mean rating on scale 1-5, (s.d)

3.09 (1.09)

2.66 (1.08)

2.86 (0.87)

2.94 (0.77)

3.29 (0.83)

2.75 (0.97)

2.86 (0.90) 3.00 (0.87) 2.97 (0.96)

Have you had a staff away day in the past year? (N=128)

14/47 (29.8%)

6/10 (60%)

4/11 (36.4%)

1/10 (10%)

2/9 (22.2%)

3/10 (30%)

10/15 (66.7%)

12/16 (75%)

52/128 (40.6%)

Do you have a regular staff support group on the ward/team? (N=129)

23/48 (47.9%)

10/10 (100%)

7/11 (63.6%)

4/10 (40%)

5/9 (55.6%)

10/10 (100%)

7/15 (46.7%) 8/16 (50%) 74/129 (57.4%)

Have you had a social event for all staff in the past year? (N=129)

44/48 (91.7%)

9/10 (90%)

8/11 (72.7%)

8/10 (80%)

9/9/ (100%)

8/10 (80%)

14/15 (93.3%)

16/16 (100%

116/129 (89.9%)

Are appraisals carried out regularly for all clinical staff on the ward/team? (N=128)

22/47 (46.8%)

5/10 (50%)

5/11 (45.5%)

7/10 (70%)

5/9 (55.6%)

4/10 (40%)

10/15 (66.7%)

12/16 (75%)

70/128 (54.7%)

Do all ward staff have a named supervisor and receive supervision regularly? (N=127)

21/46 (45.7%)

5/10 (50%)

9/11 (81.8%)

7/10 (70%)

2/9 (22.2%)

6/10 (60%)

13/15 (86.7%)

12/16 (75%)

75/127 (59.1%)


137

Any training sessions relevant to clinical practice held for the ward/team in last year? (N=128)

40/47 (85.1%)

10/10 (100%)

7/11 (63.6%)

9/10 (90%)

6/9 (66.7%)

9/10 (90%)

13/15 (86.7%)

14/16 (87.5%)

108/128 (84.4%)

Is there an Acute Care Forum that regularly holds meetings within your trust? (N=98)

47/48 (97.9%)

0/7 (100%)

7/9 (77.8%)

3/7 (42.9%)

4/5 (80%)

9/9 (100%) n/a

12/13 (92.3%)

82/98 (83.7%)

Does your ward have meeting in which issues relating to the ward are discussed? (N=97)

45/48 (93.8%)

10/10 (100%)

10/11 (90.9%)

3/9 (33.3%)

9/9 (100%)

9/10 (90%)

n/a n/a 86/97 (88.7%)


138

Table 37. Leadership on models of care – manager reports

General acute wards

N=48

CAMHS

wards

N=10

Forensic

Wards

N=12

Older adult

wards

N=9

Rehab

wards

N=9

PICUs

N=10

Community

Mental

Health

Team

N=17

Crisis

Resolution

Team

N=16

All

N=131

Is there a Modern Matron/Lead Nurse who works with your ward? (N=98)

36/48 (75%)

9/10 (90%)

10/12 (83.3%)

9/9 (100%)

6/9 (66.7%)

9/10 (90%)

n/a n/a 79/98 (80.6%)

Does your ward/team have a designated lead consultant psychiatrist? (N=130)

29/48 (60.4%)

10/10 (100%)

6/12 (50%)

7/10 (70%)

9/9 (100%)

10/10 (100%)

10/15 (66.7%)

10/16 (62.5%)

91/130 (70%)

Do any Practice Development Practitioners regularly work with your ward/team? (N=130)

18/48 (37.5%)

3/10 (30%)

6/12 (50%)

3/10 (30%)

2/9 (22.2%)

6/10 (60%)

7/15 (46.7%)

7/16 (43.8%)

52/130 (40%)

On you ward, have any nursing models or therapeutic approaches been adopted? (N=96)

24/48 (50%)

5/10 (50%)

3/10 (30%)

4/9 (44.4%)

7/9 (77.8%)

4/10 (40%)

n/a n/a 47/96 (49%)

Has your ward adopted the use of protected therapeutic time?(N=94)

27/48 (56.3%)

2/7 (28.6%)

1/11 (9.1%)

4/9 (44.4%)

2/9 (22.2%)

5/10 (50%)

n/a n/a 41/94 (43.6%)


139

variables found to be significant in this model. Finally, Stage 3 involved adding the Demand-Support-Control variables to the model obtained at Stage 2, investigating which variables were significant independently of Karasek’s triad.

For emotional exhaustion, most individual level organisational variables emerged as significant when tested individually apart from days of training and number of supervision sessions received. Most ward level variables were non-significant, except for an association between lower emotional exhaustion and all staff having an appraisal. There was also an unexpected association between the adoption of protected therapeutic time and greater emotional exhaustion.

In the Stage 3 model for emotional exhaustion with Demand-Support-Control variables also included, three main organisational context variables emerge as independently associated with emotional exhaustion even with the Karasek triad in the model. These are role conflict, team communication and substantive fairness. In this adjusted model, a one point increase in role conflict on 1 to 5 point scale is associated with a score 1.9 higher on emotional exhaustion (scored from 0 to 54), a one point increase in team communication is associate with a 1.5 decrease emotional exhaustion score, and a one point increase in substantive fairness with a 2.1 decrease in emotional exhaustion. With these variables in the model, support from managers and from colleagues are no longer significant (see Table A4, Appendix 1), raising the possibility that the effects of support on emotional exhaustion shown in Section 4.4 may be mediated by fairness, lack of role conflict and good team communication.

For cynicism, the picture was relatively similar, although this time without substantive fairness in the adjusted model. For personal accomplishment, several of the variables regarding human resource practices emerged as significant when individually tested, including at individual level appraisal, having a personal development plan, days of training and number of supervisions, and at ward level having a regular staff support group and all staff having an appraisal. Of these, only whether all staff have an appraisal remained significant at Stage 2 when the organisational variables were added together alongside demographic and occupational control variables, but it is possible that these human resource practices may have associations with personal accomplishment that are mediated by role clarity and team communication, the individual ratings of organisational context that remained significant at this stage. Once the Karasek Demand-Support-Control variables were added, only Role Clarity remained independently significant, still with a relatively large effect suggesting a 2.6 increase in personal accomplishment for every point on the role clarity scale.


140

Table 38. Associations between organisational variables and Maslach burnout inventory variables

Outcome variable

Stage 1 Testing of associations for individual variables:

Organisational variables in simple model* entered individually and unadjusted


Stage 2 Adjusted model – Significant variables for Stage 1 together with significant demographic and occupational variables from Chapter 3 models. Only significant organisational variables shown

Stage 3 – Significant variables in final model



N=1646 final model

Individual level data:

Role clarity: -5.9 (-6.6 to -5.2, p<0.0005)

Role conflict: 6.0 (5.6 to 6.5, p<0.0005)

Team conflict: 5.3 (4.6 to 5.9, p<0.0005)

Team communication: -6.2 (-6.8 to -5.7, p<0.0005)

Substantive fairness: - 6.9 (- 7.4 to -6.3, p<0.0005)

Procedural fairness: -5.7 (-6.3 to -5.1, p<0.0005)

Voice: how much are senior managers influenced?: -3.2 (-3.8 to -2.2, p<0.0005)

Satisfaction with training: -4.7 (-5.2 to -4.2, p<0.0005)

Satisfaction with supervision & appraisal: -2.7 (-3.2 to -2.2, , p<0.0005)

Has had an appraisal in past year: -1.2 (-2.3 to -0.0, p=0.05)

Has a personal development plan: -2.0 (-3.1 to -0.9)

Team manager level

Whether all staff have appraisal: -2.4 (-3.8 to -1.0, p=0.001)

Adoption of protected therapeutic time: 2.1 (0.5 to 3.6, p=0.001)


Role clarity: -1.4 (-2.2 to -0.5, p=0.002)


Team conflict: 1.2 (0.4 to 2.0, p=0.003)




Team communication: -1.5 (-2.3 to -0.8 p<0.0005)

Substantive fairness: -2.1 (-2.8 t0 -1.4 P<0.0005)

Cynicism


N=1715 final model

(Stage 1- variables examined individually)


Role clarity: -2.0 (-2.3 to -1.6, p<0.0005)




Substantive fairness: - 1.7 (2.0 to -1.4, p<0.0005)


Voice: how much are senior managers influenced? -1.0 (-1.3 to -0.8,

(Stage 2 – significant stage 1 variables in model with significant demographic and occupational variables:

Role clarity: -0.6 (-1.0 to -0.2, p=0.004)


Team conflict; 0.8 (0.3 to 1.2, p<0.0005)

(Stage 3)


Team communication: -1.5 (-2.3 to -0.8 p<0.0005)


141

p<0.0005)


Satisfaction with supervision & appraisal: -0.9 (-1.2 to -0.7 , p<0.0005)

Has a personal development plan: -0.8 (-1.3 to -0.3) p=0.003

Team communication: -0.6 (-1.0 to -0.1, p=0.01)

Personal Accomplishment

Positive value of coefficient indicates association with lower burnout

N=1664 final model



Role clarity: 3.7 (3.2 to 4.1, p<0.0005)

Role conflict: -1.2 (-1.5 to -0.8, p<0.0005)

Team conflict: -1.4 (-1.9 to -0.9, p<0.0005)

Team communication: 2.5 (2.0 to 2.9, p<0.0005)

Substantive fairness: 1.9 (1.5 to 2.4, p<0.0005)

Procedural fairness: 1.5 (1.0 to 1.9, p<0.0005)

Voice: how much are senior managers influenced?: 1.5 (1.1 to 1.8, p<0.0005)

Satisfaction with training: 1.5 (1.0 to 1.8, p<0.0005)

Satisfaction with supervision & appraisal: 1.4 (1.1 to 1.8 , p<0.0005)

Number of supervisions in past 6 months: 0.17 per meeting (0.09 to 0.24), p<0.0005

Has a personal development plan: 1.6 (0.8 to 2.4) p<0.0005

Has had an appraisal: 1.4 (0.6 to 2.1), p<0.0005

Days of training in last year 0.09 per day (0.04 to 0.13), p<0.0005

Team manager reports

Whether all staff have appraisal: 1.1 (0.2 to 2.0), p=0.02

Regular staff support group: 1.0 (0.1 to 2.0), p=0.03

(Stage 2 – significant stage 1 variables in model with significant demographic and occupational variables)

Role clarity: 2.7 (2.0 to 3.4), p<0.0005

Team communication: 1.1 (0.5 to 1.9), p<0.0005



(Stage 3)

Role clarity: 2.6 (1.0 to 3.2), p<0.0005

*As in Section 4.1, throughout multilevel regression has been used to explore associations with outcome variables, using the xtmixed command in Stata 10. The two levels are individual and ward, with individuals nested within wards.


142

Table 39 shows the same modelling process conducted for associations between the two job-related well-being variables, anxiety-contentment and depression-enthusiasm. Fuller versions with other types of variable also included are to be found in table A5, Appendix 1.

In both models, the main individual ratings of organisational context and most of the individually-reported variables relating to human resource practices are significant when tested individually. For each of these outcomes, several of the individual ratings remain significant following adjustment for the control variables and Karasek triad. Anxiety-contentment is associated in this adjusted model with role conflict (adjusted mean differences indicate 0.1 lower contentment/greater anxiety on a 1 to 5 scale for each one point increase in role conflict), team communication (0.06 greater contentment/less anxiety for each one point increase in team communication), and procedural and substantive fairness (0.05 great contentment/less anxiety for each point’s increase on each of these fairness scales). Depression-enthusiasm is independently associated with role clarity (0.05 less depression/greater enthusiasm for each point on the role clarity scale), role conflict (0.07 more depression/less enthusiasm for each point on role conflict scale), team communication (0.11 greater enthusiasm/less depression for each point on team communication scale), and procedural fairness (0.06 greater enthusiasm/less depression for each point on procedural fairness scale). Having a personal development plan also retained it significant association with the latter outcome, with an adjusted mean difference of 0.1 points on the 1 to 5 scale between those with and without such a plan. Support from managers was no longer significantly associated with either of these job-related well-being once organisational variables were also entered in the model.


143

Table 39. Associations between organisational variables and job-related well-being

Outcome variable

Stage 1 Testing of associations for individual variables: Organisational variables in simple model*, entered individually and unadjusted


Stage 2 Adjusted model – Significant variables for Stage 1 with significant demographic and occupational variables from Chapter 3 models. Only significant organisational variables shown

Stage 3 Only significant variables shown

Anxiety-contentment Positive value of coefficient indicates association with lower anxiety / greater contentment

N= 1683

final model


Role clarity: 0.36 (0.32 to 0.40), p<0.0005

Role conflict: -0.31 (-0.34 to -0.28), p<0.0005

Team conflict: 5.3 (4.6 to 5.9, p<0.0005

Team communication: 0.37 (0.33 to 0.40) p<0.0005

Substantive fairness: 0.35 (0.31 to 0.39), p<0.0005

Procedural fairness: 0.33 (0.29 to 0.36), p<0.0005

Voice: how much are senior managers influenced?: 0.19 (0.11 to 0.26) p<0.0005


Satisfaction with supervision & appraisal: 0.16 (0.13 to 0.19), p<0.0005

Has had an appraisal in past year: 0.13 (0.06 to 0.19), p<0.0005

Has a personal development plan: 0.16 (0.09 to 0.22), p<0.0005

Number of supervisions in past 6 months: 0.007 per meeting (0.001 to 0.013)

Team manager level

Whether all staff have appraisal: -0.10 (-0.19 to -

Role clarity: 0.11 (0.06 to 0.17), p<0.0005


Team communication: 0.09 (0.04 to 0.14) p=0.001


Procedural fairness: 0.07 (0.02 to 0.13), p=0.006

Satisfaction with training: 0.06 (0.02 to 0.11), p<0.0005

Role conflict : -0.10 (-0.13 to -0.06) p<0.0005

Team communication: 0.06 (0.01 to 0.10), p=0.01

Substantive fairness: 0.05 (0.01 to 0.10) p=0.02

Procedural fairness; 0.05 (0.01 to 0.10) p=0.02

Support from colleagues: 0.06 (0.03 to 0.09), p<0.0005


144

0.00), p< 0.04


Negative value of coefficient indicates greater depression / less enthusiasm

N=1536



Role clarity: 0.40 (0.35 to 0.44), p<0.0005

Role conflict: -0.32 (-0.39 to -0.25) p<0.0005

Team conflict: -0.31 (-0.35 to -0.27), p<0.0005


Substantive fairness: 0.39 (0.35 to 0,43) p<0.0005

Procedural fairness: 0.37 (0.33 to 0.41) p<0.0005

Voice: how much are senior managers influenced? 0.25 (0.21 to 0.29, p<0.0005)

Satisfaction with training: 0.33 (0.30 to 0.37), p<0.0005)

Satisfaction with supervision & appraisal; 0.21 (0.18 to 0.24) p<0.0005



Number of supervision meetings: 0.0015 (0.0009 to 0.0022) per meeting, p< 0.0005

Days of training in past year: 0.005 per day (0.0005 to 0.009), p=0.03


Whether all staff have appraisals: 0.13 (0.03 to 0.35), p<0.006

Staff away day in past year: 0.11 (0.01 to 0.20), p=0.04

(Stage 2 – significant stage 1 variables in model with significant demographic and occupational variables:

Role clarity: 0.14 (0.08 to 0.20), p<0.0005

Role conflict -0.11 (-0.15 to -0.06), p<0.0005

Team communication 0.13 (0.07 to 0.18) p<0.0005

Substantive fairness 0.10 (0.05 to 0.15), p<0.0005

Procedural fairness 0.06 (0.01 to 0.12), p=0.03

Satisfaction with training 0.10 (0.06 to 0.15), p<0.0005

Has a personal development plan 0.10 (0.00 to 0.21), p=0.05

(Stage 3)

Role clarity: 0.05 (0.00 to 0.10), p=0.02


Team communication: 0.11 (0.06 to 0.11), p<0.0005

Procedural fairness: 0.06 (0.01 to 0.11) p=0.01

Support from colleagues: 0.06 (0.03 to 0.10), p<0.0005

*As in Section 4.1, throughout multilevel regression has been used to explore associations with outcome variables, using the xtmixed command in Stata 10. The two levels are individual and ward, with individuals nested within wards


145

Finally, Table 40 tests in a similar way for associations between organisational variables and intrinsic job satisfaction. Again a fuller version of the table is to be found in Appendix 1 (Table A6). All the individual staff-level variables were significant when tested individually for their association with team satisfaction. In the final adjusted model, role clarity, role conflict, team communication, substantive fairness and procedural fairness were all significant, with a particularly large adjusted effect for role clarity (0.19 increase in intrinsic satisfaction for each one point increase in role clarity). Once these organisational variables were entered, as with personal accomplishment, work demands and support from manager were no longer independently significant. At ward level, having had an away day and all staff having an appraisal were independently significant when tested on their own, but not with adjustment for other variables, and no other ward-level results were significant. Support for the ward/team manager from more senior management was not significant in this or any other model.


146

Table 40. Associations between organisational variables and intrinsic job satisfaction

Outcome variable Stage 1 Testing of associations for individual variables: Organisational variables in simple model*, entered individually and unadjusted


Stage 2 Adjusted model – Significant variables from Stage 1 with significant demographic and occupational variables from Chapter 3 models. Only significant organisational variables shown

Stage 3 Only significant variables shown


Positive coefficient associated with greater satisfaction


Role clarity: 0.55 (0.51 to 0.59), p<0.0005






Voice: how much are senior managers influenced?: 0.26 (0.23 to 0.30) p<0.0005


Satisfaction with supervision & appraisal: 0.29 (0.26 to 0.32), p<0.0005

Has had an appraisal in past year: 0.27 (0.19 to 0.34), p<0.0005

Has a personal development plan: 0.32 (0.25 to 0.40), p<0.0005

Number of supervisions in past 6 months: 0.02 per meeting (0.01 to 0.03), p<0.0005

Days of training in past year: 0.009 per

Role clarity: 0.29 (0.23 to 0.35), p<0.0005

Role conflict: -0.08 (-0.13 to -0.04), p=0.001


Substantive fairness: 0.06 (0.01 to 0.11), p=0.03

Procedural fairness: 0.08 (0.02 to 0.13), p=0.008

Role clarity: 0.19 (0.14 to 0.24), p<0.0005


Team communication: 0.06 (0.01 to 0.10), p=0.01




147

day (0.005 to 0.014), p<0.0005

Team manager level



* As in chapter 3, throughout multilevel regression has been used to explore associations with outcome variables, using the xtmixed command in Stata 10. The two levels are individual and ward, with individuals nested within wards


148

4.9 Exploratory overall models of staff morale


Many associations between socio-demographic variables and morale indicators lost significance following adjustment for other variables. None was associated with satisfaction or levels of depression-enthusiasm. Women and older age groups had higher morale than men and younger age groups on some indicators. Black staff had less cynicism and greater personal accomplishment but higher emotional exhaustion that White staff. Longer journey times were independently associated with emotional exhaustion.

After adjustment for other variables, being a psychiatrist or a nursing assistant was associated with low morale on some indicators. Neither of these had been associated with low morale in univariate analyses however.

Staff were more enthusiastic during their first year on the ward, but there was no subsequent linear relationship between length of service and morale.

CMHTs had poorer morale than other service types on several indicators. Other relationships between service type and morale lost significance after adjustment for other indicators in these final adjusted models.

Job control was highly significantly associated with all morale indicators.

Work demands were significantly associated with some indicators.

Support from colleagues remained associated with morale in multivariate analysis; support from managers did not.

Role conflict, role clarity, team communication, substantive fairness and procedural fairness were all independently associated with several morale indicators. Having a personal development plan was associated with greater enthusiasm.

Being bullied at work and the frequency of exposure to verbal abuse or threats were both associated with low morale.

Wards in deprived areas had lower morale on several indicators. A higher proportion of male patients was associated with lower personal accomplishment and high use of agency staff was associated with greater anxiety and less satisfaction.

No features of the built environment were independently associated with staff morale; neither were staffing levels. However, staff ratings of quality of built environment were associated with morale.

The final step in our initial analyses of the Modules 1 and 2 data set was to construct a set of exploratory models that included all the types of variables identified as potentially significant influences on morale.


149

4.9.2 Additional variables included in final models

At this stage, we also included a small number of variables, mainly associated with resource use, that do not belong to any of the groups of variables already discussed.

At individual level, the one further variable examined at this stage that has not been included in other sections was journey time, measured in minutes of travel from home to work. 2013 of the sample reported this: the mean journey time was 34.0 minutes (standard deviation 24.3). PICU staff had the longest travelling times at 44.3 minutes and older people’s ward staff the shortest at 26.7 (F=6.5 for one-way analysis of variance comparing service types, p<0.0005).

At ward level, we examined at this stage several variables that reflect aspects of resource use in the wards. Table 41 shows these by ward type. Variations between ward types in bed numbers, staffing and agency staff use were substantial, reaching a highly significant level on one way analysis of variance in each case. General adult wards were the largest wards and PICUs, forensic wards and CAMHS units reported the highest staffing levels, unsurprisingly in the first two cases in view of their function of managing higher levels of risk. PICUs and forensic wards also reported the highest levels of use of agency staff, again probably reflecting the need to maintain staffing levels at all times in order to manage risk.

Before investigating the effects of adding these variables to overall models of the main outcome measures, we investigated using the same methods as in previous sections the extent to which they were associated with each of our main morale indicators.

Journey time had a highly statistically significant positive association with emotional exhaustion (regression coefficient 0.05 (0.02 to 0.07), p<0.0005)) and was also associated with greater depression/less enthusiasm (-0.001 (-0.002 to -0.0003), p=0.02).

Given the large variations between ward types and the different staffing requirements for maintaining safety, we adjusted throughout for ward type in investigating associations with the resources variables. No associations were found between total number of beds except in the case of personal accomplishment, where an association was found between larger number of beds and less personal accomplishment (coefficient -0.13 (95% confidence intervals -0.25 to -0.01, p=0.02) just reached statistical significance. Hours of agency or bank staff time used per bed just reached significance for an association with more anxiety/less contentment (-0.13 (-0.25 to -0.02, p=0.04), but not for other variables. Number of qualified staff per bed was not significantly associated with any morale indicator. However, a significant association was found between total staffing level per bed and personal accomplishment (8.2 (3.0 to 13.4), p=0.002), and that with cynicism approached statistical significance (-3.3 (-6.0 to 0.3), p=0.07).


150

Table 41. Resource use on wards

General

acute wards

N=48

CAMHS

wards

N=10

Forensic

Wards

N=12

Older adult

wards

N=9

Rehab

wards

N=9

PICUs

N=10

All

Mean number of beds on ward (s.d)

20.4

(4.6)

10.4

(2.1)

14.2

(4.4)

17.6

(4.7)

13.4

(4.2)

12.6

(2.5)

16.9

(5.6)

Number of qualified staff on a weekday

late shift

2.3

(0.6)

2.2

(0.6)

2.3

(0.9)

1.8

(0.4)

1.3

(0.5)

2.7

(0.7)

2.2

(0.8)

Number of qualified staff per bed on a

weekday late shift

0.11

(0.04)

0.22

(0.07)

0.17

(0.08)

0.11

(0.03)

0.10

(0.03)

0.22

(0.06)

0.14

(0.07)

Total number of clinical staff on a

weekday late shift (qualified and

unqualified)

4.6

(1.0)

3.9

(0.6)

5.6

(1.6)

4.6

(0.9)

3.1

(0.8)

5.4

(1.0)

4.5

(1.2)

Total number of staff per bed on a

weekday late shift

0.23

(0.07)

0.39

(0.09)

0.43

(0.15)

0.28

(0.11)

0.25

(0.09)

0.44

(0.08)

0.30

(0.12)

Number of hours of agency or bank

staff time used on the ward in a typical

week

89

(89)

44

(62)

128

(79)

93

(88)

47

(38)

165

(137)

84

(91)

Hours of agency/bank staff time per

bed in a typical week

4.8

(4.8)

4.7

(7.6)

9.9

(6.8)

6.0

(7.2)

3.5

(2.4)

13.1

(11.1)

5.7

(6.7)


151

4.9.3 Final exploratory models of morale

Tables 42–47 show the final models obtained for each of the main indicators of morale examined in this report. Models were obtained through the same multilevel regression approaches as in previous sections, with individual staff clustered within wards. Because some of the variables examined were collected only for wards and not for community teams – examples are the built environment variables and data on catchment areas – we have obtained two models for each variable, one for both ward and community staff and one for ward staff only.

Our approach in constructing these models was to include initially (a) age, sex, ethnic group and service type, (b) all other demographic and occupational variables found to be significant in the initial analysis reported in Section 4.3, (c) Karasek’s Demand-Support-Control variables: work demands, job control, support from colleagues, support from manager, (d) organisational context variables found to be significantly associated with each morale indicator in the final models obtained in Section 4.8, (e) adverse events found to be significant in final models in Section 4.5, (f) for wards, physical environment variables found to be significant in Section 4. 6, geographical and clinical population variables found to be significant in Section 4.7, and the resource variables listed earlier in this chapter. As a further test of the robustness of these models, we then investigated the effects of adding to this model each of the variables that had initially been found to be significantly associated with the relevant morale indicator, but where the significant association had no longer been found after adjustment for other variables.

4.9.4 Important caveats regarding the models reported here are:

a) except for the initial tests of the Karasek model, we have throughout tested only for simple linear associations: we have not tested for more complex forms of association or for interaction effects.

(b) the data on which we report here are cross-sectional: we cannot be sure what the direction of causality is where we have found associations.

(c) where associations found on analyses with just one or a few independent variables are no longer significant with adjustment for other variables, it cannot be assumed that this means they have no role in the causal pathways determining morale. For example, the significant association of hours worked with morale indicators disappears once work demands are also added to the model: this is likely to be because the effect of hours worked on morale is mediated by perceived work demands. Likewise, having a personal development plan is no longer significantly associated with various morale indicators once role-related variables are also added to the model: this may well be because effects on morale of having a personal development plan are mediated by greater role clarity and less role conflict. Although we have aimed to avoid entering into models together variables


152

that are very highly correlated with one another, many of the independent variables are associated to some degree.

(d) our analytic strategy is exploratory throughout, investigating possible associations with a large range of variables thought likely to be associated with morale on theoretical and/or empirical grounds rather than testing a small number of defined hypotheses. Particular where findings are only marginally significant and relate to only one indicator of morale, the reader should bear in mind that we have made no adjustment for multiple testing and that it is likely that some of our findings have occurred by chance. Greater weight may be attached to findings that recur for several of the morale measures and that are at high levels of statistical significance with substantial effect sizes.

Summarising these complex models, the main findings from them regarding the factors associated with morale are as below. Ward only models are very similar to those for the whole sample in most cases, apart from effects from variables collected only on the wards.

4.9.5 Socio-demographic variables

Many socio-demographic associations disappear with adjustment for other variables, notably the large effects initially found for ethnic group prior to entry to models of other variables. For intrinsic satisfaction and depression-enthusiasm, there are no associations with demographic variables. Regarding other outcome variables, cynicism has several demographic associations, with less cynicism among women, older age groups, staff from Black and Asian backgrounds, and staff who are married or cohabiting. Other reported associations with demographic variables were greater emotional exhaustion but also greater personal accomplishment among Black staff, greater emotional exhaustion among men, and greater contentment/lower anxiety for the over 55 age group. Longer journey times remained associated with greater emotional exhaustion.

4.9.6 Occupational variables

Many of the relationships reported in 4.1 with occupational characteristics are also no longer present once other variables have been added. In terms of profession, one association that emerges in these final models is an association between being a psychiatrist and poorer morale on several indicators (emotional exhaustion, cynicism, anxiety-contentment and depression-enthusiasm). This is a relationship not found in the initial models in which only demographic and occupational variables are entered, indicating that the morale of psychiatrists is not worse than that of other groups when raw scores are considered, but being a psychiatrist is independently associated with lower morale once adjustment is made for all the other final model variables. Similarly, being a nursing assistant/support worker is associated with lower personal accomplishment and intrinsic satisfaction once adjustment is made for all the other variables, but not initially. The association between being a ward or team manager and


153

greater satisfaction is present both in the initial model and in the final one with adjustment for other potential influences on morale.

Most of the initial relationships with working patterns and working history are no longer significant in these final models, but a strong association between depression-enthusiasm and time on current ward persists, with those in their first year on the ward less depressed than those who have worked their longer. The relationship does not however appear linear, with little evidence that depression-enthusiasm scores continue to deteriorate after this first year. Longer time working in mental health services is associated with lower personal accomplishment and longer time on the ward with poorer anxiety/contentment scores, but these results are not at a high level of significance.

4.9.7 Service type

Many of the initial differences identified in Section 4.1 are no longer significant with adjustment for subsequently added variables, appearing adequately accounted for by differences on these latter variables. Of the persisting differences, several are not highly significant and found only for one indicator of morale. However, there are highly significant differences in these final models between CMHTs and other services on emotional exhaustion, depression-enthusiasm, anxiety-contentment and intrinsic satisfaction.

4.9.8 The Demand-Support-Control variables

Karasek’s model, demonstrated to apply to this group of workers in Section 4.3, largely continues to hold with adjustment for further variables, though with certain exceptions. Job control, the latitude that people have in meeting the demands of their job, is the one variable that is highly significant in every regression, being especially closely associated with intrinsic satisfaction. Perceived work demands were strongly associated with all morale variables except for personal accomplishment and job satisfaction, where there was no remaining association in the final models. As already demonstrated in Section 4.8, associations with support variables, especially manager support, were less stable once variables relating to organisational context were added to the model: manager support did not appear in any of the final models, but support from colleagues remained significantly associated with personal accomplishment, depression-enthusiasm, anxiety-contentment and intrinsic satisfaction.

4.9.9 Organisational context

Variables related to definition of role were also persistently highly associated with morale in final models. For the burnout variables, role conflict was related to emotional exhaustion and cynicism and role clarity to personal accomplishment. Both role variables were related to depression-enthusiasm, anxiety-contentment and intrinsic satisfaction.


154

Team communication was also associated in the expected direction with every morale variable, although for personal accomplishment this applied only to the ward staff only version of the model. Fairness was also retained in several final models, with greater substantive fairness associated with better scores on personal accomplishment, anxiety-contentment, depression-enthusiasm and intrinsic satisfaction; procedural fairness was associated with the last three of these. Of the variables relating to human resources, the only significant one in these final models was a relatively weak association between having a personal development plan and greater levels of enthusiasm/less depression.

4.9.10 Adverse events

Self-reports of having been bullied were associated at a high level of significance with poorer morale measured on the emotional exhaustion, depression-enthusiasm, anxiety-contentment and intrinsic satisfaction scales, although for the ward staff only versions of the anxiety-contentment and intrinsic satisfaction models it was replaced by a related variable, discrimination. Greater cynicism, emotional exhaustion and anxiety were all associated with more frequent experiences of verbal abuse/threats.

4.9.11 Built environment

No specific feature of the ward environment emerged as significant in these final models. However, staff ratings of the quality of the ward environment were significantly associated with emotional exhaustion, anxiety-contentment, depression-enthusiasm and intrinsic satisfaction, in most cases at a highly significant level.

4.9.12 Resources, clinical population and geographical characteristics

Other data collected only regarding wards concerned ward resources, catchment area and clinical population. As would be expected from the initial analyses on these data, relatively few of these characteristics featured in the final models. There was a significant difference in emotional exhaustion and also in anxiety/contentment in the final models between wards in the most deprived and the second most deprived quartile, with morale worst in the most deprived areas. Regarding clinical population, an association was found between a higher proportion of the patient population being male and lower personal accomplishment. Regarding staffing, greater use of agency staff was associated with greater anxiety/less contentment, and also with less intrinsic satisfaction. Staffing levels were not associated with any morale indicator in these final models.


155

Table 42. Final exploratory models – associations with emotional exhaustion: ward and community and ward only

samples

Burnout variable Variables significantly associated

Ward and community samples together



Demographic

Sex: Greater exhaustion in men: 1.0 (0.1 to 1.8), p=0.02

Ethnic group: Black staff more exhausted: 1.3 (0.4 to 2.9), p=0.047. Comparison category - white staff. Groups not significantly different: Asian -0.2 (-1.7 to 1.4), p=0.84. Mixed/other -1.0 (-4.3 to 2.3), p=0.56

Greater exhaustion with longer journey time: 0.03 (0.01 to 0.05) per minute travelled, p=0.001

Occupation

Psychiatrists more exhausted: 4.0 (2.2 to 5.7), p<0.0005 Comparison category - nurses. Groups not significantly different: Nursing assistants/support workers: -0.0 (-1.1 to 1.0), p=0.93, Occupational therapists 1.8 (-0.3 to 3.9), p=0.10, Psychologists 2.2 (-0.6 to 5.0), p=0.13, Social workers -1.0 (-3.1 to 1.1), p=0.35, Ward/team managers -0.9 (-0.8 to 2.6),p=0.36, Other staff 0.9 (-0.8 to 2.6), p=0.29

Service type

More exhaustion in CMHTs (4.1 (2.4 to 5.7), p<0.0005) and CRTs (1.8 (0.1 to 3.4), p=0.04). Comparison service – general acute wards. Service types not significantly different: PICUs: 1.0 (-0.9 to 2.9), p=0.32, CAMHS wards 1.1 (-0.7 to 2.8) p=0.23, Forensic -0.4 (-2.1 to 1.2) p=0.59, Rehab -0.2 (-2.2 to 1.7) p=0.80, older people’s wards 0.5 (-1.3 to 2.4, p=0.57)

Job characteristics

More exhaustion with greater work demands: 4.4 increase in emotional exhaustion per point on work demands scale (3.8 to 4.9), p<0.0005

Less exhaustion with greater job control -0.9 decrease per point on job control scale (-1.5 to -0.6), p=0.001

Organisational variables

More exhaustion with greater role conflict: 1.5 increase per point on role conflict scale (1.0 to 2.1),


156

p<0.0005

Less exhaustion with better team communication: -1.9 decrease per point on team communication scale (-2.5 to -2.4), <0.0005

Less exhaustion with greater substantive fairness: -1.8 decrease per point on substantive fairness scale (-2.5 to -1.2), p<0.0005

Adverse events

More exhaustion if bullied in past year 3.0 (2.0 to 4.0), p<0.0005

More exhaustion if more frequently threatened/verbally abused 1.0 per point on scale (0.7 to 1.2), p<0.0005

Ward staff only


N=1295 in final sample

Greater exhaustion with longer journey time: 0.03 (0.00 to 0.05) per minute travelled, p=0.02

Occupation

More exhaustion among psychiatrists (4.3 (2.2 to 6.4), p<0.0005) and OTs (2.9 (0.4 to 5.5), p=0.03). Comparison category - nurses. Groups not significantly different: Nursing assistants/support workers: 0.2 (-1.0 to 1.4), p=0.71, , Psychologists 1.4 (-2.4 to 1.3), p=0.46, Social workers 0.5 (-4.9 to 6.0), p=0.85, Ward managers -0.6 (-2,.9 to 1.6),p=0.59, Other staff 0.9 (-0.8 to 2.6), p=0.22

Job characteristics

More exhaustion with greater work demands: 4.3 increase in emotional exhaustion per point on work demands scale (3.6 to 4.9), p<0.0005

Less exhaustion with greater job control: -0.9 decrease per point on job control scale (-1.6 to -0.3), p=0.007


More exhaustion with greater role conflict: 1.2 increase per point on role conflict scale (0.6 to 1.9), p<0.0005

Less exhaustion with better team communication: -1.8 decrease per point on team communication scale (-2.6 to -1.0), <0.0005

Less exhaustion with greater substantive fairness: -1.5 decrease per point on substantive fairness


157

scale (-2.3 to -0.8), p<0.0005

Adverse events

More exhaustion if bullied in past year 2.9 (1.7 to 4.2), p<0.0005

More exhaustion if more frequently threatened/verbally abused 1.1 per point on scale (0.8 to 1.4), p<0.0005

Physical environment

Less exhaustion with better staff rating of physical environment: -0.7 decrease per point on scale (-1.4 to -0.04), p=0.04

Area deprivation

Less exhaustion in second most deprived quartile for catchment area employment deprivation than in most deprived quartile -1.6 (-3.1 to -0.1), p=0.04. Quartiles not significantly different: third quartile: -0.8 (-2.2 to 0.5), p=0.23; least deprived quartile 2.2 (-4.4 to 8.9), p=0.5

Table 43. Final exploratory models for associations with cynicism: ward and community and ward only samples

Burnout variable Variables significantly associated

Ward and community staff together

Cynicism

N=1763 in final model for ward and community staff

Demographic

Sex: Greater cynicism in men: 1.6 (1.1 to 2.1), p<0.0005

Ethnic group: Black (-1.1 (-1.8 to -0.4), p=0.001) and Asian (-0.9 (-1.8 to -0.4), p=0.03) less cynical. Comparison category - white staff. Groups not signifcantly different:. Mixed/other -1.2 (--3.1 to 0.7), p=0.22.

Age category: 45 to 54 years (-1.5 (-2.6 to -0.4), p=0.009) and 55 years+ (-2.3 (-3.6 to -1.1), p<0.0005) less cynical. Comparison category: under 25. Groups not significantly different: 25-34 years: 0.04 (-1.0 to 1.1), p=0.94, 35-44 years: -0.7 (-1.8 to 0.4), p=0.19.

Marital status: Less cynicism among married/cohabiting: -0.6 (-1.1 to -0.2), p=0.04. Comparison category – single people. Groups not significantly different: divorced/widowed/separated: 0.6 (-0.3 to 1.4), p=0.23


158

Occupation

Psychiatrists more cynical: 1.3 (0.3 to 2.3), p=0.008. Comparison category - nurses. Groups not significantly different: Nursing assistants/support workers: -0.5 (-1.1 to 0.9), p=0.10, Occupational therapists -0.3 (-1.5 to 0.9), p=0.58, Psychologists 0.01 (-1.6 o 1.6), p=0.99, Social workers -0.2 (-1.0 to 1.4), p=0.0.69, Ward/team managers = -0.3 (-1.4 to 0.7),p=0.36, Other staff 0.1 (-0.8 to 1.0), p=0.04

Service type

More cynicism in PICUs (1.3 (0.3 to 2..4), p=0.01) and CRTs (0.9 (0.01 to 1.9), p=0.045). Comparison service – general acute wards. Service types not significantly different: CAMHS wards -0.9 (-1.9 to 0.9) p=0.07, Forensic 0.4 (-0.5 to 1.3) p=0.36, Rehab -0.5 (-2.5 to 0.6) p=0.39, older people’s wards -1.2 (-0.5 to 1.3, p=0.57), CMHT (0.4 (-0.5 to 1.3), p=0.41.

Job characteristics

More cynicism with greater work demands: 1.0 increase in cynicism per point on work demands scale (0.7 to 1.3), p<0.0005

Less cynicism with greater job control -0.5 decrease per point on job control scale (-0.8 to -0.2), p=0.001


More cynicism with greater role conflict: 0.8 increase per point on role conflict scale (0.5 to 1.1), p<0.0005

Less cynicism with better team communication: -0.9 decrease per point on team communication scale (-1.2 to -0.5), <0.0005

Adverse events

More cynicism if more frequently threatened/verbally abused 0.5 per point on threats/abuse scale (0.4 to 0.7), p<0.0005

Ward staff No ward-only variables significant in final model.


159

Table 44. Final exploratory models for associations with personal accomplishment: ward and community and ward only samples

Morale variable Variables significantly associated with morale variable in final model Ward and community staff Personal accomplishment N=1821 in final model

Demographic Ethnic group: Black staff have greater personal accomplishment: 1.2 (1.5 to 2.3), p=0.03. Comparison category - white staff. Groups not significantly different: Asian 0.7 (=0.7 to 2.0), p=0.33. Mixed/other -0.1 (-2.9 to 2.7), p=0.94 Occupation Less personal accomplishment among nursing assistants/support workers (-0.9 (-1.8 to -0.01) p=0.048) , more among team/ward managers (2.0 (0.4 to 3.6), p=0.02). Comparison category - nurses. Groups not significantly different: Occupational therapists: -0.1 (-2.1 to 1.7), p=0.91, Psychiatrists: 0.29 (-1.2 to 1.7), p=0.7, Psychologists -0.1 (-2.0 to 1.7), p=0.70, Social workers -0.2 (-2.0 to 1.6), p=0.84, Other staff -0.1 (-1.6 to 1.4), p=0.93 Time working in mental health services Less personal accomplishment after 10 to 15 years (-2.0 (-3.5 to -0.4), p-0.01) and after 15 years or more (-1.8 (-3.3 to -0.3), p=0.02. Comparison group: people working in mental health services for less than 2 years. Length of tenure categories not significantly different: 2-5 years: -0.8 (-2.3 to 0.7), p=0.30, 5-10 years -0.8 (-2.3 to 0.7), p=0.29. Service type Greater personal accomplishment in CAMHS wards (1.6 (0.2 to 3.0), p=0.03) and CRTs (1.6 (0.3 to 2.9), p=0.02). Comparison service – general acute wards. Service types not significantly different: PICUs: 0.9 (-0.6 to 2.5), p=0.23, Forensic -1.0 (-2.4 to 0.3) p=0.13, Rehab 0.2 (-1.4 to 1.7) p=0.80, older people’s wards -0.1 (-1.6 to 1.4), p=0.89, CMHTs: 0.6 (-0.7 to 1.9), p=0.02. Job characteristics More personal accomplishment with more job control: 1.6 increase in personal accomplishment per point on job control scale (1.1 to 2.1), p<0.0005 More personal accomplishment with more support from colleagues: 1.1 increase per point on colleague support scale (0.7 to 1.5), p<0.0005 Organisational variables More personal accomplishment with greater role clarity: 2.5 increase per point on role clarity scale


160

(2.0 to 3.1), p<0.0005 Model for ward staff only

Personal accomplishment N=1369 in final sample

Occupation Less personal accomplishment among nursing assistants/support workers (-1.3 (-2.3 to -0.3) p=0.01) more among ward managers (2.5 (0.6 to 4.5), p=0.01). Comparison category - nurses. Groups not significantly different: Occupational therapists: -0.2 (-2.1 to 2.4), p=0.90, Psychiatrists: -0.6 (-2.3 to 1.2), p=0.54, Psychologists 0.8 (-2.6 to 4.2), p=0.65, Social workers 0.7 (-4.4 to 5.8), p=0.79, Other staff -0.9 (-2.7 to 1.0), p=0.36 Time working in mental health services Less personal accomplishment after 15 years or more (-2.0 (-3.7 to -2.7), p=0.03. Comparison group: people working in mental health services for less than 2 years. Length of tenure categories not significantly different: 2-5 years: -0.3 (-2.0 to 1.4), p=0.74, 5-10 years -0.4 (-2.1 to 3.1), p=0.68, 10-15 years: -1.8 (-2.1 to 1.3), p=0.06. Service type Less personal accomplishment in forensic wards (-2.2 (-4.0 to 0.4), p=0.02). Comparison service – general acute wards. Service types not significantly different: PICUs: 0.4 (-1.5 to 2.3), p=0.67, CAMHS -0.7 (-2.4 to 1.0) p=0.43, Rehab 0.2 (-1.4 to 1.7) p=0.80, older people’s wards -0.1 (-1.6 to 1.4). Resources More personal accomplishment with more staff per bed: 0.7 increase with each 0.1 staff member per shift per bed (0.2 to 1.2), p=0.008 Job characteristics More personal accomplishment with more job control: 1.1 increase in personal accomplishment per point on job control scale (0.5 to 1.7), p<0.0005 More personal accomplishment with more support from colleagues: 0.9 increase per point on colleague support scale (0.4 to 1.4), p<0.0005 Organisational variables More personal accomplishment with greater role clarity: 2.5 increase per point on role clarity scale (1.9 to 3.2), p<0.0005 More personal accomplishment with better team communication: 0.8 increase per point on role clarity scale (0.2 to 1.4), p=0.01 Clinical population Less personal accomplishment when a higher proportion of patients are male: -0.3 decrease for each


161

10% increase in proportion of males (-0.7 to -0.1), p=0.001

Table 45. Final exploratory models for associations with anxiety-contentment: ward and community and ward only samples

Morale variable Variables significantly associated with morale variable in final model

Ward and community staff

Anxiety-contentment N=1821 in final model

Demographic

Age group: Greater contentment/less anxiety among 55+ group: 0.18 (0.04 to 0.33), p=0.01. Comparison group: under 25 years old. Age groups not significantly different: 25-34 years: 0.06 (-0.06 to 0.19), p=0.33, 35-44 years 0.09 (-0.05 to 0.21), p=0.17, 45-54 years 0.09 (-0.04 to 0.22), p=0.18.

Occupation

Greater anxiety/less contentment among psychiatrists: -0.18 (-0.29 to -0.07), p=0.001. Comparison category - nurses. Groups not significantly different: Nursing assistants/support workers: 0.03 (-0.04 to 0.10), p=0.44, Occupational therapists: -0.11 (-0.25 to 0.03), p=0.11, Psychologists -0.17 (-0.35 to 0.01), p=0.07, Social workers:-0.04 (-0.18 to 0.10), p=0.55, ward/team managers: 0.02 (-0.10 to 0.14), p=0.70; Other staff 0.06 (-0.05 to 0.17), p=0.31

Time working on current ward

Greater anxiety/less contentment after 1-3 years (-0.12 (-0.20 to -0.04)), p=0.003, 3-5 years (-0.17 (-0.25 to -0.08), p<0.0005, and after more than 5 years (-0.13 (-0.21 to -0.04), p=0.004. Comparison category: staff in post for less than 1 year.

Service type

Greater contentment in older adults’ wards (0.13 (0.02 to 0.25), p=0.03), greater anxiety in CMHTs (-0.29 (-0.39 to -0.17), p<0.0005. Comparison service – general acute wards. Service types not significantly different: PICUs: -0.11 (-0.23 to 0.00), p=0.06, CAMHS 0.03 (-0.08 to 0.14), p=0.58, Forensic 0.10 (-0.00 to 0.20), p=0.06 Rehab 0.05 (-0.07 to 0.17), p=0.39, CRTs: -0.03 (-0.14 to 0.07), p=0.55.

Job characteristics


162

Less contentment/greater anxiety with more job demands: -0.14 decrease in contentment per point on work demands scale (-0.18 to -0.11), p<0.0005

More contentment/less anxiety with more job control: 0.11 increase per point on job control scale (0.07 to 0.15), p<0.0005

More contentment/less anxiety with more support from colleagues: 0.07 increase per point on colleague support scale (0.5 to 1.0), p<0.0005


More contentment/less anxiety with greater role clarity: 0.06 increase per point on role clarity scale (0.01 to 0.10), p=0.01

Less contentment/more anxiety with greater role conflict: -0.07 decrease per point on role conflict scale (-0.11 to -0.04), p<0.0005

More contentment/less anxiety with better team communication: 0.08 increase per point on team communication scale (0.03 to 0.12), p<0.0005

More contentment/greater anxiety with greater procedural fairness: 0.07 increase per point on procedural fairness scale (0.03 to 0.11), p<0.0005.

More contentment/greater anxiety with greater substantive fairness: 0.05 increase per point on substantive fairness scale (0.01 to 0.09), p=0.02.

Adverse events

Less contentment/more anxiety if bullied in the past year: -0.12 (-0.18 to -0.05), p=0.001

Less contentment/more anxiety if more frequently threatened/verbally abused: -0.04 (-0.06 to -0.03) per point on scale, p<0.0005.

Model for ward staff only

Anxiety- Demographic


163

contentment N=1273 in final sample of ward staff only

Age group: Greater contentment/less anxiety among 55+ group: 0.21 (0.06 to 0.37), p=0.007. Comparison group: under 25 years old. Age groups not significantly different: 25-34 years: 0.04 (-0.09 to 0.16), p=0.58, 35-44 years 0.04 (-0.09 to 0.17), p=0.59, 45-54 years 0.04 (-0.10 to 0.17), p=0.59.

Occupation

Greater anxiety/less contentment among psychiatrists: -0.17 (-0.39 to -0.05), p=0.007. Comparison category - nurses. Groups not significantly different: Nursing assistants/support workers: 0.00 (-0.07 to 0.07), p=0.99, Occupational therapists: -0.09 (-0.25 to 0.06), p=0.24, Psychologists -0.05 (-0.28 to 0.18), p=0.68, Social workers:0.17 (-0.23 to 0.56), p=0.40, ward/team managers: -0.01 (-0.15 to 0.12), p=0.84; Other staff 0.05 (-0.08 to 0.18), p=0.43


Greater anxiety/less contentment after 1-3 years (-0.11 (-0.20 to -0.02)), p=0.02, 3-5 years (-0.18 (-0.27 to -0.09), p<0.0005, and after more than 5 years (-0.15 (-0.25 to -0.06)), p=0.001. Comparison category: staff in post for less than 1 year.

Job characteristics

Less contentment/greater anxiety with more job demands: -0.12 decrease in contentment per point on work demands scale (-0.16 to -0.08), p<0.0005

More contentment/less anxiety with more job control: 0.11 increase per point on job control scale (0.07 to 0.15), p<0.0005

More contentment/less anxiety with more support from colleagues: 0.08 increase per point on colleague support scale (0.04 to 0.11), p<0.0005


More contentment/less anxiety with greater role clarity: 0.07 increase per point on role clarity scale (0.01 to 0.12), p=0.01

Less contentment/more anxiety with greater role conflict: -0.10 decrease per point on role conflict scale (-0.14 to -0.06), p<0.0005


164

More contentment/less anxiety with better team communication: 0.07 increase per point on team communication scale (0.02 to 0.12), p<0.0005

Adverse events

Less contentment/more anxiety if experienced discrimination in the past year: -0.12 (-0.18 to -0.05), p=0.001

Less contentment/more anxiety if more frequently threatened/verbally abused: -0.04 (-0.06 to -0.03) per point on scale, p<0.0005.

Physical environment:

More contentment/less anxiety with more positive staff rating of physical environment: 0.15 increase per point on quality of environment scale (0.11 to 0.19), p<0.0005

Staffing

Less contentment/more anxiety with more use of agency staff: -0.07 decrease with every 10 hours of agency time used on ward per week (-0.12 to -0.02), p=0.005

Table 46. Final exploratory models for associations with depression-enthusiasm: ward and community and ward only samples

Variables significantly associated with morale variable in final model

Ward and community staff



Occupation

Less enthusiasm/greater depression among psychiatrists -0.19 (-0.31 to -0.07), p=0.002. Comparison category - nurses. Groups not significantly different: Nursing assistants/support workers: 0.02 (-0.06 to 0.09), p=0.67, Occupational therapists: -0.06 (-0.21 to 0.09), p=0.44, Psychologists -0.17 (-0.39 to 0.04), p=0.12, Social workers:-0.02 (-0.26 to 0.22), p=0.87, ward/team managers: -0.01 (-0.18 to 0.16), p=0.87; Other staff 0.04 (-0.09 to 0.16), p=0.58


165


Greater depression/less enthusiasm after 1-3 years (-0.21 (-0.30 to -0.12)), p<0.0005, 3-5 years (-0.27 (-0.36 to -0.18), p<0.0005, and after more than 5 years (-0.26 (-0.34 to -0.17), p<0.0005. Comparison category: staff in post for less than 1 year.

Seniority

Less depression/more enthusiasm if at senior grade 0.19 (0.03 to 0.10), p=0.003

Service type

Less enthusiasm/greater depression in CMHTs: -0.21 (-0.33 to -0.09), p<0.0005. Comparison service – general acute wards. Service types not significantly different: PICUs: -0.13 (-0.26 to 0.00), p=0.05, CAMHS 0.11 (-0.00 to 0.24), p=0.06, Forensic 0.08 (-0.03 to 0.20), p=0.15, Rehab 0.04 (-0.09 to 0.18), p=0.53, Older people: 0.06 (-0.07 to 0.19), p=0.35m CRTs: 0.07(-0.05 to 0.19), p=0.24.

Job characteristics

Less enthusiasm/greater depression with more job demands: -0.09 decrease in enthusiasm per point on work demands scale (-0.13 to -0.05), p<0.0005

More enthusiasm/less depression with more job control: 0.16 increase per point on job control scale (0.12 to 0.21), p<0.0005

More enthusiasm/less depression with more support from colleagues: 0.06 increase per point on colleague support scale (0.03 to 0.10), p<0.0005


More enthusiasm/less depression with greater role clarity: 0.06 increase per point on role clarity scale (0.01 to 0.11), p=0.02

Less enthusiasm/more depression with greater role conflict: -0.07 decrease per point on role conflict scale (-0.10 to -0.03), p=0.001

More enthusiasm/less depression with better team communication: 0.12 increase per point on team communication scale (0.07 to 0.17), p<0.0005


166

More enthusiasm/less depression with greater procedural fairness: 0.07 increase per point on procedural fairness scale (0.03 to 0.11), p=0.001.

More enthusiasm/less depression with greater substantive fairness: 0.08 increase per point on substantive fairness scale (0.03 to 0.12), p=0.002.

More enthusiasm/less depression if has a personal development plan: 0.09 (0.03 to 0.15), p=0.004

Adverse events

Less enthusiasm/more depression if bullied in the past year: -0.12 (-0.18 to -0.05), p=0.001

Model for ward staff only


N=1201 in final sample of ward staff only

Demographic

Less enthusiasm/more depression with longer journey: -0.01 (-0.03 to -0.00), p=0.02 per 10 minutes additional travel

Occupation

Less enthusiasm/greater depression among psychiatrists -0.14 (-0.29 to -0.00), p=0.049 Comparison category - nurses. Groups not significantly different: Nursing assistants/support workers: 0.02 (-0.10 to 0.06), p=0.65, Occupational therapists: -0.08 (-0.25 to 0.10), p=0.40, Psychologists -0.10 (-0.40 to 0.19), p=0.12, Social workers:-0.22 (-0.74 to 0.29), p=0.49, ward/team managers: -0.18 (-0.40 to 0.05), p=0.39; Other staff 0.00 (-0.16 to 0.16), p=0.99


Greater depression/less enthusiasm after 1-3 years (-0.19 (-0.29 to -0.09)), p<0.0005, 3-5 years (-0.25 (-0.36 to -0.15), p<0.0005, and after more than 5 years (-0.24 (-0.34 to -0.14), p=0.001. Comparison category: staff in post for less than 1 year.

Seniority

Less depression/more enthusiasm if at senior grade: 0.31 (0.12 to 0.49), p=0.003


167

Contract

Less depression/more enthusiasm if on bank/agency contract: 0.33 (0.10 to 0.57), p=0.005

Service type

Less enthusiasm/greater depression in PICUs: -0.17 (-0.31 to -0.04), p=0.01. Comparison service – general acute wards. Service types not significantly different: CAMHS 0.14 (-0.00 to 0.28), p=0.054, Forensic 0.00 (-0.12 to 0.13), p=0.97, Rehab 0.04 (-0.11 to 0.17), p=0.62, Older people: -0.03 (-0.16 to 0.11), p=0.68

Job characteristics

Less enthusiasm/greater depression with more job demands: -0.09 decrease in enthusiasm per point on work demands scale (-0.13 to -0.04), p<0.0005

More enthusiasm/less depression with more job control: 0.17 increase per point on job control scale (0.13 to 0.22), p<0.0005

More enthusiasm/less depression with more support from colleagues: 0.08 increase per point on colleague support scale (0.03 to 0.12), p<0.0005


More enthusiasm/less depression with greater role clarity: 0.06 increase per point on role clarity scale (0.01 to 0.12), p=0.045

Less enthusiasm/more depression with greater role conflict: -0.07 decrease per point on role conflict scale (-0.11 to -0.02), p=0.003

More enthusiasm/less depression with better team communication: 0.10 increase per point on team communication scale (0.04 to 0.16), p=0.001

More enthusiasm/less depression with greater procedural fairness: 0.07 increase per point on procedural fairness scale (0.02 to 0.12), p=0.009

More enthusiasm/less depression if has a personal development plan: 0.10 (0.03 to 0.17), p=0.008

Adverse events


168

Less enthusiasm/more depression if bullied in the past year: -0.19 (-0.28 to -0.11), p<0.0005

Physical environment:

More enthusiasm/less depression with more positive staff rating of physical environment: 0.14 increase per point on quality of environment scale (0.10 to 0.19), p<0.0005

Area deprivation

More enthusiasm/less depression in second most deprived quartile for catchment area employment deprivation than in most deprived quartile 0.13 (0.03 to 0.24), p=0.01. Quartiles not significantly different: third quartile: 0.01 (--0.09 to 0.12), p=0.78; least deprived quartile -0.10 (-0.53 to 0.34), p=0.67

Table 47. Final exploratory models for associations with intrinsic satisfaction: ward and community and ward only

samples

Variables significantly associated with morale variable in final model

Ward and community staff Intrinsic satisfaction N=1813 in final model

Occupation Nursing assistants/support workers less satisfied: -0.8 (-0.14 to -0.2), p=0.01. Comparison category - nurses. Groups not significantly different from nurses: Occupational therapists: -0.01 (-0.15 to 0.12), p=0.87, Psychiatrists: -0.03 (-0.14 to 0.08), p=0.59, Psychologists -0.15 (-0.33 to 0.08), p=0.11, Social workers:-0.05 (-0.19 to 0.08), p=0.45, ward/team managers: 0.04 (-0.08 to 0.16), p=0.48; Other staff -0.07 (-0.19 to 0.08), p=0.48 Service type Less satisfaction in CMHTs: -0.11 (-0.21 to -0.01), p=0.04. Comparison service – general acute wards. Service types not significantly different from acute wards: PICUs: -0.05 (-0.17 to 0.07), p=0.44, CAMHS 0.01 (-0.10 to 0.12), p=0.85, Forensic 0.01 (-0.09 to 0.12), p=0.83, Rehab -0.04 (-0.17 to 0.08), p=0.52, Older people: 0.03 (-0.15 to 0.09), p=0.67, CRTs: 0.08 (-0.02 to 0.18), p=0.12. Job characteristics More satisfaction if greater job control: 0.33 increase per point on job control scale (0.30 to 0.37), p<0.0005 More satisfied if more support from colleagues: 0..05 increase per point on colleague support scale (0.03 to 0.08), p=0.001 More satisfied if from ward/team manager: 0.05 increase per point on manager support scale (0.03 to 0.08), p<0.0005


169

Organisational variables More satisfied if greater role clarity: 0.20 increase per point on role clarity scale (0.15 to 0.24), p<0.0005 Less satisfied if greater role conflict: -0.06 decrease per point on role conflict scale (-0.09 to -0.03), p<0.0005 More satisfied if better team communication: 0.15 increase per point on team communication scale (0.11 to 0.19), p<0.0005 More satisfied if greater substantive fairness: 0.05 increase per point on substantive fairness scale (0.01 to 0.09), p=0.02. More satisfaction if greater procedural fairness: 0.11 increase per point on procedural fairness scale (0.07 to 0.15), p<0.0005. Adverse events Less satisfied if bullied in the past year: -0.07 (-0.13 to 0.00), p=0.05

Model for ward staff only Intrinsic satisfaction N=1317 in final sample of ward staff only

Occupation Nursing assistants/support workers less satisfied: -0.9 (-0.16 to -0.2), p=0.01. Comparison category - nurses. Groups not significantly different from nurses: Occupational therapists: 0.15 (0.0 to 0.31), p=0.052, Psychiatrists: -0.05 (-0.17 to 0.08), p=0.46, Psychologists -0.33 (-0.27 to 0.21), p=0.79, Social workers:-0.02 (-0.39 to 0.35), p=0.92, ward/team managers: 0. (-0.08 to 0.16), p=0.48; Other staff -0.09 (-0.21 to 0.04), p=0.18 Job characteristics More satisfaction if greater job control: 0.31 increase per point on job control scale (0.27 to 0.35), p<0.0005 More satisfied if more support from colleagues: 0.07 increase per point on colleague support scale (0.03 to 0.10), p<0.0005 More satisfied if from ward/team manager: 0.06 increase per point on manager support scale (0.03 to 0.09), p<0.0005 Organisational variables More satisfied if greater role clarity: 0.18 increase per point on role clarity scale (0.13 to 0.23), p<0.0005 Less satisfied if greater role conflict: -0.07 decrease per point on role conflict scale (-0.11 to -0.04), p<0.0005 More satisfied if better team communication: 0.15 increase per point on team communication scale (0.10 to 0.20), p<0.0005 More satisfied if greater procedural fairness: 0.11 increase per point on procedural fairness scale (0.07 to 0.16), p<0.0005. More satisfied if has a personal development plan: 0.05 (0.00 to 0.12), p=0.05


170

Adverse events Less satisfied if experienced discrimination n the past year: -0.11 (-0.18 to 0.04), p=0.002 Physical environment: More enthusiasm/less depression with more positive staff rating of physical environment: 0.10 increase per point on quality of environment scale (0.06 to 0.14), p<0.0005 Staffing Less satisfied if more use of agency staff: -0.05 decrease with every 10 hours of agency time per bed used on ward per week (-0.10 to -0.01), p=0.04


171

4.10 Module 3 – a qualitative investigation of staff morale


There were few clear-cut distinctions between high and low morale wards in terms of the factors influencing morale. More common was variation at the ward level, partly attributable to differences in service type.

The most important positive influences on morale were relationships with colleagues and interactions with patients. Seeing patients recover was often described as an extremely rewarding part of work.

Insufficient staffing levels emerged as the most important negative influence on morale.

Relationships with managers and other leading figures were another important influence, with mixed experiences reported. Effective and inclusive leadership could boost morale, whilst under-involvement had a negative impact.

Staff described feeling alienated from senior managers above ward and team level.

A sense of being listened to, valued and supported was crucial for morale.

Staff and managers stressed the importance of role clarity in building confidence, and the ward-level structural devices that help maintain it.

Supervision was thought to be important for ensuring role clarity and provided staff with an outlet through which to voice their personal needs.

There was discussion of the difficulties involved in being with patients, including the challenges posed by particular client groups and the threat of violence.

Training opportunities varied and resource limitations were cited as a major impediment to accessing training.

Staff described how the physical environment of the ward and working conditions could affect morale, both positively and negatively.

Data were collected from four initially high morale and three initially low morale wards. Our original plan was to collect data from 10 wards: however, a number of difficulties with staff and service access that were confidentially outlined in previous interim reports to the SDO prevented us from realising this target, and a purposive sample


172

of seven was instead obtained. These comprised three acute wards, one rehabilitation ward, one recovery unit, one child & adolescent unit and one psychiatric intensive care unit. The complete data set consisted of 12 group interviews, 24 ward staff interviews (8 managerial staff, 16 non-managerial staff), and 21 patient interviews, 7 senior manager interviews, and 7 accompanied walks. Appendix 11 provides full details of data collected from each ward and participants’ characteristics. Full focus groups of 6 to 8 had been planned, but unfortunately proved impossible to fit in with ward staffing patterns in many cases: thus we have instead in most cases conducted smaller group interviews of 3 to 5 people. All data collection took place in 2008 and 2009.

A major planned focus for this qualitative study had been comparison between high and low morale wards. However, such a clear comparison turned out not to be available because of shifts on low morale wards towards higher morale, as described below in Section 4.13. Thus in the initial set of analyses, presented here, our main approach was a straightforward thematic analysis, focused on identifying themes that emerged from the data as relevant to understanding the determinants of staff morale and how it might be improved. Further analyses of this rich data set are still planned: focuses for this will include analysis from an organisational perspective, encompassing both the qualitative data and the materials collected regarding ward organisation, policies and procedure, and analysis focusing more on the patient data and the data on built environment. Key themes identified from analysis of qualitative interviews and focus groups are presented below.

4.10.2 Staffing levels

Dissatisfaction with staffing levels was widely expressed amongst staff in both high and low morale wards. There were frequent comments about recent and ongoing cuts in resources. Staff often linked increasingly stringent budgetary constraints to the “business”-minded model of Foundation Trusts. Many of those working directly with patients felt overworked, describing the physical and emotional toll of a busy shift.

…its madness non-stop. We’re just from one task to the other aren’t we pretty much all day? (Staff nurse, L focus group)

Heavy work demands were felt to constrain staff’s ability to carry out their role. They described the demoralising effect of feeling they had to compromise patient care. Many saw spending time with patients as the central and most rewarding part of their job and disliked having to spread themselves so thin.

I go home thinking, exhausted, but also I feel that I haven’t accomplished what I wanted to do for that shift – I’ve left loads of things undone. (Staff nurse, L)

A number of patients also commented that they would appreciate more informal contact with staff.


173

Conversations; how did your day go? You know, just normal stuff and laugh and joke with each other. Patient, L)

Another major concern was a perceived over-reliance on “bank” staff. Participants spoke of their feelings of uncertainty about the skills and expertise of bank staff, particularly with regards to “control and restraint” procedures:

I don’t know if there comes a situation where the patient is charging at me, if this person will know what to do, how to back me up. You are, always wondering…. ., “I wonder what could happen”? (Staff Nurse, L)

A feeling that the team is under-resourced could foster a sense of unease, pervading even in the absence of patient aggression.

I mean cutting down numbers of staff is never popular whatever the reasons. … Staff went through a lot of anxiety of how they were going to be able to cope, what about incidents, what would happen and things like that, so I think that’s had a negative impact on staff really. (Consultant psychiatrist, L)

The thing that I find the most stressful is not actually when something is happening, it’s the bit before and not knowing if anything is going to kick off – that’s the bit that I find the most stressful.(OT, H)

Difficulties were also seen to arise from bank staff being unfamiliar with the ward and its systems. Staff noticed that patients were generally reluctant to approach people they did not know and several patients confirmed this. This places the burden of responsibility on regular staff members:

They don’t really know them, it’s really frustrating and I mean we’re kind of… You feel like you’re kind of carrying the whole ward on yourself, by yourself. (Healthcare Assistant, L)

…they (permanent staff) end up shouldering more of the workload purely because they know the clientele and they know the system, so I think that obviously is a drain on people and it can lead to burnout and fatigue if you like. (Senior Nurse, L)

Several participants said they struggled to find time for a break. Particularly for those working in acute care wards, there was a fear of leaving the rest of the team vulnerable.

But you know you could go and have break, but I wouldn’t want to walk off the ward and say ‘Oh I’m going off for my forty-five minutes now’ and then someone gets hit, because then I’d think ‘Well, if I had have been there that wouldn’t have happened.’ (Healthcare Assistant, L)

Staffing levels also made it difficult to organize supervision and training. Again, this was particularly the case in the acute wards, where the risk of incidents intensified the need for adequate staff presence on the ward.

The staffing resources are there to run the ward and we do quite well, but there’s not any extras. (Deputy Ward Manager, L)

Just getting on with the day to day work means that some of the things that might actually be more supportive for people, like meeting together...get pushed to one side. (Consultant Psychiatrist, L)


174

Some staff saw staffing levels as the central most important issue threatening morale:

So I think the main issue facing the ward in terms of creating a more harmonious...a happier environment would be more staff. We need more staff desperately and yes, that’s probably the one thing more than anything else really because that would free up everything. That would free up the off-duty and the annual leave, the morale, the pressure and people would enjoy their job more.( Nursing Assistant, L)

4.10.3 Peer relationships/teamwork

Many participants identified their relationships with colleagues on the ward as the most important positive influence on morale. Being able to work effectively as a team was seen as vital for the smooth running of the ward:

…The tag team approach. So if you’re flagging in energy you could say to someone else, can you go in, I’m going to retreat. We certainly choreograph ourselves well…(Social Worker, H)

I mean, crucially the morale issues, where things go wrong in an inpatient unit like [ward X] is when staff stop talking to each other about their different points of view. (Clinical Director, H).

Furthermore, the feeling of being part of a team was clearly conducive to a positive work atmosphere. Teamwork was described as a positive influence by staff in both high and low morale wards. Although there were no clear-cut differences between wards, staff from two wards - the Recovery Unit and the Child and Adolescent Unit (both high morale) - spoke in particularly positive terms about the familial-type relationship dynamic at work.

Teamwork contributed a sense of shared responsibility, diluting the pressure felt by individual team members. Staff spoke of their reliance on informal peer support; sometimes as an alternative where formal supervision was not available:

The only thing that sort of keeps me going here is the sense of teamwork that we do have on the ward. We all pull together as a team and I think that’s one of the things that keeps me going. (OT, L)

So peer group supervision informally, and that sort of on the spot feedback, there’s a good camaraderie in team members here. And I think we will jump in and help each other out, so I trust my colleagues and they’re the people I get the most positive feedback from. … I think that’s always been the working culture here. It’s one of the reasons I came back. (Social Worker, H)

Those who felt comfortable talking freely to one another clearly valued the opportunity. A culture of openness and acceptance, where staff are encouraged to give their views, was strongly associated with good morale. The following quote from a Clinical Psychologist exemplifies the positive impact of a collaborative work ethic.

I think it goes back to what people have been saying about people liking. Trusting and respecting each other. There’s a safety to disagreeing with each other, because you feel that, the philosophy is that we’ll work on that, talk about it in staff support and then we’ll get back to continuing to work well together.. (Clinical Psychologist, H)


175

Teamwork was thus important for staff of all levels and grades. What was particularly beneficial was the extension of the “team” across the hierarchy. Appreciation of a non-hierarchical atmosphere is captured in the following remark, which elaborated on a metaphor relating to being in a war in terms of camaraderie, and being embattled.

When we’re thinking about trenches - certain parts of the, the troop are in the trench with the tarpaulin covering them and others are ensconced in nice offices that are barricaded. So I feel that it’s shared that there isn’t that distinction between, you know, officer class who can just stay out of it and let the troops, you know, just get on with it (Lead nurse, H)

The notion of community as a protective factor was common. Indeed, some described how things that would otherwise be demoralizing could actually enhance morale by fostering a sense of “togetherness”.

I do think the… having an external bone of contention that everyone can moan about has a very useful function though. (Social Worker, H)

I think one thing about the NHS is there’s quite an almost like Dunkirk spirit about it. …..you tend to kind of fight to the death for things, so I think one of the kind of things that keeps you going is that you know pretty much everybody else is in the same boat as you are and everybody is feeling just as stressed as you are. (Senior OT, H)

As a caveat, one or two mentioned that a very tight-knit ward community had the potential to engender fallouts and “cliques”:

I think it can be a very kind of supporting and holding, a containing place to be, but at the same time it can also be a bit of a hotbed. I don’t know, trickiness really, you know, that sort of raw kind of front line stuff. (Ward Manager, H)

4.10.4 Leadership and management

Relationships with lead staff on the ward (including managers and clinical leads) were highly mixed. Participants, especially those with some managerial responsibility, stressed the importance of strong and effective leadership. Consistency in leadership, aided by effective communication within the managerial team, was reassuring for staff. Weak leadership on the other hand was associated with ambiguity and uncertainty, and could be unsettling. Individual comments revealed the variability of perceived management competency:

The thing is, it's absolutely crucial that they operate as one, you know. So, even in the absence of one, the other one is saying exactly the same thing. And, I think, what really impacts negatively is when staff go to different people and get different answers-; fudges, indecision. (Service Manager, L)

Perceived leadership competency appeared to be varying on a ward or individual level, rather than with ward-morale. This may have been due to high rates of staff turnover. In six out of the seven wards there was some mention of changes in the composition of the management team. It is perhaps notable that in the one exception (the Child & Adolescent Unit), the senior manager emphasised the value of a stable leadership team (interestingly though, this


176

appeared to make the ward more vulnerable when it came to changes – see below):

I think that morale, the things that influence morale in a positive way are stability of the staff team, particularly in leadership functions, which we have. And I would argue that we have an effective leadership team; this kind of work brings its troubles but overall there’s a leadership team which I think is very responsive and containing and supportive of the wider staff team and very good at its job. So, yeah, quite a lot of… I think that’s absolutely 90% of the whole thing. (Clinical Director, H)

Some junior staff felt isolated from managers and senior staff. They valued the visible presence of managers on the “shop floor”. This comment from a Nursing Assistant describes the threat to cohesion posed by physical separation:

There’s a big divide between MDT and the nursing team at the moment, and it has been recognised... we discussed it in staff support. In the old building, they’d walk out of their rooms and they’re right in the lounge where all the kids are, so they’d often be popping in and out; they’d come in there, have tea and toast with the kids. You’d always have staff who’d be around, so if you had to run off and do something they would be there. You know, they’re much more involved. And here it’s like, very easy for them to be tucked away in their little offices up here, well away from the house. (Nursing assistant, H)

Discussions about leadership focused a great deal on individuals, and individual experiences. Leading figures on the ward had the potential to exert a boost on morale, and several participants (on different wards) made reference to the impact of inspiring individuals. On one acute ward in particular, repeated reference was made to a new consultant psychiatrist:

I think they’ve got a very, really good new consultant, and I think he is fantastic. Going back to the negative thing, they had a locum consultant for about a year and a half. I don’t think it worked at all. They got very mixed messages about patients’ care. (Lead Nurse, L)

4.10.5 Support and supervision

Managers and senior managers were generally unanimous in the opinion that support and supervision were central for maintaining morale. Supervision served multiple functions. Staff particularly valued the “debriefing” element, with the opportunity to receive guidance and feedback. This helped solidify roles and responsibilities and improved confidence. Some participants also appreciated having a space to ventilate emotions (though this was more often accessed through informal peer support rather than formal supervision). Discussion about exactly how supervision ought to be used was more prominent amongst managerial staff. Some saw the provision of support and supervision as their most important responsibility. Several admitted shortfalls and identified this as an area for improvement. Senior staff also expressed concerns about the nature and quality of supervision. Some wards made use of staff-support groups, on which views were highly mixed. Senior staff tended to promote the value of mutual emotional support. However several


177

front-line staff members said they felt “uncomfortable” in a structured group setting:

Sometimes you just want to moan and you don’t want anyone to interpret your moan; you just want to moan. (OT, H).

Supervision also provided an outlet for staff to voice their personal needs. Some staff members expressed their exasperation at feeling they needed repeatedly to hassle for their needs to be addressed:

I would just like whatever issues I raise to be dealt with without me having to chase them up three or four times and it’s really, like… You know, it kind of undermines… You feel, like, you know, no one cares (Nursing Assistant, L)

In the context of a discussion about the air quality on the ward, one nurse said:

Anything you say is like, it stops at the person you say it to. It stops right there. Nothing goes further. No help comes from above or wherever. Nothing. (Staff nurse, L)

Support from senior staff following incidents of violence or assault on the ward was felt to be particularly important; again, experiences were mixed. Although informal support from peers was generally viewed positively, several people said they would appreciate more support from “above”. Comments suggested that the feeling of being neglected was particularly demoralizing.

They used to come down afterwards and check if everyone was alright and that’s important, you know? The small things make a big difference. (Staff Nurse, L)

In a similar vein, front-line staff greatly valued positive feedback from their managers. A number of people across both ward types commented that praise was rare, emphasizing the boost in morale it might otherwise provide.

A lot of the time I suppose the patients give us more praise than the staff. (Nursing Assistant, H)

The availability of formal supervision and the extent to which staff felt supported in their roles varied enormously. Although there were no clear-cut distinctions, certain trends did emerge. Staff in high-morale wards talked more about their emotional connections with leading staff and peers. Feeling looked after, known, and appreciated contributed to a sense of belonging, which was clearly more a feature of some wards than others. The Recovery ward, in particular, emerged as upholding a very supportive environment. In contrast, the feeling of being under-valued was more apparent amongst those in low-morale wards. Nevertheless, once again there was a high degree of variation at the ward level and even between individuals on the same ward. The following two quotes, both in fact from (different) low-morale wards, give some idea of the disparity between individuals’ experiences:

I think a major problem as well is that I think we’re bending over backwards to look after the patients, but we’re not being looked after. Breaks are really hard to take and it’s just that more and more is being taken and it’s a case of well, it’s effective and it’s not really a case of ‘you’re doing a good job, so good on you’ sort of thing and ‘You’ve


178

got through the shift, what are you complaining about?.....It’s not that bad here, it could be worse somewhere else.’ (Nursing Assistant, L).

Yeah, yeah our manager is tops, she’ll bend over backwards for you to [squeaking] sort of make sure you’re okay; she looks after everyone. All the staff try to look after each other too, we all try, we all support each other (Staff nurse, L)

4.10.6 Relationships with senior managers

Staff described feeling alienated from those in senior management positions (i.e. those more senior than the ward manager based on the ward). This issue came up in all seven wards. There was a widespread belief that senior managers had a poor understanding of the nature of work on the ward. Participants remarked that senior managers were scarcely seen on the ward, and when they were it tended to be when things went wrong. This, in conjunction with perceived staff deficiencies, left some staff feeling that financial concerns obscured concern for staff welfare:

I just think sometimes the managers are up there, they have their job we have our job, but I don’t think they understand what we really do. They’d have to spend like two weeks solid working with us 12 hours a day to understand what’s going on. Because as much as they’re saying, do this, do that, we need this, we need that, it’s like, well hang on we’re doing what we can - why don’t you just come here for a week and you’ll see what it’s about? (Staff nurse, L)

I think the other thing is, which affects morale as well, I mean, because the senior managers, I don’t know, whoever writes policies, they’ve never been on the wards and stuff like that. And then they come up with all these great ideas, how we should work and what kind of impact, what impacts us and stuff like that, which to me, I think, is nonsense. For you to kind of implement good policies, you should actually be in the environment and see how the shift goes and what patients need and stuff like that. (Charge nurse, L)

Interestingly however, staff did not necessarily perceive the absence of senior management as a negative factor. On the Recovery Ward, staff valued a sense of insularity which incorporated a degree of independence from senior managers:

Within the recovery team we’re in this little bubble… and we’re quite a low profile unit anyway because we’re right in the middle of the community and it’s just a tree lined avenue and you wouldn’t noticeably know that we’re a mental health unit. (Staff Nurse, H)

4.10.7 Perspectives of ward managers

Relationships between managers and senior managers were variable. Ward managers were generally well aware of the opinions of nursing staff regarding senior managers and argued for greater integration.

I think part of the kind of morale stuff, and being listened to was felt at the time to be a real kind of division and kind of lack of, understanding from management perspective, of the reality of the floor. (Ward manager, H).

Several managers described the difficult position of being “sandwiched” between two tiers. Mediating between them involved a delicate “balancing act”. Although effective communication was


179

acknowledged as vital, managers were aware that there ought to be limits to transparency, and felt a responsibility to protect as well as engage front-line staff. Managers felt pressured from “above” by budgetary constraints and sometimes had to implement unpopular policies. Some also felt a degree of role conflict in having to balance managerial and clinical responsibilities. Differences were observed here; one manager in particular disliked having to make up part of “the numbers”, whereas others valued their clinical time.

4.10.8 Voice: opportunities/empowerment

A related theme was the importance of having an input into decisions about patient care. A few junior members of staff were frustrated at being excluded from ward rounds despite spending a great deal of time with patients.

I don’t get a chance, really, to give much feedback, so it was a really good opportunity for me to do that and I think that if that opportunity was given more often it would be really good. I mean, I would feel a lot more valued because I feel like I’m just here to kind of go through processes and the mechanics of the day and then that’s it. I don’t feel, like, I have an opinion and that’s really valued, taken into account and valued in any way. (Nursing assistant, L)

Others were irritated at not having a say in policy-making decisions:

For you to kind of implement good policies, you should actually be in the environment and see how the shift goes and what patients need and stuff like that. (Charge nurse, L).

Interestingly, rather more was said on this theme by managers and leading clinicians than front-line staff. (For the latter, feeling “unheard” was more of an issue in relation to working conditions and personal needs than to clinical decision making). Managers described their efforts to include staff in decision-making and the difficulties this posed. Obstacles to greater inclusion included logistical factors (the need to ensure adequate 24 hour staff coverage of the ward), nervousness on the part of junior staff members and the historical legacy of the medically-led hierarchy. The importance of empowering the staff team is encapsulated in the quote below from a consultant psychiatrist:

If you don’t give people support, if you don’t allow people to have a voice that is much more undermining to morale, I would say. Because then people don’t have a sense of, almost, importance in what they’re doing, which is so crucial and so central - and if you take that from an individual in the workplace, what on earth do they have? Why on earth should they give of themselves? (Consultant Psychiatrist, L)

4.10.9 Clarity and Confidence

There was also some variability in the extent to which staff members wanted greater responsibility:

I’m quite happy with my job, being a nursing assistant and I even got a chance to go and do my training., I said I didn’t want to. I’m happy with this job…..it’s a routine


180

thing now. I know I have to do, not being told to do things,….I know what you call it, my responsibility is. (Nursing assistant, H)

With heightened responsibility, the potential risk implications of error and misjudgment are amplified. It therefore becomes more important that staff feel confident and adequately supported in their roles. The following comment comes from the same Nursing Assistant quoted above, who had to attend a ward round unexpectedly:

…it was a really good experience but, it was like being thrown in the deep end. I really enjoyed it but I was… I think it kind of built some stress and here was I thinking, oh, my God, and I’m gonna have to sit there with the consultant and, like, talk about all the patients. I know them all really well but it’s still really quite an intimidating situation. (Nursing Assistant, L)

Both front-line and managerial staff identified the importance of having a clear sense of one’s responsibilities as a way of maintaining confidence:

… people feeling that they can do their job not just adequately, but efficiently and effectively for the best of the service users. (Senior Nurse, L).

One ward had recently changed its intake criteria so as to accept more acutely unwell clients. Managers on this ward were aware of the mixed impact this was having, and the need to balance changes with adequate support for staff.

4.10.10 Consistency of structures

One way of maintaining role clarity was through the provision of structural clarity. Consistency of protocols and guidelines were important for building confidence and morale, whilst change and uncertainty created anxiety.

So if you can have cohesion in terms of a cohesive, communicating staff group and cohesion in the sense of structure,.. in terms of the way ward rounds, business meetings, go and operate that acts as a defence against the anxiety and chaos of psychosis. In my experience that, that really assists the efficiency of the ward and that leads to pepping up morale, sustaining morale. (Consultant Psychiatrist, L)

On all seven wards there was some mention of recent structural or organisational changes. These ranged from relatively minor changes to the organization of work and ward policy to large-scale shifts in remit and/or relocation. Changes were inevitably received differently depending on their implications, but managers in particular remarked that there seemed to be something stressful about coping with change itself:

The team worked incredibly hard for four months on about 30 different protocols with groups of four or five people meeting, you know, three or four times. I’m talking of, hundreds of hours of work of, of man/woman hours of work. Only to be told, no we’re not going to do that, we don’t want you to do that. And that makes my skin crawl now saying it. It was horrifying, and it has been up and down, having the most negative impact on my staff and my morale. For me it’s been the incompetence and uselessness of the commissioners. (Ward Manager, H)


181

4.10.11 Structure: organisation of work

Structure was also important for the organization of work on the ward. Formal frameworks were vital for the maintenance of regular formal supervision and team meetings. Without structure, these tended to fall by the wayside. As well as the direct negative implications of inadequate supervision, failure to prioritise it could lead nurses to feel under-valued. Other comments related to the planning of shifts and rotas. A few people reported problems coordinating work and home life, commenting that they would appreciate more flexibility around shift patterns:

We were told, you know, you definitely won’t have less than three weeks between your nights. It’s not true; we have done. So people are pissed off about that and t... there’s not much thought being put into us and, like, what we need, and, kind of, no flexibility around the rota really. (Healthcare Assistant H)

Juggling multiple commitments was stressful in itself, but it was the feeling of not being listened to that was particularly demoralizing.

From my experience what I found in all my different roles is that it’s not always the fact that things can be changed. It’s the fact that you actually feel that you’re being listened to… (Ward Manager, H).

4.10.12 Training

Opportunities for training were another source of disparity between wards. Those in high-morale wards had more positive things to say about training than those in low-morale wards. Ward managers were generally keen to promote training; not just to improve standards, but also to boost morale. Training was seen as a way of maintaining role clarity and imbuing confidence. It also allowed staff to “look forward” and sent a message that they were valued. Those able to access ongoing professional development courses were extremely grateful. Across the board however, there was frustration at the restrictions to accessing training. Several participants commented that resource limitations meant that aside from mandatory courses, training had to be done in their own time. Managers were sympathetic and often resented the way tightening budgets forced them to curtail training.

But, the thing is, sometimes we're, kind of, we're thwarting people's ability to, kind of, develop. And then they leave out of anger and frustration, rather than leaving feeling really good,- helped to get where I am now and I'll always remember it fondly, that kind of thing. (Service manager, L).

4.10.13 Being with patients: client groups

Participants were asked how they felt about the particular client group they worked with. Although some general themes did emerge, there was a great deal of individual variability on this issue.

Those in acute and intensive care wards frequently described the stresses involved in working with patients who are severely unwell.


182

Referring to the importance of team cohesion, one consultant described vividly the challenges posed by working with people with psychosis:

I think psychosis has a way of inducing chaos and fragmentation, and it’s kind of like a manifestation of the condition but also, somehow that gets projected into structures and organisations and systems, in my experience, and there’s plenty of room for chaos in a ward environment - especially within a busy ward environment. (Consultant Psychiatrist, L)

Some participants had experience working in different settings and two of the wards had recently changed their remits. Several commented that acute care was more stressful than other types of work. On the other hand, at least two members of staff said they preferred the intensity of acute care. As indicated in the quote below, there was an acknowledgement that stressfulness is, to a certain extent, intrinsic to the work (especially on acute wards).

You know, it’s like soldiers complaining that wars involve people firing guns at each other, you know. That’s what it is and out of that process people, at times, are stressed by it. (Clinical Director, H)

4.10.14 Aggression and violence

Knowing this, however, may do little to mitigate distress of being assaulted. The volatility of acute wards and the risk of violence were major concerns for staff, and several said they had either been the target of or witnessed a serious assault. A common vein running through discussions was that in cases of assault, “there’s no reparation really that can be made” (Ward Manager, H). Both staff and patients described how one or two individuals could shift the whole atmosphere of the ward. Several recalled particularly difficult periods.

I think the worst time we had here was some time last year when we had a sort of gang mentality on the ward - like them and us, and that was pretty frightening sometimes really. I mean they were practically trying to sort of take over. (Staff nurse, L)

As mentioned earlier, the potential for violence was a source of anxiety for staff and patients, and the unpredictability element could engender a sense of dread:

You don’t know who is going to kick off next and you don’t know what’s going to happen. You don’t know if the patient’s going to throw things, or it’s just one of those ones where sometimes you do feel quite safe. (Patient, PICU)

As much as you’d like to think you’re safe, but you don’t know.. What compounds your anxieties, is something that has happened before, you know. ‘Cos I’ve had someone grab me from the back, put me in headlock, throw me back and subsequently sprained my neck.(Staff nurse, L)

With the potential for violence often accepted as a given, especially by staff in acute and PICU wards, risk management became an important issue. Again, responses revealed disparities between the wards. Across all wards, at least some mention was made of the


183

need for improvements in safety provision. Most frequently and urgently stressed was the need for more staff. On four of the wards, concerns were also raised about aspects of the physical environment, including locks and alarms.

I’d like them to walk in on just a normal day – even the senior management, to just walk in unannounced and then ask us ‘what’s going on there?’ but the thing is that as soon as they do that, it would be ‘Well, you’re in charge so it’s your fault’ and not ‘I haven’t got the resources to do this.’ We haven’t got enough staff, we haven’t got enough time and we haven’t got enough pagers and alarms to do it safely. That’s the trouble. (Staff nurse, L).

There were also differences between the wards in terms of the availability of support during and after adverse incidents. Feeling confident about relying on one’s colleagues in a risk situation was important for staff, as was retrospective emotional support. One nurse, working in intensive care, described how adverse incidents, when managed effectively, actually had the potential to enhance team morale:

No incident is nice, but if we deal with it correctly and no one gets off really hurt or whatever and all the procedures are done, it’s a good feeling. I think it’s good because that shows we’ve got team work. (Staff Nurse, L).

Support from managers was sometimes felt to be lacking; again, particularly for those on acute or PICU (low-morale) wards. In these cases it was generally “acknowledgement” and basic emotional support that staff wanted. One group talked about how they used to receive letters following an incident, which had since ceased. Although it was “the exact same letter” every time, said one person, “at least you felt they were thinking of you”.

4.10.15 Dealing with wider social problems

One issue raised voluntarily by a small number of participants was having to deal with patients’ wider social problems, including housing, finances and drug/alcohol use. Attitudes towards this aspect of work varied and depended to a large extent on staffs’ perceptions of their roles. For those working in Recovery, facilitating social rehabilitation was seen as one of the most rewarding aspects of the job:

Here it’s seeing people moving on and getting their own independence and living in their own flats and then being a part of that really. (Staff nurse, H)

In contrast, staff on one acute ward in particular voiced their frustration at having to deal with these issues. Some described feeling ill-equipped and under-resourced to properly address non mental health related needs. Adding to their frustration was the thought that some patients are not really unwell and deliberately “use the system”, or get “stuck” in the service due to housing issues. One or two participants commented that patients with problems relating to housing or alcoholism prevented “genuine patients” from accessing a bed.


184

4.10.16 Conversation and activities

The opportunity for casual and, indeed, meaningful conversation was presented as a rare gift; both patients and staff felt it ought to be more commonplace. Staffing levels and administrative duties were the major impediments. The level of paperwork required on shift was seen as excessive and burdensome.

It’s kind of like a circle isn’t it? You can’t spend enough time with them and you’re stressed out, and then that makes it even more stressful, because they’re telling you you’re basically not doing your job properly, because you’re not spending time with us as much as you should. (Healthcare assistant, H)

You’ve got to have backup notes and you spend your entire shift getting people to sign things and giving bits of paper to people. They don’t want it. They’re just shoving it on the floor in the drawer. You know, they want someone to talk to. They don’t want bits of paper. (Staff nurse, L)

Patients generally confirmed this impression. More than one said that if staff spent more time talking to patients there would be fewer violent incidents. One acute ward patient described the atmosphere as being one of “crisis management”. Another repeatedly stressed that staff ought to be trained in counselling skills.

Unsurprisingly, this was much less of an issue for staff in the Recovery and Rehabilitation wards, where dedicated patient time was built in to the structure of work. The point made by staff and patients in acute wards however was that contact time was no less important here than in more long-stay units. Staff who could find time for patients seemed to place more value in their roles. Work life was clearly enriched by the inclusion of a therapeutic element:

That’s the best thing for me, to see them just getting that tiny little boost of self esteem here and there from all of the sorts of things, the day to day things that we’re doing – that’s why I do it. (OT, H)

Looking more broadly at social interactions, staff and patients both said they enjoyed jointly engaging in social and recreational activities. Predictably, those working in Recovery and Child & Adolescent units had more scope for this than those in acute wards. Joint activities could be a way of building unique relationships with patients:

I was doing the cooking, and there wasn’t too much of healthy eating at that time, and then I remember one of the patients said to me, ‘ Today I felt like I’m a human being’. I said,’ Why are you saying that?’ She said,’ You know the food that you gave me, it made me feel good, like I’m still alive.’ (Healthcare Assistant, H).

4.10.17 Helping patients recover

Across all the wards, seeing patients get better was a positive influence on morale. This experience was more forthcoming for some than others. Those working in Recovery and Child & Adolescent units gained fulfillment from a long-term emotional investment in clients. For those in acute/intensive care however, success was rated on a more short-term basis in terms of “stabilising” patients and


185

discharging them home. Staff on these wards who maintained more consistent positive morale embraced the “challenge” of acute psychiatric care.

Acute care staff were also more likely to see patients return to the ward. For a few, particularly on one acute ward, the “revolving door” phenomenon was a cause of frustration. Some felt disillusioned at the way factors beyond their control contributed to repeated readmission. Others also blamed patients themselves for being “manipulative” in gaining inappropriate access to a bed.

4.10.18 Physical Environment

Participants were asked directly about the physical environment of the ward, which often elicited substantial discussion. Each ward had its own unique “problems”, and experiences were highly mixed. Some staff complained about cramped offices and limited access to computers. One ward, on the other hand was repeatedly said to be too large, with staff feeling pushed to ensure adequate cover. Poor air quality and lighting were both raised as problems on two wards, with inadequate ventilation making life particularly stressful. Contrastingly, two buildings were praised for their lightness. Particularly demoralizing were enduring problems, which could lead staff to feel neglected. Improvements, on the other hand, were highly morale-enhancing. One of the wards had recently moved to a new, purpose-built building from temporary accommodation:

Placing you in nice surroundings makes you feel valued and appreciated and I think… it ups the ante. They’re just saying, yeah, we are doing a fine job, we’ve got a fine building here, it looks great for people visiting, it looks reassuring for families, so lot’s and lots of positives about it. (Social Worker, H)

However, with regards to their previous environment, more than one person mentioned the “joining” effect of “making do”. There was an interesting disparity here in the way physical problems were tolerated. Clearly, it was easy for poor conditions to exert a negative effect:

Money speaks more than anything, more than any care at all for our welfare. And that’s with everything with the atmosphere, with the ward, with the patient care, our safety, physical safety as well as the carcinogenic fumes that we’re having to breathe in all the time. (OT Assistant, L)

Where there was a certain robustness of spirit however, poor working conditions could be tolerated and even embraced:

It almost had a really good effect on us, because we got into this kind of, ‘we’re going to soldier on and be in this falling-down building’ Do you know what I mean? And we had had, like, water coming through the lights, and we didn’t care, and it was kind of like... In a way it was like we didn’t... a lot of us didn’t actually want to move and were a bit scared to come here. (Healthcare Assistant, H).

Facilities for staff also varied, with a few people commenting that there was nowhere they could comfortably relax. In one ward, two members of staff reported sitting in their cars for breaks. A more


186

prominent theme of discussion though was the availability of resources for patients.

Outdoor space, in particular, was highly valued; one manager commented that the addition of a garden reduced the number of violent incidents. A few people said that lack of designated spaces restricted the scope for group activities or one-to-one sessions. A comfortable and attractive environment was positively conducive to the atmosphere on the ward.

Generally, as an environment …..the staff love it and really sort of value the space they have and the atmosphere, because it’s a very relaxed atmosphere round here. (Ward Manager, H).

The physical environment was therefore implicated in staff members’ conceptions of the service they were able to provide. An important contributor to good morale was the feeling of being proud of that service.

4.11 Module 4 – Leavers’ Survey


The movement of staff from inpatient wards to community services is common. Nearly 70% of surveyed staff from community teams had previous experience of working in inpatient settings.

39% of the small sample in the Leavers’ Survey left the NHS altogether.

Factors relating to control were most commonly reported as reasons for leaving inpatient wards. Over three quarters of respondents from community teams reported leaving ward work in order to gain more control over how they work; 70% of Leavers’ Survey respondents identified wanting more control of how they worked as an important reason for leaving their inpatient job

Factors relating to the demands of inpatient work were less commonly reported as reasons for leaving than control factors but were not negligible. Large minorities of respondents identified stress at work and the prevalence of violence and drug use on wards as important reasons for leaving.

4.11.2 Response rates

Data regarding people leaving inpatient wards were collected from two sources

(a) a Leavers’ Questionnaire distributed to people who had participated in the survey and subsequently left the wards they had worked on.


187

(b) questions within the Module 2 survey for CRT and CMHT staff, asking whether they had ever worked on an inpatient ward and, if so, why they had left.

Data collection for the Module 4 Leavers’ survey was incomplete due to resource limitations within the study team and reluctance among managers and Human Resources Departments in some participating Trusts to contact leavers about the study. The data collection was carried out in 2009.

In total, 145 Leavers’ Questionnaires were distributed to staff from 20 wards in 5 NHS Trusts. 37 completed questionnaires were returned, a response rate of 25.5%.

Data about reasons for leaving inpatient wards were collected from staff in Community Mental Health Teams and Crisis Resolution Teams participating in Module 2 of this study. 467 out of 474 community staff who completed community staff questionnaires for Module 2 provided information about previous experience of working in inpatient wards and reasons for leaving.

4.11.3 Reasons for leaving inpatient wards: staff currently working in community teams

Responses from staff working in community teams from Module 2 questionnaires concerning previous experience of working in inpatient wards and reasons for leaving are presented in Table 48. Staff were asked to rate the importance to them of each of the list of potential reasons below: most endorsed multiple reasons as important.

Table 48. Reasons for leaving inpatient wards - CMHT and CRT staff

CMHT Staff

n=258

CRT Staff

n=216

Total

n=474

Ever worked on an inpatient ward (n=467) 165 (64.2%)

161 (76.7%) 326 (69.8%)

Left less than 2 years ago (n=70) 31 (20.4%) 39 (24.8%) 70 (22.7%)

Left 2-5 years ago (n=100) 27 (17.8%) 73 (46.5%) 100 (32.4%)

Left more than 5 years ago (n=139) 94 (61.8%) 45 (28.7%) 139 (45.0%)

Were the following very or fairly important reasons for leaving ward work?

The shifts were too inconvenient (n=285) 54 (39.7%) 29 (19.5%) 83 (29.1%)

Ward work was too stressful (n=285) 40 (29.2%) 54 (36.5%) 94


188

(33.0%)

Ward work was not satisfying enough (n=289)

59 (42.8%) 82 (54.3%) 141 (48.8%)

There were not enough opportunities for career development (n=289)

78 (55.7%) 97 (65.1%) 175 (60.6%)

There was too much violence on wards (n=288)

38 (27.5%) 56 (37.3%) 94 (32.6%)

There was too much drug use on wards (n=287)

30 (21.7%) 41 (27.5%) 71 (24.7%)

Wanted to work in a community setting (n=292)

110 (76.9%)

119 (79.9%) 229 (78.4%)

Wanted to get a promotion (n=289) 75 (53.6%) 83 (55.7%) 158 (54.7%)

Wanted more control over the way I work (n=291)

109 (76.8%)

115 (77.1%) 224 (77.0%)

Table 48 shows that nearly 70% of community staff respondents had previously worked in inpatient wards. For the majority of CMHT staff this was more than 5 years ago, but CRT staff tended to have left inpatient wards more recently. The most commonly reported important reasons for leaving ward work were a wish to work in community settings and to have more control over how they worked. These were both identified as fairly or very important by more than three quarters of respondents. Seeking more opportunities for career development and wanting a promotion were also reported as important by a majority of respondents. Escaping stress, violence and drug use also featured among reasons, but tended to be less important than seeking greater satisfaction, control and opportunities for career development.

4.11.4 Reasons for leaving: inpatient wards leavers’ survey

The characteristics of respondents to the Leavers’ Survey, their sickness over the last year and reported recent morale are described in Table 49.

Table 49. Characteristics of Leavers’ Survey respondents (n=37)

Characteristic

Age (n=36) Mean age in years (s.d.)

43.9 (9.8)

Gender (n=36) Male 7 (19%) White ethnic groups 29 (83%) Black ethnic groups 2 (6%) Ethnicity (n=35) Asian ethnic groups 4 (11%)

Occupational Mental health nurse 21 (58%)


189

Nursing Assistant 5 (14%) Psychologist 4 (11%) Psychiatrist 1 (3%)

group (n=36)

Other occupation 5 (14%) Other inpatient unit 6 (17%) Crisis Team (CRT) 1 (3%) Other community team 9 (25%) Other NHS role 6 (17%) Private healthcare 4 (11%)

Current employment (n=36)

Other 10 (28%) Mean days (s.d.) 21.7 (50.0) Days off sick in

past year (n=30) Median days 3.5 Very poor 1 (3%) Poor 6 (20%) Neither poor nor good 9 (30%) Good 7 (23%)

Reported morale at work in recent weeks (n=30)

Very good 7 (23%)

The reported reasons for leaving their job in an inpatient ward of respondents to the Leavers’ Survey are reported in Table 50.

Table 50 shows that most respondents reported multiple reasons for leaving an inpatient job. Reasons relating to the convenience of work (shift work and working near home) or its financial rewards were rated as important by fewer respondents than reasons relating to the nature of their experience while at work. In this sample of very recent leavers, control is still prominent, but reasons related to stress and difficult experiences on wards are more prominent than in the community sample, many of whom had left rather longer before.

Table 50. Reasons for leaving: inpatient staff Leavers’ Survey

Were the following reported as very or fairly important reasons for leaving previous inpatient job?

The shifts were too inconvenient (n=29) 9 (31%)

Ward work was too stressful (n=30) 17 (67%)

Ward work was not satisfying enough (n=31) 14 (45%)

Not enough opportunities for career development

(n=30)

15 (50%)

There was too much violence on wards(n=31) 14 (45%)

There was too much drug use on wards(n=32) 15 (47%)

To get experience in a different inpatient setting (n=31) 15 (48%)

Wanted to work in a community setting(n=30) 13 (43%)

Wanted to get a promotion(n=31) 14 (45%)

Wanted more control over the way I work (n=30) 21 (70%)

Relationships with colleagues (n=31) 10 (32%)


190

To get a job nearer home (n=30) 4 (13%)

To improve health and wellbeing (n=32) 21 (66%)

To improve pay (n=31) 9 (29%)

To improve working conditions (n=31) 22 (71%)

The lack of time to work with patients (n=31) 20 (65%)

The work with patients was not satisfying (n=30) 13 (43%)

To work in a more pleasant physical environment

(n=30)

17 (67%)

The atmosphere of the Trust or hospital (n=31) 18 (58%)

The management of the Trust or hospital (n=30) 19 (63%)

How the ward was led and managed (n=31) 21 (68%)

The way the ward team worked together (n=30) 17 (67%)

4.12 Module 5 – The persistence of high and low morale

Summary points

75-80% of staff remained in the same burnout or GHQ stressed case category at 1 year follow up and changes in contentment and enthusiasm over one year were typically modest: overall, morale amongst inpatient staff is only moderately labile.

However, change in morale was more common among the sub-group of staff who were burnt out or stressed cases at baseline: over half no longer were at follow up.

Individual staff data suggest emotional exhaustion among ward staff might be slightly less stable than cynicism or personal accomplishment.

A majority of high burnout status wards (Mean Maslach Burnout Inventory Scores) at baseline were no longer high burnout at follow-up.

There were greater increases in morale across indicators in one region than the other participating in this module.

One year on from the Module 1 survey, follow-up questionnaires were distributed to staff in 20 wards from London and Midlands regions which had participated in the Module 1 survey. These wards were selected as having a range of initial levels of morale. They included the 10 wards which participated in the qualitative study (Module 3). 483 Module 5 questionnaires were distributed to ward staff. 331 questionnaires were collected, a response rate of 68.5%. These included 106 individually identifiable questionnaires from staff


191

who had participated in the Module 1 survey. Data collection took place in 2008 and 2009.

First, results regarding the persistence of morale at individual staff level will be reported in Section 4.5.1, using data from the 106 identifiable respondents who completed questionnaires at both time points. Second, ward level results will be presented in Section 4.5.2, comparing all data from wards which were surveyed at both timepoints.

4.11.5 Persistence of morale – individual staff level results

Table 51 shows individual staff scores at baseline and follow-up for the Maslach Burnout Inventory domains of emotional exhaustion, cynicism and personal accomplishment. Standardised scores (from 1-5) are also provided to allow direct comparison of the magnitude of change in different domains.


192

Table 51. Maslach Burnout Inventory change scores over 1 year follow up – individual staff data

Maslach Burnout Inventory domain

Baseline

One year follow-up

Change score (from standardised data, scale 1-5)

Mean (s.d.) Mean for standardised score (standardised to 1to 5 (s.d.)

Mean (s.d.) Standardised mean (s.d.)

Mean (s.d.)

Emotional Exhaustion*

(n=106)

18.58

(11.11)

2.38

(0.82)

16.65

(10.83)

2.23

(0.80)

0.14

(0.84)

positive change score means reduced burnout

Cynicism*

(n=106)

4.85

(5.19)

1.65

(0.69)

4.05

(4.37)

1.54

(0.58)

0.11

(0.59)

positive change score means reduced burnout

Personal accomplishment**

(n=103)

34.59

(9.38)

3.88

(0.79)

36.63

(7.81)

4.05

(0.65)

-0.17

(0.68)

positive change score means increased burnout


193

Table 51 shows modest reduction in mean burnout scores at follow-up for all three measured domains. Alteration in change scores was greatest for the personal accomplishment domain. In relation to overall variance, change was relatively modest, representing less than a quarter of a standard deviation for each of the types of burnout.

Table 52 reports the proportion of respondents meeting criteria in each domain for high level of burnout at each time point. (Criteria were reported in Section 4.1: high burnout is defined as a score of 21 or above on the emotional exhaustion scale; 8 or above on the cynicism scale of 28 or below on the personal accomplishment scale.) Again the table relates to the 106 identifiable respondents at both time points.

Table 52. Stability of burnout scores over 1 year follow-up – individual staff data

Maslach Burnout Inventory domain

Burnout status at baseline

Status unchanged at 1 year follow-up

Status changed at 1 year follow-up

High burnout

38 (36%) 22 (58%) 16 (42%)

Low/medium Burnout

68 (64%) 55 (81%) 13 (19%) Emotional exhaustion

Total

106 77 (73%) 29 (27%)

High burnout

18 (17%) 11 (61%) 7 (39%)

Low/medium burnout

88 (83%) 79 (90%) 9 (10%) Cynicism

Total

106 90 (85%) 16 (15%)

High burnout

23 (22%) 10 (43%) 13 (57%)

Low/medium burnout

80 (78%) 73 (91%) 7 (9%)

Personal accomplishment

Total 103 83 (81%) 20 (19%)


194

Table 52 shows that burnout status, measured by the Maslach domains, changed for about one fifth to quarter of respondents (19-27%) at one year follow-up. Burnout status changed for more respondents in the emotional exhaustion domain than for cynicism or personal accomplishment. A markedly greater proportion of staff who were burnt out at baseline changed status at follow-up, compared to staff who were not burnt out at baseline. Between 39% and 57% of burnt out staff at baseline in the different domains were no longer burnt out at one year follow-up.

Table 53 shows changes in individual staff scores on the anxiety-contentment and depression-enthusiasm scales between baseline and one year follow-up.

Table 53. Change in Warr scale scores over 1 year follow-up –

individual staff data

Warr scale Baseline

mean (s.d.)

One year follow-up

mean (s.d.)

Change score*

mean (s.d.)

Anxiety-contentment

(n=94)

(high score = high contentment)

3.26

(0.68)

3.33

(0.68)

-0.07

(0.61)

Depression-enthusiasm (n=92)

(high score = high enthusiasm)

3.52

(0.72)

3.62

(0.71)

-0.10

(0.65)

*positive change score means reduced contentment or enthusiasm

Table 53 shows a small increase in mean contentment and enthusiasm at follow-up. In both measured scales however, the magnitude of the change score is less than 0.2 of a standard deviation, indicating no effect (Cohen 1988).

Table 54 reports the proportion of those staff who provided baseline and follow-up data who met General Health Questionnaire (GHQ) criteria for being a stressed case at baseline. The stability of GHQ stressed case-status at follow-up is shown.


195

Table 54. Ward staff meeting General Health Questionnaire stressed case criteria: stability over one year follow-up

GHQ stressed case status at baseline Status unchanged at 1 year follow-up

Status changed at 1 year follow-up

Stressed case

13 (14%) 5 (38%) 8 (62%)

Not a stressed case

77 (86%) 69 (90%) 8 (10%)

Total

90 74 (82%) 16 (18%)

The same number of staff (13) met GHQ stressed case criteria at both timepoints, but the majority of those meeting these criteria at baseline no longer did at follow up.

4.11.6 Persistence of morale – ward level results

In this section, scores at baseline and one year follow-up will be presented for each of the 20 wards which participated in Module 5. Large tables reporting change scores in morale indicators for individual wards are provided in Appendix 1 (Tables A7–A10), and summarised in this chapter. Wards are not identified by name, but are numbered 1–20 in each Table and their ward type and area are reported.

Emotional exhaustion: Mean emotional exhaustion scores fell for 4 out of 10 wards in the Midlands area and 8 out of 10 in London from baseline to follow-up. Ward burnout status improved for 6 wards, deteriorated for one and remained the same for 13 wards. Of 8 wards which were high burnout status at baseline, only three remained high burnout at follow-up. Raw and standardised scores for each ward for the Maslach Burnout inventory emotional exhaustion domain are provided in Appendix 1. Categorisation of wards as high, medium or low burnout status at each time point was based on the criteria described in Section 4.1.

Table 55 reports the number and proportion of respondents from the 20 wards which participated in Module 5 who met the high burnout threshold for the emotional exhaustion domain at baseline and follow-up.


196

Table 55. Proportion of staff with high burnout scores on MBI emotional exhaustion scale – ward level data

High burn out staff n(%) Area Ward ID

Ward type

Baseline 1 year follow-up

% change in burnt out staff*

1 Acute 2 (29%) 7 (43%) +14%

2 Acute 6 (43%) 8 (43%) 0%

3 PICU 6 (67%) 12 (50%) -17%

4 Acute 7 (41%) 6 (38%) -3%

5 Acute 6 (33%) 5 (33%) 0%

6 Acute 4 (44%) 9 (53%) +9%

7 Acute 11 (52%) 6 (67%) +15%

8 Acute 10 (63%) 10 (71%) +8%

9 Older adult 7 (50%) 9 (45%) -5%

Mid

lands

10 Rehab 3 (30%) 4 (31%) +1%

11 Acute 8 (62%) 5 (33%) -29%

12 Acute 7 (64%) 2 (33%) -31%

13 Acute 8 (80%) 1 (7%) -73%

14 Acute 6 (55%) 3 (25%) -30%

15 CAMHS 7 (29%) 4 (19%) -10%

16 Forensic 4 (22%) 5 (25%) +3%

17 Acute 16 (59%) 7 (35%) -24%

18 Forensic 9 (31%) 6 (30%) -1%

19 Rehab 3 (25%) 1 (17%) -8%

London

20 PICU 5 (24%) 8 (50%) +26%

All 135 (43%) 118 (38%) -5%

* positive change score = increase in proportion of burnt out staff at follow-up

Table 55 results indicate that the proportion of high emotionally exhausted staff decreased at follow up for 11 wards out of the 20 surveyed - 3 out of 10 Midlands wards and 8 out of 10 London wards. The proportion of high burnout staff changed by less than 10% in 10 wards, between 10-20% in 5 wards and more than 20% in 5 wards.

Cynicism: Ward mean cynicism scores fell for 14 wards between baseline and follow-up – 6 out of ten in the Midlands region and 8 out of 10 in


197

London. Burnout status at follow-up was improved for 8 wards, had deteriorated for one ward and was unchanged for 11 wards. None of the three high burnout status wards at baseline remained high burnout status at follow-up. Raw and standardised scores for each ward for the Maslach Burnout inventory cynicism domain are presented in Appendix 12.

Table 56 reports the number and proportion of respondents from the 20 wards which participated in Module 5 who met the high burnout threshold for the cynicism domain at baseline and follow-up.

Table 56. Proportion of staff with high burnout scores on MBI cynicism scale

– ward level data

High burn out staff n(%) Area Ward ID Ward type Baseline 1 year follow-up % change in burnt out staff*

1 Acute 1 (14%) 1 (6%) -8% 2 Acute 2 (14%) 8 (40%) +26% 3 PICU 3 (33%) 9 (38%) +5% 4 Acute 4 (24%) 3 (19%) -5% 5 Acute 6 (33%) 2 (13%) -20% 6 Acute 3 (33%) 5 (29%) -4% 7 Acute 4 (20%) 3 (33%) +13% 8 Acute 6 (38%) 9 (64%) +26% 9 Older adult 3 (21%) 6 (30%) +9%

Mid

lands

10 Rehab 0 (0%) 2 (15%) +15% 11 Acute 4 (31%) 6 (40%) +9% 12 Acute 3 (27%) 0 (0%) -27% 13 Acute 3 (27%) 2 (14%) -14% 14 Acute 4 (36%) 1 (8%) -28% 15 CAMHS 2 (8%) 4 (19%) +11% 16 Forensic 1 (6%) 0 (0%) -6% 17 Acute 11 (41%) 7 (35%) -6% 18 Forensic 6 (21%) 3 (15%) -6% 19 Rehab 1 (8%) 1 (17%) +9%

London

20 PICU 10 (48%) 3 (19%) -29% All 77 (25%) 75 (24%) -1%

* positive score = increase in proportion of burnt out staff at follow-up

As previously noted, there was relatively little variability in the sample as a whole on this component of burnout, with few respondents scoring in the high range. Table 56 results indicate that the proportion of burnt out staff on the cynicism scale decreased at follow up for 11 wards out of the 20 surveyed - 4 out of 10 Midlands wards and 7 out of 10 London wards. The proportion of high burnout staff changed by less than 10% in 10 wards, between 10-20% in 4 wards and more than 20% in 6 wards.

Burnout – personal accomplishment: Even more than with cynicism, scores in the high burnout range were unusual for this component of burnout. Mean personal accomplishment scores increased between baseline and follow-up on 16 wards – 7 out of 10 in the Midlands region and 9 out of


198

10 in London. Ward burnout status improved by follow-up in 6 wards, deteriorated in one ward and stayed the same in 13 wards. No wards were high burnout status at baseline or follow-up. Of 10 wards which were medium burnout status at baseline, 6 improved status at baseline and 4 were unchanged in status. Mean scores at each time point for the 20 wards for the personal accomplishment dimension of burnout at baseline and at follow up are presented in Appendix 12.

Table 57 reports the number and proportion of respondents from the 20 wards which participated in Module 5 who met the high burnout threshold for the personal accomplishment domain at baseline and follow-up.

Table 57. Proportion of staff with high burnout scores on MBI Personal

Accomplishment scale – ward level data

High burn out staff n(%) Area Ward ID

Ward type Baseline 1 year follow-

up % change in burnt out staff*

1 Acute 0 (0%) 2 (13%) +13% 2 Acute 1 (7%) 5 (28%) +21% 3 PICU 2 (22%) 3 (13%) -9% 4 Acute 7 (41%) 2 (14%) -27% 5 Acute 5 (28%) 2 (13%) -15% 6 Acute 3 (33%) 5 (29%) -4% 7 Acute 6 (29%) 1 (11%) -18% 8 Acute 4 (25%) 3 (21%) -4% 9 Older adult 6 (43%) 7 (35%) -8%

Mid

lands

10 Rehab 0 (0%) 2 (17%) +17% 11 Acute 3 (23%) 3 (20%) -3% 12 Acute 5 (46%) 2 (33%) -13% 13 Acute 1 (10%) 0 (0%) -10% 14 Acute 1 (9%) 1 (8%) -1% 15 CAMHS 3 (13%) 1 (5%) -8% 16 Forensic 3 (17%) 1 (5%) -12% 17 Acute 11 (41%) 3 (16%) -25% 18 Forensic 11 (39%) 2 (11%) -28% 19 Rehab 6 (50%) 1 (17%) -33%

London

20 PICU 8 (38%) 3 (19%) -19% All 86 (28%) 49 (16%) -12%

* positive score = increase in proportion of burnt out staff at follow-up

Table 57 indicates that the proportion of burnt out staff indicated by personal accomplishment scale scores fell in 17 wards between baseline and follow-up – 7 out of 10 from the Midlands region and 10 out of 10 from London. The proportion of high burnout staff changed by less than 10% in 7 wards, between 10-20% in 8 wards and more than 20% in 5 wards.

Stressed cases – General Health Questionnaire: Table 58 shows the proportion of staff on each ward meeting General Health Questionnaire for being a stressed case at baseline and one year follow-up.


199

Table 58. Staff meeting GHQ stressed case criteria: ward scores over one

year follow-up

Area Ward ID Ward type Stressed cases - baseline n/N (%)

Stressed cases – follow up n/N (%)

Change in proportion of stressed cases*

1 Acute 1/7 (14.3%) 0/14 (0%) -14.3% 2 Acute 2/13 (15.4%) 5/18 (27.8%) 12.4% 3 PICU 3/8 (37.5%) 7/24 (29.2%) -8.3% 4 Acute 4/17(23.5%) 3/14 (21.4%) -2.1% 5 Acute 1/17 (5.9%) 1/16(6.3%) 0.4% 6 Acute 4/9 (44.4%) 4/17 (23.5%) -20.9% 7 Acute 5/21 (23.8%) 1/8 (12.5%) -11.3% 8 Acute 6/16 (37.5%) 10/15 (66.7%) 29.2% 9 Older adult 2/13 (15.4%) 3/19 (15.8%) 0.4%

Mid

lands

10 Rehab 2/10 (20.0%) 2/13 (15.4%) -4.6% 11 Acute 4/12 (33.3%) 3/14 (21.4%) -11.9% 12 Acute 1/10 (10.0%) 1/5 (20.0%) 10% 13 Acute 4/11 (36.4%) 0/13 (0.0%) -36.4% 14 Acute 2/10 (20%) no respondents n/a 15 CAMHS 1/23 (4.3%) 5/21 (23.8%) 19.5% 16 Forensic 0/15 (0.0%) 1/20 (5.0%) 5% 17 Acute 9/24 (37.5%) 4/20 (20%) -17.5% 18 Forensic 5/27 (18.5%) 2/20 (10.0%) -8.5% 19 Rehab 2/11 (18.2%) 1/6 (16.7%) -1.5%

London

20 PICU 6/21 (28.6%) 3/16 (18.8%) -9.8% All 69/295 (23.4%) 56/293 (19.1%) -4.3%

*positive score = higher proportion of stressed cases at follow-up

Table 58 shows General Health Questionnaire data were collected at both time points from 19 wards. overall, the proportion of stressed cases in participating wards fell slightly. The proportion of stressed cases indicated in the data rose in seven wards – 4 out of 10 in the Midlands region and 7 out of 10 in London. The proportion of stressed cases changed by less than 10% in 10 wards, between 10-20% in 7 wards and more than 20% in 3 wards.

Job-related well-being: Ward mean scores at baseline and follow-up for the anxiety-contentment scale and the depression-enthusiasm scale are presented in Appendix 1. Ward scores for contentment rose in 14 wards and for enthusiasm in 16 wards – 4 wards and 6 wards respectively out of 10 in the Midlands and all 10 on both scales in London. Both scales show a modest increase in overall mean scores at follow-up and a similar amount of variance in the data between scales and at different time points. For both variables, 5 wards out of 20 showed change scores greater than half a standard deviation of baseline or follow-up data, suggesting a medium effect (Cohen 1988) for the magnitude of improvement.


200

4.12 Module 6 – Ward sickness and turnover rates and their relationship to morale


For individual staff, days off sick in the preceding year and number of periods of absence were significantly correlated with all morale indicators, but correlations were small in size.

Significant correlations between sickness levels and morale indicators were seldom replicated at ward level, probably primarily because of issues of power. However, at one year follow up, sickness absence rates were positively associated with Emotional Exhaustion. The proportion of leavers over the preceding year was positively associated with Personal Accomplishment.

Three sets of data were available to allow assessment of the relationship between sickness rates, turnover and indicators of staff morale. These were (a) the individual level data from Modules 1 and 2, from which the association between self-reported retrospective sickness rates and staff well-being and burnout could be examined; (b) ward level data for most participating services for sickness and turnover in the previous year, allowing examination of the relationship at ward level; (c) prospective data from a limited number of wards allowing examination of the extent to which morale at the time of the Module 1 survey predicted sickness and turnover over the following year. For (c) an incomplete data set was obtained because of a combination of the difficulty we experienced in obtaining these data from wards and Trusts human resource department and time difficulties at the end of the study.

Individual-level data: Individual-level data on sickness absence were collected at baseline as part of the Module 1 survey. Ward and team staff were asked how many days off sick they had had in the past year and over how many separate episodes of illness. Table 59 reports descriptive statistics for self-reported sickness absence. Tables 60 and 61 report the same data analysed by ward/team type.

Table 59. Self-reported sickness absence: full sample

N Mean Median Standard deviation

Range

How many days off sick have you had in the past year?

1942 10.10 4 21.34 0 - 200

Over how many separate episodes of illness?

1711 1.86 2 1.63 0 - 15

Table 60. Self-reported sickness absence: wards and community teams


201

Ward or team type

N Mean* Median* Standard Deviation*

Range*

Ward Staff 1486

1312

10.30

1.86

4.00

2.00

21.80

1.64

0-200

0-15

CMHT Staff 253

217

9.28

1.88

3.00

2.00

20.87

1.75

0-182

0-14

Crisis Team Staff 203

182

9.73

1.83

4.00

2.00

18.39

1.46

0-120

0-8

*(top = number of days off sick in the past year; bottom = number of separate periods of illness)

Table 61. Self-reported sickness absence: ward type

Ward/team type

N* Mean* Median* Standard Deviation*

Range*

Acute 686

600

11.30

1.96

4.00

2.00

24.03

1.79

0-200

0-15

PICU 137

123

7.39

1.85

4.00

2.00

13.41

1.85

0-90

0-13

CAMHS 185

163

7.00

1.63

3.00

2.00

16.34

1.28

0-180

0-6

Forensic 209

190

11.43

1.85

5.00

2.00

22.86

1.37

0-180

0-7

Rehabilitation 131

112

9.09

1.89

3.00

2.00

21.68

1.50

0-180

0-7

Older Adult 138

124

12.01

1.65

4.00

1.50

21.29

1.54

0-120

0-9

*top = number of days off sick in the past year; bottom = number of separate periods of illness

The relationship between self-reported sickness absence and morale was explored using correlational analysis. Table 62 shows correlations between individual-level sickness absence and scores on five key measures of morale identified: the Emotional Exhaustion and Personal Accomplishment subscales of the Maslach Burnout Inventory, the Warr Anxiety/Contentment and Depression/Enthusiasm scales and Intrinsic Job Satisfaction.


202

Table 62. Correlations between self-reported sickness absence and measures of morale

How many days off sick have you had in the past year?

Over how many separate episodes of illness?

Pearson Correlation

-.110(**) -.145(**)

Sig. (2-tailed) .000 .000

Anxiety-contentment (high score – high contentment)

N 1867 1651

Pearson Correlation

-.150(**) -.176(**)

Sig. (2-tailed) .000 .000


N 1869 1654

Pearson Correlation

-.154(**) -.138(**)

Sig. (2-tailed) .000 .000

Intrinsic Job Satisfaction

N 1916 1691

Pearson Correlation

.132(**) .136(**)

Sig. (2-tailed) .000 .000

MBI Emotional exhaustion

N 1923 1700

Pearson Correlation

-.095(**) -.056(*)

Sig. (2-tailed) .000 .022

MBI Personal accomplishment

N 1904 1684

* Correlation is significant at the 0.05 level

** Correlation is significant at the 0.01 level (2-tailed).

Table 62 results indicate significant correlations between all components of the Maslach burnout inventory and sickness absence, with all correlations going in the expected direction of poorer well-being and satisfaction being correlated with more sickness absence. However, the size of correlations is modest throughout.


203

4.12.2 Ward-level sickness and turnover

Data on ward-level sickness and turnover levels were sought at two time points: at baseline and at one year follow up. Baseline data was collected from ward and team managers as part of the initial survey. Managers were asked to provide information on sickness and turnover rates over the previous year. Follow up data was acquired by approaching ward managers and Trust Human Resources/Workforce Information departments at least one year after the initial survey. Retrospective information was requested for sickness and turnover rates for the 12 months immediately after the initial survey. CMHTs and Crisis Teams were not included in the follow-up stage.

At baseline, information was gathered on the number of full time equivalent (FTE) staff employed at the time of data collection and 12 months previously, the number of staff members leaving the team during this period, the percentage of work days lost to sickness absence, and the current number of vacant posts in the team. At one year follow up, the same information was gathered for the 12 months following the initial survey.

Data were gathered from all participating Trusts at baseline, and from all n the London, Warwick and Sheffield regions at follow up. Resource limitations at follow up prevented us from acquiring data from the Bristol region. Some data, not always complete, were obtained for 64 out of a possible total of 79 wards. Where data was not obtained this was largely due to its being unavailable or inaccessible, or because of structural changes to the ward in question since the initial survey.

Table 63 shows descriptive statistics for data on sickness and turnover at the two time-points. Proportion of leavers was calculated by dividing the number of staff leaving over the 12 month period by the total number of staff (in full time equivalents) in post at the start of this year. Proportion of vacant posts was calculated by dividing the number of vacant posts (in full time equivalents) at the end of the year in question by the sum of the number of vacant posts and the number employed in the team (i.e. giving a figure for the proportion of unfilled posts).


204

Table 63. Descriptive statistics for ward-level sickness and turnover rates at baseline and 1-year follow up.

N Mean Median Standard

deviation

Range

At baseline (modules 1 and 2)

Proportion of staff left

over past 12 months

(FTE)

126 0.11 0.10 0.09 0.00 – 0.60

Proportion of vacant posts

(FTE) 124 0.10 0.07 0.11 0.00 -0.43

% work days lost through

sickness absence in

preceding year

77 7.60 7.00 4.72 0.00 –

22.00

At follow up (module 6)

Proportion of staff left

over past 12 months

(FTE)

47 0.14 0.11 0.13 0.00 – 0.71

Proportion of vacant posts

(FTE) 41 0.17 0.15 0.13 0.00 - 0.51

% work days lost through

sickness absence 57 7.56 7.12 3.80

1.75 –

18.10

The relationship between ward-level sickness/turnover and morale was explored using correlational analysis. Tables 64 and 65 show correlations between measures of sickness and turnover and ward-level indices of morale at baseline and at one year follow up.


205

Table 64. Correlations between ward-level sickness/turnover and measures of morale: at baseline (retrospective data for turnover and sickness)

Proportion of staff who left in the past year

Proportion of vacant posts (in FTE)

% work days lost through sickness absence in past year

Pearson Correlation

-.222 - -.091 -.110

Sig. (2-tailed) .075 .469 .487

Anxiety - Contentment (high score - high cont)

N 65 65 42

Pearson Correlation

-.234 -.075 .058

Sig. (2-tailed) .060 .555 .715

Depression - Enthusiasm (high score - high enth)

N 65 65 42

Pearson Correlation

-.209 -.034 -.137

Sig. (2-tailed) .095 .791 .388


N 65 65 42

Pearson Correlation

.130 .148 .101

Sig. (2-tailed) .302 .241 .526


N 65 65 42

Pearson Correlation

-.202 .036 .024

Sig. (2-tailed) .106 .777 .882

MBI Personal Accomplishment score - high score means low burnout N 65 65 42

** Correlation is significant at the 0.01 level (2-tailed).


206

Table 65. Correlations between ward-level sickness/turnover and measures of morale: at one year follow up (prospective data for turnover and sickness)

Proportion of staff who left in the past year

Proportion of vacant posts (in FTE)

% work days lost through sickness absence in past year

Pearson Correlation

.054 -.162 -.138

Sig. (2-tailed) .721 .313 .307

Anxiety - Contentment (high score - high cont)

N 47 41 57

Pearson Correlation

-.058 -.173 -.095

Sig. (2-tailed) .697 .280 .480

Depression - Enthusiasm (high score - high enth)

N

47 41 57

Pearson Correlation

.099 .131 -.146

Sig. (2-tailed) .509 .415 .277


N 47 41 57

Pearson Correlation

.086 .188 .291*

Sig. (2-tailed) .564 .239 .028


N 47 41 57

Pearson Correlation

.356* .252 .149

Sig. (2-tailed) .014 .112 .270

MBI Personal Accomplishment score - high score means low burnout

N 47 41 57

* Correlation is significant at the 0.05 level (2-tailed).


207

Few of the ward-level correlations reached significance. However, they were in some cases larger in size than those reported above for the relationship at individual level between sickness and morale, but power for this ward level analysis was much less than for the individual level analysis. There were no significant correlations between retrospective measures of sickness/turnover and morale at baseline. At one year follow up, sickness absence rates were significantly positively correlated with Emotional Exhaustion (r(55) = 0.291, p = 0.05). In addition, the proportion of leavers over the preceding year was significantly positively correlated with Personal Accomplishment (r(45) = 0.356, p=0.014), suggesting a link between higher personal accomplishment and higher turnover.


208

5 Discussion

5.1 Strengths and Limitations of the study

5.1.1 Modules 1 and 2: the 100 ward survey and surveys of CRTs and CMHTs

Strengths of the main staff survey reported in our study include its large size and scope: it has substantially exceeded any other investigation of the mental health workforce both in the numbers surveyed and in the sample’s reach in terms of geography and of coverage of different professions and different ward sub-specialties. The range of potentially associated factors covered has also been larger than in other studies. The overall response rate (64%) is respectable, and we have also included a range of morale indicators, allowing comparison both with previous mental health investigations and with other samples in occupational psychology.

Limitations include that despite a reasonable response rate, there may have been systematic differences between non-responders and responders. There were also wide differences in response rates per ward, with outliers at 22% and 100% response rates; thus it is possible that scores for some wards with low response rates were particularly unrepresentative.

Even with excellent numbers overall, it should be borne in mind that some groups within the sample are relatively small, such as professional groups other than nurses and nursing assistants. Where data have been collected at ward/team level, numbers are much smaller than at individual staff level.

With regard to the examination of potential influences on morale, there are several caveats:

(a) the data on which we report here are cross-sectional and we cannot be sure of the direction of causality where we have found associations. In particular, where variables are attitudinal, as with many organisational context variables such as role clarity and team communication, it could well be that staff tend to perceive their roles as clearer or communication as better because they are generally happier at work, rather than vice versa. The same applies to some reports of events, especially where definitions may be subjective – for example, someone who is already stressed and unhappy at work might be more likely to perceive themselves as being bullied.

(b) Given that there are a substantial number of potential influences on morale, many relatively uninvestigated in the mental health workforce, we have pursued an essentially exploratory analytic approach. Thus we have investigated possible associations between morale and a substantial number of variables identified on theoretical and/or empirical grounds as potentially important rather than testing a small number of specific hypotheses. Particular where findings are only marginally significant and relate to only


209

one indicator of morale, the reader needs to bear in mind that we have made no adjustment for multiple testing and that it is likely that some of our findings have occurred by chance. Greater weight should be attached to findings that recur for several of the morale measures and that are at high levels of statistical significance with substantial effect sizes.

(c) where variables are not significant in the final models, but were significant on initial testing without other variables in the model, it should not be assumed that they have no place in the causal pathways determining morale. For example, the significant association of hours worked with morale indicators disappears once work demands are also added to the model: this is likely to be because the effect of hours worked on morale is mediated by perceived work demands. Likewise, having a personal development plan is no longer significantly associated with some morale indicators once role-related variables are also added to the model: this may well be because effects on morale of having a personal development plan are mediated by greater role clarity and less role conflict, an idea supported by the finding that people who have a personal development plan report significantly higher role clarity and lower role conflict.

(d) Apart from in the tests of the Karasek model reported in Section 4.3, we have tested only for linear associations and not for more complex forms of association or for interaction effects.

(e) Sporadically missing data mean that cases available vary from analysis to analysis, with up to 20% missing for some following listwise deletion of missing data. Use of listwise deletion rather than imputation of missing data may have introduced bias: the use of multiple imputation posed some technical problems in combination with the multiple imputation approach that we used, but in future analyses of these data we plan to find means for assessing the effects of imputing missing data. The anonymously returned questionnaires where the participant’s ward was not recorded could not be used in the main multilevel analyses.

(f) As noted in the sections on sampling, our inclusion criteria in deciding who made up part of each team were relatively broad, and may have resulted in the inclusion of staff who did substantial work off the ward and whose primary allegiance might in fact been to differently constituted teams or split between services. For example, many of the doctors in the service are likely also to have worked in other service settings, and staff such as psychologists, occupational therapists and social workers may identify more with psychology, occupational therapy or social work departments of hospitals than with ward teams. The bias introduced by this is probably towards dilution of differences between teams.

The analyses presented in this report are the first thorough set of analyses from this large and complex data set, which will be subject to further analyses with the aim of deriving the best possible models of morale and the factors associated with it from these data.


210

5.1.2 Module 3 – qualitative findings

Limitations of Module 3 include the inclusion of only 7 rather than the intended 10 wards due to resource limitations, and the difficulties we encountered in assembling enough staff at the same time to hold full-scale focus groups, leading us to substitute smaller scale group interviews. A very large amount of rich qualitative data was gathered, and the current report presents the results from an initial thorough thematic analysis. There is considerable scope for further and more detailed analyses, which we plan to pursue in further publications: for example, patients’ and senior managers’ perspectives can be more fully explored than in the current report, and further analyses from an organisational perspective are also planned. A further limitation of our qualitative work is that we had planned to contrast high and low morale wards, but a tendency for lower morale wards to have improved at follow up obscured this comparison.

5.1.3 Modules 4,5 and 6 – prospective elements of the study

Some research staff issues previously reported to the funders, and also difficulties getting prompt access to some Trusts to collect data, limited the scope of Modules 4 and 6, so that data collection was not fully completed as planned. In module 4, the response rate for leavers recruited from the Module 1 survey was only around a quarter, and we were not able to carry out the study on all wards, so that our resulting sample was small. Conclusions regarding leavers are therefore based more on the community team staff whose experiences of inpatient care were in many cases a substantial time ago, and who may not have been typical of all leavers. In Module 5, despite reasonably good response rates at both data collection points, a surprisingly low number of respondents (around 30%) provided data at both timepoints, calling into question representativeness. For module 6, the investigation of relationships between morale, sickness and turnover, we were able to obtain data for only 64 wards in the prospective follow-up element of the study, this may limit representativeness and also power, as the analysis is at the ward level. Analysis thus far of the data from these modules is at a simple descriptive level.

5.2 Main Findings

5.2.1 Modules 1 and 2

a) The morale of mental health staff:

Regarding the inpatient workforce, the overall pattern of morale is relatively encouraging, and does not suggest that there is a crisis in this workforce or that they are likely to be unable to implement new initiatives. Staff are in general satisfied with their work and very satisfied with their relationships with their colleagues, and they report relatively low levels of cynicism and good levels of personal accomplishment. Where they are burnt out, as in most previous investigations among mental health staff, this tended to be on the emotional exhaustion component of burnout, and the numbers


211

reaching threshold for caseness on the GHQ-12 were also substantial. That mental health work should have a relatively high emotional impact is in some ways unsurprising, but a better understanding of this finding, its antecedents, its impact on patients and any available means of alleviating psychological strain is nonetheless desirable.

There were considerable variations between types of ward, and general acute wards in particular tended to score highly on emotional exhaustion, with a mean just above the threshold for burnout. This is of concern given that these wards are numerous and have a key role in the mental health system. We compared these findings with other inpatient samples reviewed by Cahill et al. (2004). Compared both with the mixed inpatient samples and the acute ward samples included in this review, the mean emotional exhaustion score in our study was at the higher end of the range. However, many of the samples investigated in these previous studies were small and response rates low, so that more confidence should probably be placed in our findings.

It is less obvious why there should be much difference between our study findings and those of the only previous large multicentre investigation of acute inpatient ward staff morale, that reported by Bowers et al. (2009). Bowers and colleagues reported good morale on acute inpatient wards, with a mean for emotional exhaustion just below 18, some 3 points lower than in our study. Potential sources for the difference include a slightly lower response rate, a different disciplinary mix, and the 3 to 4 years that elapsed between the two data collection periods. By the time of our second data collection on 20 wards, there was some evidence of improvement in morale: it may well be that means for morale fluctuate substantially over time depending on various aspects of the current climate in the health service and beyond – improving our understanding of morale may involve examining further how mean levels fluctuate over time and what aspects of the current climate in the NHS influence this.

Cahill et al. found relatively little evidence regarding specialist wards. A small quantity of data regarding long-stay wards suggested better morale than on acute wards: this is compatible with the rather benign profile found on rehabilitation wards in our study.

Turning to the community staff within our sample, with regard to CRT staff, the study confirms the finding of Nelson et al (2009) from a sample of London teams that morale is generally good, despite the potential stresses of working with an acutely ill group in community settings. The team model of working in these services, enhancing support from colleagues may well be relevant to this.

Regarding CMHTs, there is considerably more cause for concern as this was the group for which the evidence of psychological strain was greatest in our study. Despite good levels of satisfaction, 60% of these staff reached the threshold for burnout on emotional exhaustion and 39% were GHQ stressed ‘cases’. These findings are similar to those of Prosser and colleagues (1999), who found higher levels of stress and burnout among community staff than inpatient staff in a UK survey from the mid-1990s, though morale


212

seemed to have improved by time of a follow-up point two years later. More recent studies (Billings et al. 2003, Priebe et al. 2005), conducted early in the new millennium, did not find high burnout among CMHT staff. Thus this group seem to have reverted to a level of stress that caused substantial concern about the teams’ sustainability in the 1990s when they were relatively new (Wykes et al, 1997).

b) Factors influencing morale

We examined a number of types of candidate influence on morale. Before discussing specific areas, a notable overall tendency was for individual-level factors to have a more important influence on morale than ward-level factors. Typically only around 5-8% of the variation on each morale indicator was found to be at ward level, and most of the ward-level potential influences that we investigated for morale were not significantly associated with the various morale indicators in final models.

Regarding the types of potential influence on morale that we examined;

i. The Demand-Support-Control model

Our study provides the first full confirmation that a major theory of work psychology, the Karasek model is applicable to UK mental health workers. It illustrates that the demands made on people, the amount of control or discretion they have in their jobs, and the support from both their managers and colleagues are important focal points for understanding and helping to improve the well-being of mental health workers. Whereas past studies of the antecedents of morale have often investigated a mixture of general and mental health service-specific indicators without being rooted in an over-arching theoretical framework, the results of our study suggest that future research should begin from this very well-established model of occupational stress. This study thus provides a benchmark model for testing organisational factors including personnel management methods, top management leadership styles, and mechanisms for extending employee involvement beyond the role level. This also enables future assessment of whether mental health-specific factors, such as the violence of patients, have their own discrete influence, or rather are mediated by general demand, control or support factors.

When further variables such as those relating to organisational context were added to the model, job control emerged as particularly important, being highly associated with all morale indicators. Work demands appeared to have a large effect in relation to emotional exhaustion and other indicators of psychological distress, but was not related in final models to personal accomplishment. Both support from colleagues and from manager were associated with most dimensions of morale, but associations weakened or disappeared when organisational context variables such as role clarity and team communication were also added to models. The latter variables are potential mediators of support: for example it may be that good support from colleagues results in good team communication and that supportive and effective managers are conducive to good role clarity. There is considerably more scope for using this large and complex data set to


213

explore interrelationships between organisational variables, and we plan to do so in future analyses.

Differences on demand-support-control variables seemed to go some way towards explaining differences between ward and team types in morale. Acute wards were characterised by fairly high demands and the lowest mean score for control in the sample, clarifying why burnout may be greater than, for example, on rehabilitation wards where staff ratings suggested relatively low demands, high control and high levels of support from both ward manager and colleagues. Ward activity profiles suggested that high turnover and a large proportion of detained patients may go some way towards accounting for the higher perceived demands and poorer morale on general adult wards. The profile of CMHTs suggested very high levels of work demands, combined with high levels of control, good support from colleagues and less support from managers than elsewhere. High demands may be a major reason for high emotional exhaustion while the mitigating effects of job control may account for good satisfaction and personal accomplishment despite these.

ii. Organisational context

In addition to the Demand-Support-Control variables, variables relating to role definition (role clarity and role conflict) and communication within the team, rated by individual staff, were highly associated with morale. Levels of role clarity were fairly good, though less so in CMHTs than elsewhere, and role conflict was relatively low overall. However, variations in role definition appear to have an important relationship with levels of morale, as has been found in some previous investigations (Rizzo et al. 1970). Thus Role Theory seems to have a significant explanatory role in relation to staff morale. This suggests that individuals will become more stressed, more dissatisfied and less effective if the behaviours expected of him or her are inconsistent and they experience role conflict. It could be postulated that role theory is particularly important when applied to multi-disciplinary teams where core capabilities may be shared (e.g. in The Ten Essential Shared Capabilities - A Framework for the whole of the Mental Health Workforce (DoH, 2004)) and disciplinary or role boundaries blurred by common goals for professional development (as in the NHS’s Knowledge and Skills Framework (KSF) (Department of Health, 2004).

Cohesion is a factor connected to group performance, or team functioning (Bollen & Hoyle, 1990), and individual members’ perceptions of unit cohesiveness have been postulated as being of importance in terms of a sense of belonging and morale Survey results do not indicate team cohesion or communication to be problematic in our sample, though variations in perceived quality of communication have a substantial association with several morale indicators.

A key finding is that respondents in all types of service rated decision making processes affecting them as unfair rather than fair. Perceived employer fairness has been identified as a basis for employee job satisfaction (Moorman et al, 1993). Our study suggests that it has significant links to several aspects of morale, reinforcing the importance of


214

the relatively poor ratings it attracted. Procedural fairness, i.e. the fairness of procedures used to determine working terms and conditions, attracted poorer ratings than substantive fairness, i.e. how fairly treated participants actually felt in terms of the working terms and conditions they had.

Procedural justice has been linked to employee voice (Moorman et al, 1993), i.e. ways in which employees have opportunities to feed information and ideas into managerial decision making and to be consulted about or negotiate over decisions (Wood and O’Creevy, 2005). The survey results found voice to be very problematic with about two thirds of respondents indicating they could not, or could only minimally, put their views forward to senior management and over three quarters not feeling heard by senior management. CMHT staff were consistently the most negative in their views on fairness and voice. The reasons for this remain unclear from the survey. The other type of community team studied, CRTs, did not replicate this pattern. Results raise the question of why CMHT staff should feel more unfairly treated and that they have less voice within the same organisations as colleagues in other types of team. A key difference may be that CRTs generally adopt a team model of working, with shared responsibility for patients, whereas the lone care coordinator role, often with relatively little input from other professionals, may well be a burdensome and poorly supported aspect of CMHT working.

Regarding supervision, there is no general guidance on the expected frequency of supervision although policy documents argue strongly for its important role in professional support and learning, knowledge and competence, consumer protection and safety is clear (Department of Health, 1993). It links with other concepts such as Clinical Governance and Continuing Professional Development, and is a vehicle for providing support, clinical teaching, training and guidance and promoting the delivery of safe and good quality services. Survey participants received supervision, on average, just slightly more than once every two months, and about a quarter reported no supervision at all in the previous six months. Ward manager reports confirmed that not all wards had been able to implement policies on supervision. These levels of supervision are concerning, although we were not able to discern a clear link between amount of supervision and indicators of morale. Appraisal and personal development plans were in place for around two thirds of the sample. Most staff reported some training days, but overall levels of satisfaction with training were only moderate.

In final analyses, these human resources practices did not emerge as having many independent associations with morale after adjustment for other relevant variables. However, links such as those between having a personal development plan and greater role clarity and less role conflict, suggest that they may have an effect on morale which is mediated by these attitude variables. Even at univariate level, we could find relatively little evidence of any link between morale and interventions that are intended to provide support, such as staff support groups and away days, nor did we find any evidence that any specific therapeutic model or Protected Engagement Time had a positive effect on morale. The heterogeneity of the models reported by wards and the fact that most have not become widely


215

disseminated should be borne in mind before concluding that initiatives to improve care delivery are not linked to better staff morale. In particular, we did not capture the effects of some important recent initiatives, such as Star Wards and AIMS, that have very quickly been taken up by very large numbers of wards: it is conceivable that these have a role in the apparent improvement in morale over one year follow up on 20 wards. A further surprising negative finding from our study was that we found little evidence of a link between staffing levels or use of agency time and staff morale.

iii. Adverse events

Just under a quarter of the sample reported that they had been bullied in the past year and just over a quarter that they had experienced discrimination. The most frequent form of discrimination was on grounds of ethnic background, and 54% of Black African or Caribbean staff reported discrimination, with patients the most frequent source of this. Reporting having been bullied was highly associated with several indicators of morale. Few specific reports regarding bullying in mental health service settings have been published. Compared with general health settings, rates in mental health settings appear to be high, for example compared with the 16% reporting bullying and 18% reporting discrimination in a large sample of Scottish general healthcare staff (Alexander et al., 2000). The reports from Black staff that half had experienced discrimination are particularly concerning in our study. As the introduction summarises, workplace bullying has been found to be associated with poor well-being in the general health workforce: our study provides support for this finding from a mental health setting.

Staff also experienced high levels of violence: the proportion of staff reporting at least one attack in the past year ranged from 45% of rehabilitation ward staff to 76% of older adult ward staff. Violent and threatening behaviour was highly associated with poorer morale. Community staff reported much lower levels of violence, though there was evidence that community team managers under-reported incidents, reporting that there had been no incidents in teams where several individual staff told us of incidents. Physical aggression has been reported as a substantial problem faced by mental health staff over many years (Brennan, 2000), and our study findings suggest that this, and the previously postulated effects on staff psychological well-being, have changed little.

iv. Built environment

We found a strong link between staff perceptions of the ward environment and their overall morale, suggesting that the built environment probably is important to morale, as hypothesised. However, in our analyses thus far of these data we have not been able to establish clearly links between specific features of the ward environment and morale. Ratings combining several important aspects of ward environment may prove better predictors of morale than when features of ward design are examined individually

v. Geographical context and ward population


216

Most of the wards and teams we selected were situated in the most deprived areas of the country, even though this was not an intended goal in sampling. We found some evidence that employment deprivation predicts lower levels of morale. Staff working on wards and teams whose catchment areas covered areas of the country with greater levels of employment deprivation had significantly greater levels of emotional exhaustion, cynicism and depression. This might reflect greater pressure on mental health services or greater complexity of complicating social problems or comorbidities. The overall index of multiple deprivation, however, did not show the same relationship with morale.

Admission rates varied considerably. Greater numbers of admissions predicted higher levels of exhaustion and cynicism among staff, as well as greater anxiety and depression. After adjustment for other predictors of morale, the admission rate was no longer significantly associated with morale outcomes. For these outcomes, ward speciality (such as older adults or rehabilitation) remained significantly associated with morale despite adjusting for annual admission rate. Several of the case-mix variables were significantly associated with morale in simple unadjusted models, but most lost significance after adjusting for other predictors.

Working in teams with higher proportions of male patients predicted higher levels of cynicism among staff and lower levels of personal accomplishment. After adjustment, higher proportions of male patients still predicted lower levels of personal accomplishment, but not cynicism. The ethnic diversity of patients did not predict staff morale ratings in any of the fully adjusted models. However in simple models, there were conflicting relationships between ethnic diversity of patients and staff morale. Although staff working with greater proportions of BME patients were more emotionally exhausted, they reported higher levels of intrinsic satisfaction. A similar picture was observed with the proportion of detained patients.

High levels of detained patients did not predict morale in our final models. However, in simple models staff working with higher proportions of detained patients were more exhausted and had higher levels of cynicism, yet more enthusiasm on the Warr depression-enthusiasm scale. Many of the caseload-related variables were strongly associated with service type. Their tendency to lose their significance when adjustment is made for service type may indicate that these variables are on a common causal pathway to predict morale, with work demands perhaps also relevant on this pathway.

5.2.2 Module 3 – qualitative findings

Much of the interpretation of our qualitative findings is presented along with the results in Section 4.10. Some of the findings amplify and contribute to interpretation of our quantitative findings, while others fit less clearly. A wide mixture of experiences emerged from the qualitative findings, with several recurring themes.

Staffing levels were seen as a major issue, even though we have not found clear evidence for this in the quantitative findings. Many participants felt


217

stretched and unable to fully realize their roles. Why, given the strength of this view, it is not supported by our quantitative data, is not clear. It may be that staff are wrong in their perception that staffing levels, as well as use of agency staff are crucial to their morale, or it may be that we have somehow failed to capture this. Inaccurate data might be one potential basis for this, or it may be that we have not entered into models one or more key variables that interact with staffing levels to influence morale, or that there is not enough variation in staffing levels within our sample to capture its relationship with morale.

Relationships between front-line staff, managers and senior managers emerged as another important issue, and this time there is considerable congruity between quantitative and qualitative data. Senior managers in Trusts tended to be seen as remote and lacking awareness of how things really were on the ward. They were seen as potential positive influences on team morale if they provided good leadership and communicated effectively. However, in practice they were seen as under-involved and alienating to staff.

Again congruent with the quantitative findings, staff and managers stressed the importance of role clarity and the structural factors needed to maintain this. Empowering staff was recognised as a way of boosting morale, but only if responsibilities are clearly defined and staff feel adequately supported. Supervision was thought to be important for ensuring role clarity and also provided staff with an outlet through which to voice their personal needs. A sense of being listened to and valued was crucial for morale, and often felt to be lacking. Staff identified the most important positive influences on morale as their interactions with patients and their relationships with colleagues. Although there was much discussion of the challenges and difficulties involved in caring for patients, staff were motivated by the rewarding nature of the work.

An emerging higher-order theme concerned the harmony between a person’s “ideal” conceptualization of their role and the reality of that role in practice. Thus, limited staffing, hierarchical structures and inadequate training all restricted actualization of one’s ideal. Instability of structure and unreliable supervision impeded role clarity and undermined confidence in pursuing that ideal. And feeling unappreciated damaged willingness to do so. Where staff did not identify with and take ownership of their roles, they were likely to become demoralized. It was notable in those who felt positively about work that they had chosen to be where they were. Crucially, this was not always the case:

I think they get on with each other very well and they all have a passion for recovery. I think that’s the bottom line. They want to be in the job that they’re doing and they’ve chosen to work within recovery, so I think that’s very important. That you are actually working within an environment that you want to work in and that you feel confident to work in, rather than being placed somewhere. (Ward Manager, H)


218

5.2.3 Module 4 – reasons for leaving wards

Two main findings are suggested by the survey of community team staff reported in Section 4.11.

First, many staff move from inpatient wards to community services. Nearly 70% of surveyed staff from community teams had previous experience of working in inpatient settings. Second, a desire for more control at work appears more commonly to be an important reason for staff leaving inpatient work than levels of stress. Over three quarters of respondents from community teams reported leaving ward work in order to gain more control over how they work; more than 60% also reported wanting greater opportunities for career development. Thus staff moving from inpatient wards seems to a considerable extent motivated by a realistic view that they will have more autonomy in community settings. By contrast, fewer than a third of respondents identified demand factors including levels of stress, violence or drug use on wards as important reasons for leaving.

The Leavers’ Survey reported in Section 4.4.3 does not provide robust findings because of its small sample and low response rate. However, its results appear to be broadly congruent with responses from community team staff. Only 17% of respondents in the Leavers’ Survey moved to another inpatient ward, while 28% moved to community teams and 39% left the NHS altogether, suggesting difficulties with staff retention in NHS inpatient services. 70% of respondents identified wanting more control of how they worked as an important reason for leaving their inpatient job - the second most common response.

The importance of control factors in staff decisions to leave inpatient jobs is consistent with the strong association between job control and morale found in Modules 1 and 2. As in Module 1 and 2, results from module 4 do not suggest though that demand and support factors are of negligible importance. Large minorities of respondents identified stress at work and the prevalence of violence and drug use on wards as important reasons for leaving. The quality of management at ward and Trust level were also identified as important by a more than half of respondents in the Leavers’ Survey.

5.2.4 Module 5- stability of morale

Individual-level results suggest that, in general, morale amongst inpatient staff is only moderately labile. General Health Questionnaire and Maslach Burnout Inventory data suggest about 75-80% of staff maintain morale status over one year follow-up. Warr scale data show only small changes in contentment and enthusiasm. Among the sub-group of staff who rated as burnt out or stressed cases at baseline however, changes in morale by follow-up were more frequent. General Health Questionnaire results indicate more than 60% of stressed cases at baseline were not stressed cases at follow-up. Maslach Burnout Inventory results suggest about half of burnt out staff at baseline were not burnt out at follow-up. Ward staff thus


219

demonstrate resilience and there seems to be a tendency for staff to recover from extremes of low morale.

Ward-level data suggest low morale wards also have the capacity to recover. Maslach Burnout Inventory results suggest that, on each measured domain, a majority of high burnout status wards at baseline were no longer high burnout status at follow-up. About a quarter of wards showed apparently substantial (>20%) changes in their proportion of burnt out or stressed case staff at the two time points. Maslach Burnout Inventory, General Health Questionnaire and Warr scale data consistently showed a greater increase in morale at follow-up among the London region wards compared to the Heart of England Region wards.

Individual-level Maslach Burnout Inventory results suggested that levels of emotional exhaustion among ward staff might be slightly less stable than cynicism or personal accomplishment. (More respondents changed status on the emotional exhaustion domain and the amount of variance was greater than for the other two domains.) This was not replicated in ward level results however, where the number of wards changing status and the magnitude of changes appear broadly similar for all three burnout domains.

5.2.5 Module 6- relationship of morale to turnover an sickness rates

Great weight cannot be placed on Module 6 findings for reasons discussed in the strengths and limitations section of this report. However, the conclusion it suggests is that, while there are some relationships between sickness rates and turnover, these are modest. This suggests that in this workforce, sickness may be related more to real illness than to dissatisfaction at work, and reasons for leaving jobs are complex and, as the leavers’ survey suggestions, often motivated more by real benefits and opportunities in a new role and by career development rather than by low morale.

5.3 Implications for research

Even though this is the most extensive study of morale in the mental health workforce of which we are aware, it suggests a substantial number of unanswered questions for further research. Certain of these can be addressed by further analyses of the data we have obtained; others require further research. Some of the more important of these are as follows:

(a) What is the best approach to measuring staff morale as simply as possible? Section 4.2 reports that an exploratory analysis reduced the various measures of well-being and satisfaction at work to two main dimensions, one relating to psychological stress and the other to engagement in and sense of achievement from work. A one item self-rating of morale loads substantially onto both. If this can be replicated in other samples, it suggests a move towards simple measurement techniques involving these two main factors. Especially given the emergence in this model of a distinct factor that correlates most highly with the Job Involvement measure, we suggest that in future work Workplace


220

Engagement should have a greater centrality than in this report. This is an area in which theoretical and empirical literature have been advancing rapidly in the past five years. Workplace engagement is defined as a state that encompasses vigour, dedication and absorption (Bakker and Demrouti, 2008), and is most frequently measured in recent literature by the Utrecht Workplace Engagement Scale (Schaufeli et al. 2006).

(b) Why is morale so poor in CMHTs and what could be done to address this? The high levels of psychological stress reported from these community teams suggest addressing this question should be a high priority. Some candidate factors, including high demands, low managerial support and low levels of voice and perceived fairness are identifiable from our data. Further work, in which qualitative methods may be the approach of choice, should address the mechanisms that produce this adverse profile and seek ways of addressing this.

(c) What types of experiences lead mental health staff to report that they have been bullied or discriminated against? Our study suggests that experiences of being bullied or discriminated against, experiences which are associated within one another, are important to morale. However, we based this on very simple self-report data and know very little about the nature and context of such experiences. More detailed research, probably combining quantitative and qualitative methods, is very desirable to achieve an understanding of what underlies this high prevalence and how it might be alleviated.

(d)How does staff morale affect patient experiences and outcomes? Research on staff morale tends to be based on an assumption that it is important not only for the well-being of the workforce, but also because workforce well-being directly affects patients. There have been few substantial tests of this: examining this relationship across a substantial number of services would be an obvious next step.

(e) How can morale be improved? A further obvious next step following on from this research is to use it to inform an intervention to improve morale. Paris and Hoge (2009) have recently published a review of burnout in mental health services. They found little empirical evidence of the effectiveness of interventions to enhance morale, but identified 12 strategies which have been advocated:

a) competitive salaries

b) financial and non-financial incentives to enhance staff motivation

c) opportunities for promotion and career enhancement

d) funding for increased staffing levels

e) training staff on self-care strategies

f) additional clinical supervision and mentoring

g) clear job descriptions/expectations

h) routine assessment of burnout


221

i) flexible work schedules

j) social events and informal support

k) in-service training

l) open door policies with management

Our study suggests that those strategies that target increased job control, role clarity, fairness or peer support and reduced role conflict should be prioritised in any interventions to improve morale.

5.4 Implications for policy and practice

The limitations of our study have been discussed above: this and the lack of evidence regarding both the effects of low morale and how to improve morale, should be borne in mind in any attempt to translate these findings into practice. However, some indications about emerge about potentially worthwhile targets for policy making and service planning.

(a) The morale of CMHTs and general acute wards: Our study suggests that the most important foci for any initiative to improve the mental health of the workforce are CMHTs and, to a lesser extent, acute inpatient wards, although the substantial variations even among these teams and the tendency for low morale services to recover over time should be borne in mind. The role of CMHTs in a service system that is increasingly based on specialisation by service function has been debated, and in this context it would not be surprising if they have lost both resources and clarity regarding their role. If they or their successors are to be effective components in a restructured service system, the morale of their staff needs urgently to be addressed. Very high levels of perceived demands, together with a lack of support from senior managers appear are very prominent. The more benign profile of CRTs, despite an acutely ill population and high levels of risk, suggest that a team approach may be significantly protective. Such an approach, with frequent handovers and discussions of patients and considerable sharing of patients has not tended to characterise CMHTs, where individual caseworking often prevails despite a multidisciplinary staffing. Recent discussion of the Assertive Community Treatment model suggests that a useful and effective component of this may be the team approach to patients, even though the model overall seems to have little effect on outcomes (Burns,2010). Effects on staff morale may be a further rationale for adopting more team-based models of working in CMHTs, drawing on models from newer types of team such as assertive outreach teams and early intervention services for psychosis.

General acute wards also emerge as a significant focus, with high turnover and large numbers of disturbed compulsorily detained patients as potential factors in the finding of slightly lower morale on these wards than on most types of specialist ward. Recent initiatives to reduce hospital bed use are likely to have intensified these characteristics of general wards. Most initiatives intended to improve inpatient care have in fact focused on general acute wards, so that it seems pointless to recommend a shift in


222

focus to these wards, but it does support the focus on these wards and suggest that evaluations of new initiatives must include examination both of influences of staff morale on their implementation and outcome and of their influence on staff morale. Since our main study was conducted, a model that has become increasingly popular is of separate, short stay admission wards that aim to discharge a high proportion of patients after only a brief stay, with transfer to standard acute wards only in the minority with whom this is achieved. This may provide some protection to staff on the standard wards to which patients are transferred by reducing their turnover, though the typical level of disturbance of patients on this wards. The admission wards have potential to result in a high level of demands on staff, with potential impacts on morale: investigating this and ensuring that jobs on these wards are as well designed and supported as possible (see below) are priorities if this model is to become a standard one in the NHS.

(b) Job design and team functioning: Within the NHS, strategies for alleviating mental health staff stress have traditionally focused on managerial or peer support, for example via supervision and appraisal, peer support groups or training. Our findings do suggest that such strategies have the potential to improve morale if they are experienced as supportive. Colleague support and team communication are important for several outcome indicators: thus interventions to enhance team cohesion and relationships may have potential, as well as more formal manager support ones. Current initiatives in inpatient settings (e.g. protected engagement time) are highly focused on staff–patient interaction, improving the quality of this and getting staff out of clinical offices into wards. However, opportunities for staff to have more high quality interactions with one another spend more time together may also have a function in improving the necessary to improve staff morale and thus performance.

That demand is important for well-being seems intuitively likely and is unsurprising given a range of studies that have cited various mental health service-specific demands as important to staff stress. How demands may be reduced is rather less obvious, given high demands for mental health services in general and the likely adverse effects of the current economic crisis on resources. A beginning might be the identification of specific tasks or roles where staff are under high levels of pressure with a view to redesigning the way teams work to try and alleviate stress experienced in these. Involvement of staff in these processes should increase their success. Initiatives to reduce the demands on services as a whole are difficult to design, but might include clearer intake criteria and protocols that allow staff to focus on the activities that are their core roles, rather than expending effort on, for example, working with patients on matters that could be better dealt with elsewhere. The recent Productive Ward initiatives (http://www.institute.nhs.uk/quality_and_value/productivity_series/productive_ward.html) , aimed at redesigning processes across wards of a range of types, have as their target improving service organisation in order to allow staff to have more time to deliver good quality care to patients: if successful, this has potential to reduce demands.


223

The strong associations of autonomy and role definitions with staff well-being suggests that these are factors that should be prominent in attempts to optimise staff morale and functioning. We are not aware of strategies for improving staff morale that have specifically focused on autonomy in mental health services, despite the strong and consistent link between this and all the indicators of morale that we investigated. However, job design is a well developed research tradition, especially in industry, methods for analysing and enhancing job design have been developed in a range of settings (Hackman and Oldman, 2010). A first step would be to investigate in more detail the organisation of jobs and teams to identify areas in which autonomy might be increased, especially in groups that report low levels. Job redesign or changes in management practices to allow greater autonomy in deciding how to work would then be feasible, though training initiatives may well be needed to support these. Job redesign initiatives could also usefully focus on role definition and role clarity, which like other aspects of ward organisation need to be addressed both at the level of individual jobs and the overall organisation of the ward team. Both quantitative and qualitative findings suggest that staff feel they work best when they understand what their task is, have the skills and resources to do it, and have some latitude in deciding how to complete their tasks: this should be the aim of initiatives to analyse and clarify individual and team roles.

(c) Fairness and voice: As discussed above, perceived fairness and voice with managers above ward level are areas in which there appears to be considerable room for improvement: both quantitative and qualitative findings concur here, and the indicators of fairness appeared to have substantial associations with indicators of morale. Thus senior managers may wish to examine the way they relate to and are perceived by ward staff, and to consider means of creating more open and effective means of communication with front-line staff and with managers. Our impression at the moment is that ward and community teams tend to be highly cohesive staff groups that operate in relative isolation, often perceiving themselves as an embattled unit under siege.

Thus there is considerable scope for enhancing worker voice and relationships with senior management in the NHS. Current mechanisms, such as Acute Care Forums and news on Trust websites and by email do not seem to be achieving a sense of real participation in important management decisions. Trade unions and professional bodies are a traditional conduit for worker voice, but no mentions were recorded of these in the qualitative study, suggesting they may not be prominent in the everyday life of the current NHS. Innovations for improving voice might include greater presence of senior managers on wards (the sense that they did not know what the life of the ward was like in practice was very strong among staff), opportunities for staff to be present at higher level Trust meetings, speak-up mechanisms allowing staff to get concerns and ideas for improvement heard by managers, more extensive consultations on important decisions and greater attention to the unions (Pyman et al., 2006, 2010; Marchington and Wilkinson, 2005). These are areas in which it is essential that


224

innovators look outside the NHS as many of the schemes intended to enhance senior management/employee relations have been in the private sector, often in large companies with a tradition of innovation in human resource management, such as British Telecom.

(d) Bullying and discrimination: Further investigation of the context and nature of reports of bullying are warranted, as noted above, but the high prevalence of such reports in this study suggests that anti-bullying initiatives deserve a high profile in NHS mental health settings. In our study, as in previous workplace investigations, bullying was strongly associated with poorer morale. These findings are not novel: bullying and the closely associated issue of discrimination have repeatedly been identified as issues in the NHS (Healthcare Commission, 2006), the Boorman review (Department of Health 2009b) describing it as endemic in NHS culture. The public and private sectors yield a range of models for anti-bullying campaigns, characteristically including education for managers, who are often found to have a limited understanding regarding the behaviours that constitute bullying, mechanisms for rapid feedback on negative behaviours and ensuring that aggressive management styles receive no positive reinforcement, and rapid and easy access to confidential reporting of personal and colleagues’ experiences of bullying for staff. Where senior management have made real commitments to and invested substantial resources in intensive anti-bullying strategies in the workplace, considerable changes have been reported in employee well-being and productivity (Lutgen-Sandvik et al. 2009). A range of such initiatives are being implemented in the NHS: in the mental health services, it will be essential that they are implemented sufficiently vigorously to make a real impact on management styles and team cultures: simply providing information and setting up helplines may not be sufficient to achieve this.

Some relatively difficult to address issues are raised by the frequent reports that patients discriminate against staff, especially Black staff. There is a fine line between accepting that patients who are currently mentally ill may not have the values and behaviour we would usually like and exposing staff to unacceptable levels of harassment – further guidance on these issues is desirable.

(e) Violence against staff: Our findings that mental health inpatient staff report high levels of violence and that these are associated with poorer morale are also not original: this is a problem that has long been identified but has yet to be successfully addressed. Despite being repeatedly identified as a serious and unacceptable problem, there is little evidence of any initiative having made a substantial impact on this. Dividends from a successful initiative in terms of improved morale and reduced sickness are potentially great. Cowman and Bowers (2009) have examined risk management and security staff on inpatient wards, and note that we still lack evidence-based and consistently implemented guidance: this seems an urgent need. Potential avenues for trying to reduce violence on wards include closer links with police and more use of judiciously targeted prosecutions, security staff on wards, training for staff in reducing violence, environmental audits, greater attention to procedures for ensuring staff and


225

patient safety, and clinical interventions targeting violence in specific clinical groups, such as patients with personality disorders, are among the potential avenues for attempting to introduce effective interventions (Cowman and Bowers, 2009; Steinert et al., 2008; Bowers et al., 2006; Mann et al., 2008). A further priority, highlighted in the qualitative study, is the response to violence. This tended to be described as rather passive and fatalistic, suggesting that mental health staff and managers may feel there is little they can do about the problem of endemic violence. Strategies are thus also needed for improving the response to violence and thus, it might be hoped, reducing sick leave and long term adverse psychological consequences. A minimum response should surely be some supportive meetings with managers/supervisors and monitoring of responses following violent incidents: the qualitative findings suggested this was far from standard, let alone more specific interventions.

In the community, levels of violence are considerably lower, but seem to be under-recognised by managers, this is a concern as while incidents in the community are less common than in hospital settings, the relative isolation of community workers may make these incidents particularly frightening and dangerous. Developing clear protocols for preventing and responding to violence in community settings is thus also a priority.

(f) General initiatives for improving human resource management and providing health and well-being services: Above we discuss potential initiatives to address the issues that are most prominent in the results of our survey. A full review of initiatives to improve employee well-being is beyond the scope of this report, whose main focus is not on interventions. However, we note that employee well-being is currently an important focus for new guidance, both within the NHS and outside it, and that the recommendations that result for enhanced workplace health services and improved human resource management practice may well prove very relevant to improving the well-being of the mental health workforce: this remains to be tested. The development of the NICE guidance on interventions to improve mental health in the workplace (NICE, 2009) has involved an extensive review of relevant evidence and stakeholder interventions. A paucity of evidence on effective workplace interventions to improve mental health is noted, but draft recommendations include promoting a culture of participation and fairness (see above), promoting a supportive style of management, maximising opportunities for flexible working, creating an awareness of mental well-being and reducing stigma attached to mental health problems, and optimising human resource practices such as training, appraisal and recruitment. They also advocate a targeted approach to employees who are experiencing high levels of work-related stress or may be experiencing mental health problems, suggesting an active approach to managing absences is a cornerstone of this. The recommendations of the Boorman Review (Department of Health 2009b) regarding NHS staff health and well-being likewise emphasise workplace health services that provide early and effective intervention in health problems, including mental health problems related to work stress as one of the highest priorities. Like the NICE guidance, this review identifies


226

employee participation and effective human resource practices as key to staff well-being. A number of interesting case studies describe local initiatives targeting NHS staff well-being.


227

References Alexander, D. A., Gray, N. M., Klein, S., Hall, G., & Kettles, A. 2000, "Personal safety and the abuse of staff in a Scottish NHS Trust", Health Bull.(Edinb.), vol. 58, no. 6, pp. 442-449.

Appleby, L. (2003) So, are things getting better? Psychiatric Bulletin, 27, 441-442.

Bakker, A. B., Schaufeli, W. B., Leiter, M. P., & Taris, T. W. (2008). Work engagement: An emerging concept in occupational health psychology. Work & Stress, 22, 187-200.

Billings, J. et al. (2003) Assertive outreach teams in London: staff experiences and perceptions: Pan-London Assertive Outreach Study, Part 2. British Journal of Psychiatry 183:139-147

Bollen, K. A., & Hoyle, R. H. (1990). Perceived cohesion: A conceptual and empirical examination. Social Forces, 69, 2, 479-504.

Bond M., & Holland, S. (1998) Skills of Clinical Supervision for Nurses. Buckingham: Oxford University Press

Bowers, L, Allen, T, Simpson, A, Jones, J and Whittington R (2009) Morale is high in acute inpatient psychiatry. Social Psychiatry and Psychiatric Epidemiology 44: 39-46.

Bowers, L., Nijman, H., Allan, T., Simpson, A., Warren, J., and Turner, L. (2006) Prevention and management of aggression training and officially reported violent incidents: The Tompkins Acute Ward Study. Psychiatric Services 57: 1022-1026

Bray, J. (1999) An ethnographic study of psychiatric nursing. Journal of Psychiatric & Mental Health Nursing.6:297-305

Brennan, W. (2000). We don't have to take this: dealing with violence at work. Nursing Standard, 14, 28, 3-17.

Burns, T. (2010) The rise and fall of assertive community treatment? International Review of Psychiatry 22: 130-137.

Cahill, J. et al (2004) Systematic Review of staff morale in in-patient units, SDO website

Care Quality Commission. (2008). The National NHS Staff Survey. http://www.cqc.org.uk/usingcareservices/healthcare/nhsstaffsurveys/2008nhsstaffsurvey.cfm

Carpenter et al. (2003) Working in multidisciplinary community mental health teams: the impact on social workers and health professionals of integrated mental health care. British Journal of Social Work, 33, 1081-1103.


228

Carron, A.V., Colman, M.M., Wheeler, J., & Stevens, D. (2002). Cohesion and performance insport: A meta analysis. Journal of Sport & Exercise Psychology, 24, 2, 168-188.

Clarke, I. (2008) Learning from critical incidents. Advances in Psychiatric Treatment 14: 460-468.

Coffey, M. and Coleman, M. (2001) The relationship between support and stress in forensic community mental health nursing. Journal of Advanced Nursing 34: 397-407.

Cowman, S. & Bowers, L. 2009, "Safety and security in acute admission psychiatric wards in Ireland and London: a comparative study", Journal of Clinical Nursing, vol. 18, no. 9, pp. 1346-1353.

Department of Health (1993). Vision for the Future Report of the Chief Nursing Officer. London: HMSO.

Department of Health (2000) Improving Working Lives Standard. London: Department of Health.

Department of Health (2002) Acute Adult In-patient Care: Policy Implementation Guide. London: Department of Health.

Department of Health (2004) The ten essential shared capabilities. London: Department of Health

Department of Health (2005) Model employer. www.dh.gov.uk/PolicyAndGuidance/HumanResourcesAndTraining/ModelEmployer/fs/en

Department of Health (2009a) New Horizons: Towards a shared vision for mental health. Consultation. London: Department of Health.

Department of Health (2009b) NHS Health and Wellbeing: Final Report http://www.nhshealthandwellbeing.org/FinalReport.html

Di Meglio, K., Padula, C., Piatek, C., Korber, S., Barrett, A., Ducharme, M., Lucas, S., Piermont, N., Joyal, E., DeNicola, V., & Corry, K. (2005). Group cohesion and nurse satisfaction: Examination of a team-building approach. The Journal of Nursing Administration, 35, 3, 110-120.

Edwards, D., Hannigan, B., Fothergill, A., & Burnard, P. (2002). Stress management for mental health professionals: a review of effective techniques. Stress and Health, 18, 5, 203-215.

Feldman Barrett,L. & Russell,J. (1999) “structure of current affect” Current Directions in Psychological Sciences vol. 8 (11)

Fernandes, C., Raboud, J., Christenson, J., Bouthillette, F., Bullock, L., Ouellet, L., & Moore, C. (2010). The effect of an education program on violence in the emergency department. Annals of Emergency Medicine 39, 1, 47-55.


229

Folger, R., & Greenberg, J. (1985) Procedural justice: An interpretive analysis of personnel systems. In K. Rowland & G. Ferris (Eds.), Research in personnel and human resources management (Vol. 3, pp. 141-183). Greenwich, CT: JAI Press.

Foster, C., Bowers, L., & Nijman, H. 2007, "Aggressive behaviour on acute psychiatric wards: prevalence, severity and management", Journal of Advanced Nursing, vol. 58, no. 2, pp. 140-149

Garcia, I., Kennett, C., Quraishi, M., & Durcan. (2005). Acute care 2004: a national survey of adult psychiatric wards in England. London: Sainsbury Centre for Mental Health

General Medical Council (2001). Appraisal. Good Medical Practice. GMC, London

Goldberg, D. P., & Williams, P. (1988). The User's Guide to the General Health Questionnaire. Windsor: NFER—Nelson.

Gunter, B. and Furnham, A. (1996) Biographical and Climate Predictors of Job Satisfaction and Pride in Organization, The Journal of Psychology 130(2): 193-208

Harper, H. and Minghella, E. (1997) Pressures and rewards of working in community mental health teams. Mental Health Care. 1: 18-21

Hauge, L. J., Skogstad, A., & Einarsen, S. 2007, "Relationships between stressful work environments and bullying: Results of a large representative study", Work and Stress, vol. 21, no. 3, pp. 220-242.

Haynes,C.; Wall,T.; Bolden,R.; Stride,C.; Rick,J. (1999) “Measures of perceived work characteristics for heallth services research: test of a measurement model and normative data” British Journal of Health Psychology vol. 4(3) pp 257-275

Healthcare Commission (2006) “NHS National Staff Survey 2006” DoH, London

Higgins,R. Hurst,K. Wistow,G. (1999) “Nursing acute psychiatric patients” Journal of Advanced Nursing, 29, 52-63.

Hogh, A., Henriksson, M. E., & Burr, H. 2005, "A 5-year follow-up study of aggression at work and psychological health", International Journal of Behavioral Medicine, vol. 12, no. 4, pp. 256-265.

Hutchinson, M., Vickers, M., Jackson, D., & Wilkes, L. 2006, "Workplace bullying in nursing: towards a more critical organisational perspective", Nursing Inquiry, vol. 13, no. 2, pp. 118-126.

Jackson, D. Who would want to be a nurse? Violence in the workplace - a factor in recruitment and retention. Journal of Nursing Management 10(1), 13-20. 2002


230

Janner,M. (2007) From the inside out – Star Wards. Letters from acute inpatient wards. Journal of Psychiatric Intensive Care (2007), 3:75-78

Jehn,K. (1995) “A multimethod examination of the benefits and detriments of intra-group conflict” Administratve Science Quarterly vol.40 pp 256-282

Johnson, J. V., & Hall, E. M. (1988). Job strain, work place social support, and cardiovascular disease. American Journal of Public Health, 78, 1336-1342.

Johnson, S. L. 2009, "International perspectives on workplace bullying among nurses: a review", International Nursing Review , vol. 56, no. 1, pp. 34-40.

Johnson, S., Lloyd-Evans,B., Howard, L., Osborn, D.P.J. and Slade M. Where next with residential alternatives to hospital? British Journal of Psychiatry 197: S52-S54

Kadushin, A. (1992). Supervision in Social Work. New York: Columbia University Press.

Kalisch, BJ. (2007). An intervention to enhance nursing staff teamwork and engagement. Journal of Nursing Administration, 37, 2, 77-84.

Karasek, Jr. R. A. (1979). Job demands, job decision latitude, and mental strain: Implications for job redesign. Administrative Science Quarterly 24, 285-308.

Karasek R (1989) Control in the workplace and its health-related aspects. In: Sauter SL, Hurrell JJ Jr, Cooper CL (eds) Job control and worker health. John Wiley & Sons, Chichester, pp 129–159

Kersley,B.; Alpin,C.; Forth,J.; Bryson,A.; Bewley,H.; Dix,G. (2004) “Inside the workplace: findings from the 2004 Workplace Employment Relations Survey” Routledge

Kivimaki, M., Elovainio, M., & Vahtera, J. 2000, "Workplace bullying and sickness absence in hospital staff", Occupational and Environmental Medicine, vol. 57, no. 10, pp. 656-660.

Landsbergis,P. Schnall,P. Deitz,D. Friedman,R. Pickering,T. (1992) “The patterning of psychological attributes and distress by “job strain” and social support in a sample of working men” Journal of Behavioural Medicine vol. 15 (4) pp 379-405

Lasalvia,A.; Bonetto,C.; Bertani,M.; Bissoli,S.; Cristofalo,D.; Marrella,G.; Ceccato,E.; Cremonese,C.; De Rossi,M.; Lazzarotto,L.; Marangon,V.; Morandin,I.; Zucchetto,M.; Tansella,M. and Ruggeri,M. (2009) “Influence of perceived organisational factors on job burnout: survey of community mental health staff” The British Journal of Psychiatry vol. 195(6) pp. 537-544.


231

de Lange AH, Taris TW, Kompier MAJ, Houtman ILD, Bongers PM (2003) “The very best of the millennium”: longitudinal research and the demand-control-(support) model. J Occup Health Psychol 2003: 282–305

Lelliott, P. and Quirk, A. (2004) What is life like on acute psychiatric wards? Current Opinion in Psychiatry 17: 297-301.

Lelliott, P., Bennett, H., McGeorge, M. and Turner, T. (2006) Accreditation of Acute Inpatient Mental Health Services. Psychiatric Bulletin 30: 361-363

Lee, R. T. & Brotheridge, C. M. 2006, "When prey turns predatory: Workplace bullying as a predictor of counteraggression/bullying, coping, and well-being", European Journal of Work and Organizational Psychology, vol. 15, no. 3, pp. 352-377

Llorens Montes, F., Ruiz Moreno, A., & Garcia Morales, V. (2005). Influence of support leadership and teamwork cohesion on organizational learning, innovation and performance: an empirical examination. Technovation, 25, 10, 1159-72.

Lutgen-Sandvik, P., Namie, G., & Namie, R. (2009). "Workplace bullying: Causes, consequences, and corrections." In P. Lutgen-Sandvik & B. D. Sypher (Eds.), Destructive organizational communication: Processes, consequences, and constructive ways of organizing. (pp. 27-52). New York: Routledge/Taylor & Francis

A. Mann, P. Sugarman, C. Rooney, M. Goodman, and J. Lynch (2007) Service innovation: policing mental health - the St Andrew's scheme The Psychiatrist; 31(3): 97 - 98.

Marchington, M. and Wilkinson A. (2005) Managing worker voice. In Human Resource Management at Work: People Management and Development. London: Chartered Institute of Personnel and Development.

Maslach, C. and Jackson, S. (1981) The Maslach Burnout Inventory. California, Consulting Psychologists Press

Maslach,C. (1998) “A multidimensional theory of burnout” in Cooper,C. ed “Theories of work stress” pp 68-85 Oxford University Press, Oxford

Matthiesen, S. B. & Einarsen, S. 2004, "Psychiatric distress and symptoms of PTSD among victims of bullying at work", British Journal of Guidance & Counselling, vol. 32, no. 3, pp. 335-356.

McGeorge, M. (2000). Managing violence in psychiatric wards: Preliminary findings of a multi-centre audit. Royal College of Psychiatrists' Research Unit, Report: London

McGeorge, M. & Rae, M. (2007) "Acute in-patient psychiatry: service improvement - the time is now", The Psychiatrist, vol. 31, no. 7, pp. 259-261.


232

Michie S. (2002) Causes and management of stress at work. Occupational and Environmental Medicine 59:67-72

Michie, S. and Williams S. (2003) Reducing work related psychological ill health and sickness absence: a systematic literature review Occupational and Environmental Medicine 60:3-9

Moorman, R.H., Blakely, G. L., & Niehoff, B. P. (1998). Does perceived organizational support mediate the relationship between procedural justice and organizational citizenship behavior? The Academy of Management Journal, 41, 3, 351-357.

Moorman, R.H., Niehoff, B. P., & Organ, D. W. (1993) Treating employees fairly and organisational citizenship behaviour. Sorting the effects of job satisfaction, organisational commitment and procedural justice. Employee responsibilities and rights journal, 6, 3, 209 – 225.

Motowidlo,S & Borman,W. (1978) “Relationships between military morale, motivation, satisfaction and unit effectiveness” Journal of Applied Psychology vol. 63(1) pp 47-52

Muijen, M. (1999) Acute hospital care: Ineffective, inefficient and poorly organised. Psychiatric Bulletin, 23, 257-259.

Needham, I., Abderhalden, C., Halfens, R. J. G., Dassen, T., Haug, H. J., & Fischer, J. E. 2005, "The effect of a training course in aggression management on mental health nurses' perceptions of aggression: a cluster randomised controlled trial", International Journal of Nursing Studies, vol. 42, no. 6, pp. 649-655.

Nelson, T., Johnson, S. and Bebbington, P. (2009) Satisfaction and burnout among staff of crisis resolution, assertive outreach and community mental health teams. Social Psychiatry and Psychiatric Epidemiology 44: 541-549.

NHS Employers (2006) NHS Employers Guidance – Bullying and harrassment. London: NHS Employers. http://www.nhsemployers.org/Aboutus/Publications/Documents/Bullying%20and%20harassment.pdf

Niedhammer, I., David, S., Degioanni, S., Drummond, A., & Philip, P. 2009, "Workplace Bullying and Sleep Disturbances: Finding's from a Large Scale Cross-Sectional Survey in the French Working Population", Sleep, vol. 32, no. 9, pp. 1211-1219.

Oldman, G. and Hackman, J.R. (2010) Not what it was and not what it will be: The future of job design research Jounal of Organisational Behaviour 31: 463–479

Onyett, S. et al. (1997) Job satisfaction and burnout among members of CMHTs. Journal of Mental Health 6: 55-66.

Paris M and Hoge M (2010) Burnout in mental health professionals: a review. The Journal of Behavioural Health Services and Research. On line first.


233

Payne RL (1979) Demands, supports, constraints and psychological health. In: Mackay CJ, Cox T (eds) In response to stress: occupational aspects. IPC Business Press, London, pp 85–105

Priebe, S., Fakhoury, W., Hoffmann, K., & Powell, R. (2005) Morale and job perception of community mental health professionals in Berlin and London. Social Psychiatry and Psychiatric Epidemiology, 40, 223-232.

Proctor, B. (1986) Supervision: a co-operative exercise in accountability. In M. Marken and M. Payne. (Eds.), Enabling and ensuring-supervision in practice (pp. 21-34). National Youth Bureau, Council for Training in Youth and Community Work, Leicester.

Prosser, D., Johnson, S., Kuipers, E., Szmukler, G., Bebbington, P. and Thornicroft, G. (1996) Mental health, ‘burnout’ and job satisfaction among hospital and community-based mental health staff. British Journal of Psychiatry 169: 334-337.

Prosser, D. et al. (1997) Perceived sources of work stress and satisfaction amongst hospital and community mental health staff. Journal of Psychosomatic Research 43: 51-59

Prosser, D. Johnson, S., Kuipers, E., Szmukler, G., Bebbington, P. Reid, Y. and Thornicroft, G. (1999) Mental health, burnout and job satisfaction in a longitudinal study of mental health staff. Social Psychiatry and Psychiatric Epidemiology 34: 295-300.

Pyman, A., Cooper, B., Teicher, J. and Holland, P. (2006) Comparison of the Effectiveness of Employee Voice Arrangements in Australia. Industrial Relations Journal, Vol. 37, No. 5, pp. 543-559

Pyman, A., Holland, P., Teicher, J. and Cooper, B (2010)., Industrial Relations Climate, Employee Voice and Managerial Attitudes to Unions: An Australian Study. British Journal of Industrial Relations, Vol. 48, Issue 2, pp. 460-480.

Quine, L. 1999, "Workplace bullying in NHS community trust: staff questionnaire survey", British Medical Journal, vol. 318, no. 7178, pp. 228-232

Quirk, A. and Lelliott, P. (2001) What do we know about life on acute psychiatric wards in the UK? A review of the research evidence. Social Science & Medicine, 53: 1565-1574.

Rask, M., & Hallberg, I.R. (2000). Forensic psychiatric nursing care - nurses apprehension of their responsibility and work content: a Swedish survey. Journal of Psychiatric & Mental Health Nursing, 7, 2, 163-77.

Remington,N. Fabrigar,L. Vissar,P (2000) “Re-examining the circumplex model of affect” Journal of personality and Social Psychology vol. 79 pp 286-300


234

Reid, Y., Johnson, S., Morant, N., Kuiperse, E., Szmukler, G., & Thornicroft, G. et al. (1999) Explanations for stress and satisfaction in mental health professionals: a qualitative study. Social Psychiatry and Psychiatric Epidemiology, 34, 301-308.

Reuven G. (1986). Unit Morale: From a Theoretical Puzzle to an Empirical Illustration: An Israeli Example. Journal of Applied Social Psychology, 16, 6, 549-64.

Rizzo, J.R., House, R.J., & Lirtzman, S.I. (1970). Role conflict and ambiguity in complex organizations. Administrative Science Quarterly, 15, 150–163.

Roberts, S. J., DeMarco, R., & Griffin, M. (2009). The effect of oppressed group behaviours on the culture of the nursing workplace: a review of the evidence and interventions for change. Journal of Nursing Management, 17, 3, 1-6.

Rose, D. (2001) Users' Voices, The perspectives of mental health service users on community and hospital care. London: The Sainsbury Centre

Russell,J. (1980) “A circumplex model of affect” Journal of Personality and Social Psychology vol. 39 pp 1161-1178

Ryan, E.P., Aaron, J., Burnette, M.L., Warren, J., Burket, R., & Aaron, T. (2008). Emotional responses of staff to assault in a pediatric state hospital. Journal of the American Acadamy of Psychiatry and the Law, 36, 3, 360-368.

Sa, L. & Fleming, M. 2008, "Bullying, burnout, and mental health amongst Portuguese nurses", Issues Ment.Health Nurs., vol. 29, no. 4, pp. 411-426.

Sainsbury Centre for Mental Health (1998). Acute problems: a survey of the quality of care in acute psychiatric wards. London: Sainsbury Centre for Mental Health Centre.

Sainsbury Centre for Mental Health (2000) Finding and keeping. London: Sainsbury Centre for Mental Health.

Schaufeli, W, Bakker, A. and Salanova, M. (2006) The measurement of work engagement with a short questionnaire. A cross-national study. Educational and Psychological Measurement 66: 701-716.

Sexton, J. B., Thomas, E. J., & Helmreich, R. L. (2000). Error, stress, and teamwork in medicine and aviation: cross sectional surveys. British Medical Journal, 320, 7237, 745-749.

Siebold, G L. (1999). The Evolution of the Measurement of Cohesion. Military Psychology, 11, 1, 5 -26.

Sikorska-Simmons, E. (2006). Organizational culture and work-related attitudes among staff in assisted living. Journal of Gerontological Nursing, 32, 2, 19-27.


235

Singh, S. P. (2000). Running an effective community mental health team. Advances in Psychiatric Treatment, 6, 6, 414-422.

Snijders, T. Bosker, R. (1999) Multilevel Analysis: An Introduction to basic and Advanced Multilevel Modelling Sage Publications Ltd

Steinert, T., Eisele, F., Goeser, U, Tschoeke, S., Uhlmann, C. and Schid, P. (2008) Successful interventions on an organisational level to reduce violence and coercive interventions in in-patients with adjustment disorders and personality disorders. Clinical Practice and Epidemiology in Mental Health 4:27

Tansella, M. and Thornicroft, G. (2009) Implementation science: understanding the translation of evidence into practice. 195: 283-285.

Tummers GER et al. (2001) A comparative study of work characteristics and reactions between general and mental health nurses: a multi-sample analysis. Journal of Advanced Nursing 36 : 151-162

Van der Doef, M., & Maes, S. (1999). The job demand-control (-support) model and psychological well-being: A review of 20 years of empirical research. Work & Stress, 13, 87-114.

Warr, P. (1990). The measurement of well-being and other aspects of mental health. Journal of Occupational Psychology, 63, 193-210.

Warr P (2007) Work, happiness, and unhappiness. Lawrence Erlbaum Associates, Mahwah NJ

Watson,D. & Tellegren,A. (1985) “Toward a consensual structure of mood” Psychological Bulletin vol. 83 pp 185-197

Whittington, R., Shuttleworth, S., & Hill, L. (1996). Violence to staff in a general hospital setting. Journal of Advanced Nursing, 24, 2, 326-33.

Wood, S. and O’Creevy, M.F. (2005) Direct involvement, Representation and Employee Voice in UK Multinationals in Europe, European Journal of Industrial Relations, 11: 27-50

Wood S (2008) Job characteristics, employee voice and well-being in Britain. Ind Rels J 39(2): 153–168

Wykes, E. (1994). Violence and health care professionals. London: Chapman & Hall.

Wykes, T., Stevens, W. and Everitt, B. (1997) Stress in community care teams: will it affect the sustainability of community care? Social Psychiatry and Psychiatric Epidemiology. 32: 398-407.

Zapf, D. (2003). Empirical findings on bullying in the workplace. In: S. Einarsen, H. Hoel, D. Zapf, & C. L. Cooper. (Eds.), Bullying and Emotional Abuse in the Workplace: International Perspectives in


236

Research and Practice (pp. 103-126). New York, NY: Taylor & Francis.


237

Appendix 1

IPSM Module 3

Focus Group Interview Guide A

Initial tasks: Introductions Brief overview of what the Focus Group today will involve

Information Sheets/questions Consent Forms Confidentiality

1. How data used (not in way that anyone can beidentified). Check first names ok (explain they will not be transcribed).

2. Confidentiality within the group (ask participants not to discuss what is said today outside the group).

____________________________________________________________________

Start digital recorder (noting ID of ward and Focus Group date) To start, ask each person to say:

- Their first name

- Job title

- How long they have worked on the ward and

- How long they have worked in Mental Health Services.

- [Co-facilitator to note these data]


238

IPSM Module 3 - Focus group interview guide A

Version 3 [FINAL] – Nov 08

1. What are the most important positive influences on morale on your ward?

2. What are the most important negative influences?

Talking wall to be used to generate ideas for these two questions, which will then be

explored in the discussion which follows. The following are prompts to be used if the

areas in question do not spontaneously come up:

Prompts: Management/supervision Support Relationships with colleagues Training The way work is organised on the ward/Responsibilities Safety/violence/abuse Pay/conditions (inc working patterns) The patient group on the ward Resources Physical Environment

3. How does morale on the ward impact on patient care?

4. What could be done to (maintain or) improve morale on the ward?

Prompts to use if necessary:

- What could the ward manager and other senior ward staff do?

- What could others in the staff team do?

- What could hospital or Trust senior management do?

- What could politicians and others who direct the NHS do?

5. Co-facilitator/facilitator to offer brief summary of main points made in

discussion, and participants asked if they think it is a reasonably accurate summary and/or if there’s anything else they’d like to add.


239

IPSM Module 3

Individual Interview Guide B – Staff

Initial tasks:

Information Sheet/questions Consent Form Test and start digital recorder (noting ID of interviewee and interview date)

Introductory Questions:

i) Can you confirm the name of the ward you work on?

ii) Can you tell me your job title?

(If not clear from job title, what is professional background?)

iii) How long have you been working on this ward?

iv) Is that full time? (If not - approx how many hrs a week?)

v) How long have you worked in Mental Health Services?

vi) [Ask participants questions from demographics sheet on nextpage]


240

IPSM Module 3

Individual Interview Guide B– Staff

DEMOGRAPHIC information:

Participant ID: ______________________ Date :__________

Gender: Male ❒ Female ❒

Q1 – What is your age group?

18-25 ❒ 46-55 ❒

26-35 ❒ 56-65 ❒

36-45 ❒ 66+ ❒

Q2 - What is your ethnic group?

❒ White

❒ British

❒ Irish

❒ Any other White background

Black/Black British

❒ Caribbean

❒ African

❒ Any other Black background

Mixed/Other Group

❒ White and Black Caribbean

❒ White and Black African

❒ White and Asian

❒ Any other mixed background

❒ Any other ethnic group (please describe)


241

Asian/Asian British

❒ Indian

❒ Pakistani

❒ Bangladeshi

❒ Chinese

❒ Any other Asian background


242

IPSM Module 3 - Individual interview guide B – Staff Version 5 [FINAL] – 04/09/08

1. How do you feel about your work on the ward?

Prompt for positives/negatives as appropriate

2. What aspects of your work have most influence on how you feel about

your job?

___________________________________________________________________

Prompts if they have not already arisen:

Available resources (e.g. activities for patients on the ward and elsewhere in the

hospital, specialist treatments, benefits and housing advice) Pay and conditions Changes that have been made while you’ve been working in this Trustand in the NHS

as a whole The physical environment on the ward and its facilities The hospital as a whole and its atmosphere, facilities and surroundings The supervision you receive The support you receive at work (from supervisor, manager, other colleagues, patients

etc) The way the ward is managed Relationships with colleagues on the ward Relations between the ward and other services Training opportunities Safety, abuse and violence at work Discrimination at work Characteristics of the patients who are admitted to the ward (inc. clinical and social) The type of work you are expected to do The amount of control you have over how you work Visitors to the ward Organisation of work on the ward (e.g. protocols for assessing and managing patients) The way difficult decisions in patient management are made

3. Does how you feel at work have an impact on the interactions you have

with patients and the care you provide?

4. What (other) improvements do you think could be made that would have a

positive impact on you at work?

5. Has this job turned out as you expected when you first took it? (i.e. Have


243

your expectations of the job been fulfilled?)

6. Does how you feel at work have an impact of your physical health?

7. Is there anything else you would like to add that you feel is relevant to

your morale at work?


244

IPSM Module 3

Individual Interview Guide C – Senior Manager

Initial tasks:

Information Sheet/questions Consent Form Test and start digital recorder (noting ID of interviewee and interview date)


i) Can you tell me your job title? And what is your professional background?

ii) How long has it been part of your job to work with this ward?

iii) What is your role (in your work with the ward)?

iv) How many wards do you work with in this role?

v) How long have you worked in Mental Health Services?

vi) [Ask participants questions from demographics sheet]


245

IPSM Module 3

Individual Interview – Senior Management



Gender: Male ❒ Female ❒


18-25 ❒ 46-55 ❒

26-35 ❒ 56-65 ❒

36-45 ❒ 66+ ❒


❒ White

❒ British

❒ Irish

❒ Any other White background

Black/Black British

❒ Caribbean

❒ African

❒ Any other Black background

Mixed/Other Group

❒ White and Black Caribbean

❒ White and Black African

❒ White and Asian

❒ Any other mixed background

❒ Any other ethnic group (please

describe)

……………………………………….


246

Asian/Asian British

❒ Indian

❒ Pakistani

❒ Bangladeshi

❒ Chinese

❒ Any other Asian background


247

IPSM Module 3 - Individual interview guide C – Senior Manager

Version 2 [FINAL] – 5 Feb 09

1. How good or bad do you feel morale currently is on __________ ward?

2. What do you think are the most important positive influences on the

ward’s morale?

3. What do you think are the most important negative influences?

___________________________________________________________________

Prompts if they have not already arisen:

Available resources (e.g. activities for patients on the ward and elsewhere in the

hospital, specialist treatments, benefits and housing advice) Staff pay and conditions Changes or cuts affecting services and working conditions – in this Trust or in the

NHS as a whole The physical environment on the ward and its facilities The hospital as a whole and its facilities and surroundings The overall atmosphere of the Trust and of the hospital Senior management support for the ward The quality of management and supervision on the ward, including theward

manager’s role Organisation of work on the ward, including whether there are clearprocedures for

managing patients and for staff support and supervision How well the ward staff get on and work together as a team Support systems for staff, such as ward support and community groups Opportunities for staff to receive training and go to meetings Relations between the ward and other services Characteristics of the patients who are admitted to the ward (inc. clinical and social) The way difficult decisions in patient management are made

3. How do you think morale on the ward impacts on patient care?

4. What do you think could be done to maintain or achieve good morale

on the ward?

Prompt for:

- What could the ward manager and other senior ward staff do?

- What could others in the staff team do?

- What could hospital or Trust senior management do?

- What could politicians and others who direct the NHS do?

5. Is there anything else you would like to add that you feel is relevant to

staff morale on _____________ ward?


248

IPSM Module 3

Individual Interview Guide D – Patients

Initial tasks:

Information Sheet (any questions) Consent Form Test and start digital recorder (noting ID of interviewee and interview date)


i) Can you confirm the name of the ward you are (or have been) staying on? ii) How long have you been on this ward? iii) Did you come on to the ward as a compulsory or a voluntary patient? iv) Is this the first time you have been on an inpatient ward?

If no, have you been an inpatient more than 5 previous times?

v) [Ask participants questions from demographics sheet on next page]


249

IPSM Module 3

Individual Interview – Patients



Gender: Male � Female �


18-25 � 46-55 �

26-35 � 56-65 �

36-45 � 66+ �


White

� British

� Irish

� Any other White background

Black/Black British

� Caribbean

� African

� Any other Black background

Mixed/Other Group

� White and Black Caribbean

� White and Black African

� White and Asian

� Any other mixed background

� Any other ethnic group (please describe)

……………………………………….


250

Asian/Asian British

� Indian

� Pakistani

� Bangladeshi

� Chinese

� Any other Asian background


251

IPSM Module 3 - Individual interview guide D – Patients

Version 6 [FINAL] – 04/09/08

1. Can you tell me about your contact with staff on this ward?

Prompt for best things/worst things about this, as appropriate

2. How do you think staff on the ward feel about their work?

3. Does the way staff feel about their work on the ward make a difference

to the care and treatment you get?

______________________________________________________________

4. Can you suggest any changes that would make life on the ward better

for patients or staff or both?

Prompt for:

Changes to:

Staff working hours and the numbers of staff who work each shift The professional background and experience of staff who are recruited to work

on the ward The way the ward team is managed The way staff work together as a team The way staff relate to patients Rules and routines for patients on the ward The meetings you have with staff (including one-to-one meetings and bigger

meetings such as CPAs and ward rounds) The amount of time staff have to spend with patients The other people who are patients on the ward (e.g. the balance between men and

women, the kinds of problems people have) Groups and activities on the ward Your physical surroundings and the facilities on the ward

(including physical environment, domestic facilities, provision for activities) The environment of the hospital as a whole and its surroundings Rules for visitors on the ward (e.g. visiting hours, where visitors are allowed to go

on the ward, how staff deal with any difficulties arising with visitors) Staff contact with family and friends Whether staff and patients feel safe on the ward


252

Appendix tables Table A1. Associations between adverse events and burnout variables

Outcome variable Stage 1 Adverse events

variables in simple model*,

entered individually and

unadjusted


Stage 2a

Individual adverse

events adjusted

for demographic

and occupational

variables**

Stage 2b Adverse

events entered

together in model

adjusted for

demographic and

occupational

variables***


model that includes adverse events

and demand, support control

variables

Emotional

exhaustion

Negative value of


association with

lower burnout

N=1797 final model

Individual reports:

Bullied in last year: 8.0 (6.8 to

9.2)P<0.0005

Discriminated against: 5.1 (4.0

to 6.3) P<0.0005

Frequency of threats of

violence: 1.9 (1.6 to 2,2)

P<0.0005

Not significant: (Team

manager reports): Number of

violent incidents against staff,

Any suicide, Any serious

incident enquiry, Any staff

member sick for more than a

week after assault, Drug

dealing on ward

Individual reports:

Bullied in last year:

7.6 (6.4 to 8.9)

P<0.0005

Discriminated

against: 5.2 (3.9 to

6.4) P<0.0005

Frequency of threats

of violence: 2.1 (1.8

to 2.4) P<0.0005

Individual reports:

Bullied in last year: 6.0

(4.7 to 2.3) P<0.0005

Discriminated against:

2.2 (0.9 to 3.4) P=0.001


violence: 1.8 (1.5 to 2.1)

P<0.0005

Bullied in past year: (3.6 (2.6 to 4.6)

p<0.0005

Frequency of threats of violence: (1.2 (1.0

to 1.5) p<0.0005

Work demands: (6.0 (5.5 to 6.4)

p<0.0005)

Job control: (-1.5 (-2.1 to -0.9)

p<0.0005)

Support from manager: (-0.8 (-1.2 to -

0.4) p<0.0005)

Support from colleagues: (-0.7 (-1.2 to -

0.3) p=0.0005)

Service type: More exhaustion in CMHT

(4.3 (2.6 to 6.1) p<0.0005) and CRT (1.9

(0.2 to 3.6 p=0.03)


253




unadjusted


Stage 2a

Individual adverse

events adjusted

for demographic

and occupational

variables**

Stage 2b Adverse

events entered

together in model

adjusted for

demographic and

occupational

variables***




variables

Occupational group: More exhaustion in

psychiatrists (p=0.003)

Not now significant: discrimination, ethnic

group, hours worked, time in career and

ward.

Cynicism

Negative value of


association with

lower burnout

N=1720 final model

Individual reports:


Discriminated against: 1.5 (1.0 to 2.1) P<0.0005

Frequency of threats of violence: 0.8 (0.6 to 0.9) P<0.0005


Any staff member sick for

more than a week after

assault: 0.7 (0.0 to 1.3)

P=0.05


manager reports) Number of



Individual reports:

Bullied in last year:

1.9 (1.3 to 2.4)

P<0.0005

Discriminated

against: 1.6 (1.1 to

2.2) P<0.0005


of violence: 0.8 (0.6

to 0.9) P<0.0005

Individual reports:

Bullied in last year: 1.1

(0.5 to 1.7) P<0.0005


0.8 (0.2 to 1.4) P=0.012


violence: 0.7 (0.6 to 0.9)

P<0.0005

Frequency of threats of violence: (0.6 (0.4

to 0.7) p<0.0005)

Work demands: (1.5 (1.2 to 1.7 to

p<0.0005)

Job control: (- 0.7 (-1.0 to -0.4)

p<0.0005)

Support from manager: (-0.3 (-0.5 to –

0.06) p=0.01)

Service type: More cynicism in PICU (1.2

(0.2 to 2.2) p=0.03) and CRT (1.2 (0.3 to

2.1) p=0.01, less in Rehab (-1.2 (-2.2 to -

- 0.1) p=0.03)

Occupational group: Less cynicism in 45-

54 (-1.7 (-2.8 to -0.5) p=0.004) and 55+

age groups 2.6 (-3.8 to -1.3) (p<0.0005),


254




unadjusted


Stage 2a

Individual adverse

events adjusted

for demographic

and occupational

variables**

Stage 2b Adverse

events entered

together in model

adjusted for

demographic and

occupational

variables***




variables



week after assault, Drug

dealing on ward

Less cynicism in Asian (-1.4 (-2.3 to -0.9),

p=0.002) and Black groups (-1.6 (-2.3 to

-0.9) p<0.0005), More cynicism in men

(1.6 (1.1 to 2.1) p<0.0005)

Not now significant: bullying,

discrimination, support from colleagues,

occupational group, marital status

Accomplishment

Positive value of


association with

lower burnout

N=1533 final model

Not significant (Individual

reports)

Being bullied in past year

Discriminated against


violence

(Team manager reports)

Number of violent incidents

against staff

Any suicide

Any serious incident enquiry

Any staff member sick for

No variables

significant in

unadjusted

analysis

No variables

significant in

unadjusted analysis

Unchanged from model in previous

chapters


255




unadjusted


Stage 2a

Individual adverse

events adjusted

for demographic

and occupational

variables**

Stage 2b Adverse

events entered

together in model

adjusted for

demographic and

occupational

variables***




variables

more than a week after assault

Drug dealing on ward

*As in Section 4.1, throughout multilevel regression has been used to explore associations with outcome variables, using the xtmixed command in Stata 10. The two levels are individual and ward, with individuals nested within wards. Because of this, anonymous respondents to whom no ward can be assigned are omitted.

**Entered in each model alongside the individual adverse events variables are the demographic and occupational variable that were found to be significant in models for the outcome measure in question as presented in Section 4.1, Table 6

***In this column, all the adverse events variables that are significant when entered individually at Stage 2 are entered together along with the same demographic and occupational variables as in Stage 2.


256

Table A2. Associations between adverse events and job-related well being variables

Outcome

variable

Stage 1 Adverse events

variables in simple model,


unadjusted


Stage 2a Individual

adverse events

adjusted for

demographic and job-

related variables

Stage 2b Adverse

events entered

together in model

adjusted for

demographic and

job-related variables




Anxiety-

contentment

scale

Positive

coefficient

means

characteristic

is associated

with higher

contentment,

lower anxiety

N=1693 for

final model

Individual reports:

Bullied in last year: -0.42 (-0.49 to -0.35) P<0.0005

Discriminated against: -0.31 (-0.38 to -0.24) P<0.0005

Frequency of threats of violence: -0.08 (-0.11 to - to -0.07) P<0.0005


Drug dealing on ward -0.13 (-0.23 to -0.03) P=0.012

Not significant: (Team manager

reports) Number of violent

incidents against staff, Any

suicide, Any serious incident

enquiry, Any staff member sick

for more than a week after

assault

Individual reports:

Bullied in last year: -

0.41 (-0.49 to -

0.34)P<0.0005

Discriminated against: -

0.34 (-0.42 to -0.27)

P<0.0005


violence: -0.10 (-0.12 to

-0.08) P<0.0005

Individual reports:

Bullied in last year:-

0.30 (-0.38 to -0.22)

P<0.0005


-0.20 (-0.27 to -0.12)

P<0.0005


violence: -0.08 (-0.10

to -0.06) P<0.0005


p<0.0005


0.07 to -0.03) p<0.0005

Work demands: -0.22 (-0.25 to -0.19)

p<0.0005

Job control: 0.15 (0.11 to 0.19) p<0.0005

Support from manager: 0.07 (0.04 to 0.09)

p<0.0005

Support from colleagues: 0.08 (0.05 to 0.12)

p=0.0005

Service type : Greater contentment/less

anxiety in forensic (0.11 (0.01 to 0.22)

p=0.04) and older people’s wards (0.17

(0.05 to 0.29),p=0.007), less in CMHTs (-

0.28 (-0.39 to -0.17, ,p<0.0005)

Occupational group: Less

contentment/greater anxiety in psychiatrists

(-0.15 (-0.27 to -0.40) p=0.01)

Time on ward: Less contented/more anxious


257

Outcome

variable




unadjusted


Stage 2a Individual

adverse events

adjusted for


related variables

Stage 2b Adverse

events entered

together in model

adjusted for

demographic and





after 1-2 years (-0.11 (-0.20 to -0.03)

p=0.009) or 2-5 years (-0.15 (-0.24 to -

0.07), p=0.001)

Not now significant: ethnic group, , time in

mental health services and in ward, bank or

agency contract.

Depression-

enthusiasm

scale

Positive

coefficient

means

characteristic

is associated

with higher

enthusiasm

lower

depression

Individual reports:

Bullied in last year: -0.51 (-0.59

to -0.44) P<0.0005

Discriminated against: -0.37 (-

0.44 to -0.29) P<0.0005

Frequency of threats of violence:

-0.09 (-0.10 to - to -0.07)

P<0.0005

No significant association with:

(Team manager reports) Number

of violent incidents against staff,

Any suicide, Any serious incident

enquiry, Any staff member sick

for more than a week after

assault

Individual reports:

Bullied in last year -0.49

(-0.57 to -0.40)

P<0.0005

Discriminated against: -

0.38 (-0.46 to -0.30)

P<0.0005


violence -0.08 (-0.10 to

- to -0.06) P<0.0005

Individual reports:


0.37 (-0.45 to -0.28)

P<0.0005

Discriminated against -

0.21 (-0.30 to -0.13)

P<0.0005


violence: -0.06 (-0.08

to -0.04) P<0.0005


p<0.0005


0.04 to -0.00) p<0.000

Work demands: -0.18 (-0.21 to -0.15)

p<0.0005

Job control: 0.21 (0.17 to 0.25) p<0.0005

Support from manager: 0.08 (0.05 to 0.11)

p<0.0005


p=0.0005

Service type: Less enthusiastic/more


258

Outcome

variable




unadjusted


Stage 2a Individual

adverse events

adjusted for


related variables

Stage 2b Adverse

events entered

together in model

adjusted for

demographic and





depressed in CMHTs (-0.26 (-0.38 to -0.13)

Time on ward: Less enthusiastic/more

depressed after 1-2 years (-0.22 (-0.31 to -

0.13) p<0.0005) or 2-5 years (-0.26 (-0.36

to -0.16), p<0.0005) or more than 5 years (-

0.23 (-0.33 to -0.12), p<0.0005)

Age: 55+ age group more enthusiastic/less depressed (0.24 (0.06 to 0.42) p=0.01)

More enthusiastic/less depressed if in senior

position (0.22 (0.11 to 0.32) p<0.0005)

Not now significant: ethnic group, marital

status, time in mental health services and in

ward, bank or agency contract.


259

Table A3. Associations between adverse events and intrinsic satisfaction

Outcome

variable




unadjusted (coefficient,

(95% CI) p=)

Stage 2a Individual

adverse events

adjusted for

demographic and

job-related

variables

Stage 2b Adverse

events entered

together in model

adjusted for

demographic and

job-related

variable

Stage 3 Significant variables in final model that

includes adverse events and demand, support

and control variables

Intrinsic

satisfaction

Positive

coefficient

means

characteristic

is associated

with higher

job

satisfaction

N=

Individual reports:

Bullied in last year: -0.46 (-

0.54 to -0.38) P<0.0005

Discriminated against: -0.39 (-

0.47 to -0.31) P<0.0005


violence: -0.08 (-0.10 to - to -

0.06) P<0.0005


Number of violent incidents

against staff: -0.01 (-0.02 to -

0.00) P=0.01


manager reports) Number of





week after assault

Individual reports:


0.49 (-0.57 to -0.40)

P<0.0005


-0.42 (-0.51 to -0.34)

P<0.0005


of violence: -0.08 (-

0.10 to - to -0.05)

P<0.0005


Number of violent

incidents against staff:

-0.01 (-0.02 to -0.00)

P=0.02

Individual reports:

Bullied in last year:-

0.33 (-0.43 to -

0.23): P<0.0005

Discriminated

against: -0.31 (-0.41

to -0.21) P<0.0005


of violence: -0.04 (-

0.07 to -0.02)

P=0.001

Bullied in past year: -0.13 (-0.20 to -0.05) p=0.001

Discriminated against: -0.12 (-0.19 to -0.04) p=0.002

Work demands: -0.14 (-0.18 to -0.11) p<0.0005

Job control: 0.13 (0.10 to 0.16) p<0.0005

Support from manager: 0.13 (0.10 to 0.16) p<0.0005


p<0.0005

Ethnic group: eater satisfaction among Black (0.21

(0.12 to 0.30), p<0.0005) and Asian (0.16 (0.06 to

0.28), p=0.002) groups

Service type: less satisfaction in CMHTs -0.19 (-0.32

to -0.06) p=0.003

Occupational group: less satisfaction in psychologists

-0.23 (-0.45 to -0.01) p=0.04.

Not now significant: frequency of threats of violence,

marital status, bank or agency contract, senior

position.


260

Table A4. Associations between organisational variables and Maslach burnout inventory variables

Outcome

variable

Stage 1 Testing of associations for individual variables:

Organisational variables in simple model* entered

individually and unadjusted


Stage 2 Adjusted model

– Significant variables

fro Stage 1 entered

together along with

significant demographic

and occupational

variables from Chapter 3

models. Only significant

organisational variables

shown

Stage 3 – Significant variables

in final model (as for stage 2

with control variables also

entered)

Emotional

exhaustion

Negative value

of coefficient

indicates

association with

lower burnout

N=1646 final

model


Role clarity: -5.9 (-6.6 to -5.2, p<0.0005)






Voice: how much are senior managers influenced?: -3.2 (-3.8 to

-2.2, p<0.0005)


Satisfaction with supervision & appraisal: -2.7 (-3.2 to -2.2, ,

p<0.0005)

Has had an appraisal in past year: -1.2 (-2.3 to -0.0, p=0.05)

Has a personal development plan: -2.0 (-3.1 to -0.9)


Role clarity: -1.4 (-2.2 to -

0.5, p=0.002)

Role conflict: 3.0 (2.3 to

3.7, p<0.0005)

Team conflict: 1.2 (0.4 to

2.0, p=0.003)

Team communication: -1.1

(-1.9 to -0.3, p<0.0005)

Substantive fairness: - 3.2

(- 4.0 to -2.4, p<0.0005)

Role conflict: 1.9 (1.3 to 2.5,

p<0.0005)

Team communication: -1.5 (-2.3

to -0.8 p<0.0005)

Substantive fairness: -2.1 (-2.8 t0

-1.4 P<0.0005)

Work demands: 4.5 (3.9 to 5.1,

p<0.0005)

Job control: -0.8 (-1.4 to -0.2,

p=0.01)

Service type - More exhaustion in

CMHT (2.0 (0.2 to 3.8, p=0.03)

Occupational group - More

exhaustion in psychiatrists (3.4,

1.5 to 5.3, p=0.003)

Ethnic group: More exhaustion in


261

Team manager level

Whether all staff have appraisal: -2.4 (-3.8 to -1.0, p=0.001)

Adoption of protected therapeutic time: 2.1 (0.5 to 3.6,

p=0.001)

Not significant:

Individual: Days of training, number of supervision sessions.

Team manager reports: Availability of staff support group,

ward meetings, named supervisors for all staff, having had an

away day. Availability of modern matron, lead consultant,

housekeeper, modern matron, acute care forum or practice

development nurse. Adoption of specific philosophy of

care/therapeutic model. Team manager rating of support from

senior management.

Black staff (2.6 (1.2 to 4.0).

p<0.0005)

Significant organisation and

control variables from previous

model that are not now

significant: role clarity, team

conflict, manager and colleague

support, hours worked, time on

ward and in service

Cynicism

Negative value

of coefficient

indicates

association with

lower burnout

N=1715 final

model



Role clarity: -2.0 (-2.3 to -1.6, p<0.0005)




Substantive fairness: - 1.7 (2.0 to -1.4, p<0.0005)


Voice: how much are senior managers influenced? -1.0 (-1.3 to -

0.8, p<0.0005)


(Stage 2 – significant

stage 1 variables in

model with significant

demographic and

occupational variables:

Role clarity: -0.6 (-1.0 to -

0.2, p=0.004)

Role conflict: 1.2 (0.8 to

1.6, p<0.0005)

Team conflict; 0.8 (0.3 to

1.2, p<0.0005)

Team communication: -0.6

(-1.0 to -0.1, p=0.01)

(Stage 3 – Demand, support,

control variables also added)

Role conflict: 1.9 (1.3 to 2.5,

p<0.0005)

Team communication: -1.5 (-2.3

to -0.8 p<0.0005)

Work demands: 4.5 (3.9 to 5.1,

p<0.0005)

Job control: -0.8 (-1.4 to -0.2,

p=0.01)

Service type - More exhaustion in


262

Satisfaction with supervision & appraisal: -0.9 (-1.2 to -0.7 ,

p<0.0005)

Has a personal development plan: -0.8 (-1.3 to -0.3) p=0.003

Not significant: Individual: Days of training, number of

supervision sessions, whether has had appraisal.

Team manager reports: Availability of staff support group,

ward meetings, named supervisors for all staff, having had an

away day, whether all staff have appraisal.. Availability of

modern matron, lead consultant, modern matron, acute care

forum or practice development nurse. Adoption of specific

philosophy of care/therapeutic model or of protected therapeutic

time. Team manager rating of support from senior management.

CMHT (2.0 (0.2 to 3.8, p=0.03)

Ethnic group - More exhaustion in

Black staff (2.6 (1.2 to 4.0).

p<0.0005)

Not now significant ole clarity,

team conflict, manager and

colleagues

Personal

Accomplishment

Positive value

of coefficient

indicates

association with

lower burnout

N=1664 final

model



Role clarity: 3.7 (3.2 to 4.1, p<0.0005)

Role conflict: -1.2 (-1.5 to -0.8, p<0.0005)

Team conflict: -1.4 (-1.9 to -0.9, p<0.0005)

Team communication: 2.5 (2.0 to 2.9, p<0.0005)

Substantive fairness: 1.9 (1.5 to 2.4, p<0.0005)

Procedural fairness: 1.5 (1.0 to 1.9, p<0.0005)

Voice: how much are senior managers influenced?: 1.5 (1.1 to

1.8, p<0.0005)



stage 1 variables in

model with significant

demographic and

occupational variables)

Role clarity: 2.7 (2.0 to

3.4), p<0.0005

Team communication: 1.1

(0.5 to 1.9), p<0.0005


Whether all staff have

appraisal: 1.1 (0.2 to 2.0),

p=0.01

(Stage 3 – Demand, support,

control variables also added)

Role clarity: 2.6 (1.0 to 3.2),

p<0.0005

Job control: 1.5 (1.0 to 2.0),

p<0.0005

Support from colleagues: 0.9 (0.5

to 1.4), p<0.0005

Service type- Greater

accomplishment on CAMHS wards

(1.8 (0.3 to 3.2), p=0.02) and in

CRTs (1.5 (0.1 to 3.0), p=0.04

Time in mental health services -


263

Satisfaction with supervision & appraisal: 1.4 (1.1 to 1.8 ,

p<0.0005)

Number of supervisions in past 6 months: 0.17 per meeting

(0.09 to 0.24), p<0.0005



Days of training in last year 0.09 per day (0.04 to 0.13),

p<0.0005


Whether all staff have appraisal: 1.1 (0.2 to 2.0),

p=0.02Regular staff support group: 1.0 (0.1 to 2.0), p=0.03

Not significant -Team manager reports: Availability of ward

meetings, named supervisors for all staff, having had an away

day. Avaiilability of modern matron, lead consultant, modern

matron, acute care forum or practice development nurse.

Adoption of specific philosophy of care/therapeutic model or of

protected therapeutic time. Team manager rating of support

from senior management.

Lower accomplishment after 10-14

years (-2.0 (-3.6 to -0.4),

p=0.02) Or 15+ years (-1.9 (-3.4

to -0.4), p=0.02)

Occupation - Greater

accomplishment among team

managers (1.8 (0.1 to 3.6),

p=0.03

Not now significant: Team

communication, work demands,

manager support, ethnic group,

whether staff have appraisals



264

Table A5. Associations between organisational variables and job-related well-being

Outcome

variable

Stage 1 Testing of associations for individual

variables: Organisational variables in simple

model*, entered individually and unadjusted


Stage 2 Adjusted model –

Significant variables fro

Stage 1 entered together

along with significant

demographic and

occupational variables

from Chapter 3 models.

Only significant


shown

Stage 3 Significant variables in final model

– as for Stage 2 with demand support

control variables also entered. Only

significant variables shown

Anxiety-

contentment

Positive

value of

coefficient

indicates

association

with lower

anxiety /

greater

contentment

N= 1683

final model


Role clarity: 0.36 (0.32 to 0.40), p<0.0005


Team conflict: 5.3 (4.6 to 5.9, p<0.0005




Voice: how much are senior managers influenced?:

0.19 (0.11 to 0.26) p<0.0005

Satisfaction with training: 0.27 (0.24 to 0.31,

p<0.0005)

Satisfaction with supervision & appraisal: 0.16 (0.13

to 0.19), p<0.0005


0.17), p<0.0005

Role conflict: -0.15 (-0.19 to

-0.10), p<0.0005


(0.04 to 0.14) p=0.001

Substantive fairness: 0.13

(0.08 to 0.18), p<0.0005

Procedural fairness: 0.07

(0.02 to 0.13), p=0.006

Satisfaction with training:

0.06 (0.02 to 0.11),

p<0.0005

Role conflict : -0.10 (-0.13 to -0.06) p<0.0005

Team communication: 0.06 (0.01 to 0.10),

p=0.01


Procedural fairness; 0.05 (0.01 to 0.10) p=0.02

Work demands: -0.15 (-0.18 to -0.11),

p<0.0005

Job control: 0.12 (0.08 to 0.16), p<0.0005

Support from colleagues: 0.06 (0.03 to 0.09),

p<0.0005

Service type - More contentment/less anxiety in

rehab (0.13 (0.01 to 0.25), p=0.04), less

contentment/more anxiety in CMHTs (-0.17 (-

0.28 to -0.7), p=0.001


265

Has had an appraisal in past year: 0.13 (0.06 to

0.19), p<0.0005

Has a personal development plan: 0.16 (0.09 to

0.22), p<0.0005

Number of supervisions in past 6 months: 0.007 per

meeting (0.001 to 0.013)

Team manager level

Whether all staff have appraisal: -0.10 (-0.19 to -

0.00), p< 0.04

Not significant- Individual: Days of training

Team manager reports: Availability of staff

support group, ward meetings, named supervisors

for all staff, having had an away day. Availability of

modern matron, lead consultant, acute care forum

or practice development nurse. Adoption of specific

philosophy of care/therapeutic model or of protected

therapeutic time. Team manager rating of support

from senior management.

Occupational group - More anxiety/less

contentment among psychiatrists (-0.18 (-0.30

to -0.67, p=0.002

Time on ward - More anxiety/less contentment

after 1-3 (-0.09 (-0.17 to 0.01), p<0.03) or 3-5

years (0.22 to 0.05) p=0.002) on ward

Significant variables from previous model

that are not now significant: role clarity,

team conflict, manager support, ethnic group,

time in mental health services, bank/agency

status

Depression-

enthusiasm

Negative

value of

coefficient

indicates

greater

depression /



Role clarity: 0.40 (0.35 to 0.44), p<0.0005





stage 1 variables in model

with significant

demographic and

occupational variables:


0.20), p<0.0005

(Stage 3 – Demand, support, control

variables also added)

Role clarity: 0.05 (0.00 to 0.10), p=0.02



p<0.0005


266

less

enthusiasm

N=1536

Substantive fairness: 0.39 (0.35 to 0,43) p<0.0005

Procedural fairness: 0.37 (0.33 to 0.41) p<0.0005

Voice: how much are senior managers influenced?

0.25 (0.21 to 0.29, p<0.0005)

Satisfaction with training: 0.33 (0.30 to 0.37),

p<0.0005)

Satisfaction with supervision & appraisal; 0.21 (0.18

to 0.24) p<0.0005


0.31) p<0.0005

Has had an appraisal: 0.17 (0.10 to 0.24),

p<0.0005

Number of supervision meetings: 0.0015 (0.0009 to

0.0022) per meeting, p< 0.0005

Days of training in past year: 0.005 per day (0.0005

to 0.009), p=0.03


Whether all staff have appraisals: 0.13 (0.03 to

0.35), p<0.006

Staff away day in past year: 0.11 (0.01 to

0.20), p=0.04

Role conflict -0.11 (-0.15 to

-0.06), p<0.0005

Team communication 0.13

(0.07 to 0.18) p<0.0005

Substantive fairness 0.10

(0.05 to 0.15), p<0.0005

Procedural fairness 0.06

(0.01 to 0.12), p=0.03

Satisfaction with training

0.10 (0.06 to 0.15),

p<0.0005

Has a personal development

plan 0.10 (0.00 to 0.21),

p=0.05


Work demands: -0.10 (-0.13 to -0.07),

p<0.0005

Job control: 0.15 (0.11 to 0.19), p<0.0005

Support from colleagues: 0.06 (0.03 to 0.10),

p<0.0005


0.13), p=0.04

Service type - More depression/less enthusiasm

in CMHTs (-0.19 (-0.31 to -0.07), p=0.001

Time on ward - More depression/less enthusiasm

after 1-3 (-0.18 (-0.27 to -0.09), p<0.0.0005),

3-5 years (-0.23 (-0.32 to -0.13), p<0.0005 or

5+ years on ward (-0.21 (-0.31 to -0.07)

p<0.0005) (all compared with under 1 year)

Time in mental health services - More

depression/less enthusiasm after more than 15

years (-0.21 (-0.35 to -0.06), p=0.005)

More enthusiasm/less depression if senior grade

(0.21 (0.11 to 0.30), p<0.0005)

Ethnic group - More depression/less enthusiasm

in Black staff (-0.11 (-0.21 to -0.04).

p=0.02)


267

Not significant - Team manager reports:

Availability of staff support group, ward meetings,

named supervisors for all staff, having had an away

day, whether all staff have appraisal. Availability of

modern matron, lead consultant, modern matron,

acute care forum or practice development nurse.

Adoption of specific philosophy of care/therapeutic

model or of protected therapeutic time. Team

manager rating of support from senior management.

Significant variables from previous model

that are not now significant: team conflict,

substantive fairness, manager support,

bank/agency status, marital status



268

Table A6. Associations between organisational variables and intrinsic job satisfaction

Outcome

variable

Stage 1 Testing of associations for individual

variables: Organisational variables in simple

model*, entered individually and unadjusted


Stage 2 Adjusted model –

Significant variables from

Stage 1 entered together

along with significant

demographic and

occupational variables

from Chapter 3 models.

Only significant


shown


model – as for Stage 2 with demand

support control variables also entered.

Only significant variables shown

Intrinsic

satisfaction

Positive

coefficient

associated

with greater

satisfaction


Role clarity: 0.55 (0.51 to 0.59), p<0.0005






Voice: how much are senior managers influenced?: 0.26

(0.23 to 0.30) p<0.0005


Satisfaction with supervision & appraisal: 0.29 (0.26 to

0.32), p<0.0005


0.35), p<0.0005

Role conflict: -0.08 (-0.13 to

-0.04), p=0.001


(0.12 to 0.24) p<0.0005

Substantive fairness: 0.06

(0.01 to 0.11), p=0.03

Procedural fairness: 0.08

(0.02 to 0.13), p=0.008

Role clarity: 0.19 (0.14 to 0.24), p<0.0005

Role conflict: -0.08 (-0.11 to -0.04)

p<0.0005


p=0.01

Substantive fairness: 0.05 (0.01 to 0.10)

p=0.02

Procedural fairness: 0.05 (0.01 to 0.10)

p=0.02

Job control: 0.34 (0.31 to 0.38), p<0.0005

Support from manager: 0.05 (0.02 to

0.08), p<0.0005

Support from colleagues: 0.06 (0.02 to

0.09), p<0.0005


269

Has had an appraisal in past year: 0.27 (0.19 to 0.34),

p<0.0005

Has a personal development plan: 0.32 (0.25 to 0.40),

p<0.0005

Number of supervisions in past 6 months: 0.02 per

meeting (0.01 to 0.03), p<0.0005

Days of training in past year: 0.009 per day (0.005 to

0.014), p<0.0005

Team manager level

Whether all staff have appraisal: 0.12 (0.02 to 0.22),

p=0.02


Not significant: Team manager reports - Availability

of staff support group, ward meetings, named supervisors

for all staff. Availability of modern matron, lead

consultant, acute care forum or practice development

nurse. Adoption of specific philosophy of care/therapeutic

model or of protected therapeutic time. Team manager

rating of support from senior management.

Service type: Less satisfaction in CMHT (-

0.15 (-0.26 to -0.04), p=0.008

Occupational group: Less satisfaction

among nurses (-0.08 (-0.015 to -0.02)

p=0.02) and psychologists (-0.23 (-0.44 to

-0.02) p=0.03)

Significant variables from previous

model that are not now significant:

work demands, team conflict, ethnic

group, marital status, bank/agency

contract, seniority

* As in chapter 3, throughout multilevel regression has been used to explore associations with outcome variables, using the xtmixed command in Stata 10. The two levels are individual and ward, with individuals nested within wards


270

Additional data from Section 4.11 - persistence of morale Table A7. Maslach Burnout Inventory Emotional Exhaustion domain - ward scores over one year follow-up

Number of respondents

Mean MBI score (s.d.)

Mean standardised MBI score (s.d)

Are

a

Ward

ID

Ward type




Stability of ward burnout mean score*

1 Acute 7 16 14.4

(11.2)

16.3 (10.3)

2.1

(0.8)

2.2

(0.8)

Stays medium

2 Acute 14 19 19.3

(10.8)

20.6

(8.4)

2.4

(0.8)

2.5

(0.6)

Stays medium

3 PICU 9 24 26.7

(14.5)

21.6

(11.9)

3.0

(1.1)

2.6

(0.9)

Change: high to medium

4 Acute 17 16 19.5

(12.1)

18.2 (10.1)

2.4

(0.9)

2.4

(0.8)

Stays medium

5 Acute 8 15 18.5

(16.2)

15.1

(8.2)

2.4

(1.2)

2.1

(0.6)

Stays medium

6 Acute 9 19 19.7

(13.6)

21.3

(11.8)

2.5

(1.0)

2.6

(0.9)

Change: medium to high

7 Acute 21 9 21.2

(13.4)

24.5

(10.1)

2.6

(1.0)

2.8

(0.8)

Stays high

Hea

rt o

f Engla

nd

8 Acute 16 14 21.6

(11.8)

26.1

(11.2)

2.6

(0.9)

2.9

(0.8)

Stays high


271




Are

a Ward

ID

Ward type





9 Older adult

14 20 20.5

(8.8)

18.7

(8.5)

2.5

(0.7)

2.4

(0.6)

Stays medium

10 Rehab 10 13 14.2

(10.0)

17.5

(12.8)

2.1

(0.7)

2.3

(1.0)

Stays medium

11 Acute 13 15 25.4

(11.7)

18.8

(11.2)

2.9

(0.9)

2.4

(0.8)


12 Acute 11 6 26.2

(14.5)

17.3

(11.3)

2.9

(1.1)

2.3

(0.8)


13 Acute 10 13 25.6

(8.5)

9.8

(7.6)

2.9

(06)

1.7

(0.6)

Change: high to low

14 Acute 11 12 21.6

(13.2)

12.5

(8.1)

2.6

(1.0)

1.9

(0.6)

Change: high to low

15 CAMHS 24 21 17.6

(8.7)

15.4

(8.5)

2.3

(0.6)

2.1

(0.6)

Stays medium

16 Forensic 18 20 17.9

(11.8)

13.9

(12.1)

2.3

(0.9)

2.0

(0.9)

Change: medium to low

Nort

h W

est

London

17 Acute 27 20 24.1

(13.4)

21.1

(11.6)

2.8

(1.0)

2.6

(0.9)

Stays high


272




Are

a Ward

ID

Ward type





18 Forensic 29 20 16.1

(9.9)

16.9

(11.1)

2.2

(0.7)

2.3

(0.8)

Stays medium

19 Rehab 12 6 15.6

(8.6)

15.3

(16.3)

2.2

(0.6)

2.1

(1.2)

Stays medium

20 PICU 21 16 17.0

(10.6)

20.5

(10.9)

2.3

(0.8)

2.5

(0.8)

Stays medium

All 311 312 19.9

(12.0)

18.2

(10.2)

2.5

(0.9)

2.3

(0.8)

Stays medium


273

Table A8. Maslach Burnout Inventory Cynicism domain- ward scores over one year follow-up

number of respondents



Area Ward ID

Ward type





1 Acute 7 16 5.1

(4.8)

3.3

(3.8)

1.7

(0.6)

1.4

(0.5)


2 Acute 14 20 4.8

(5.2)

6.4

(4.8)

1.6

(0.7)

1.8

(0.6)

Stays medium

3 PICU 9 24 9.9

(7.8)

6.6

(6.2)

2.3

(1.0)

1.9

(0.8)


4 Acute 17 16 6.2

(6.4)

3.5

(4.0)

1.8

(0.9)

1.5

(0.5)


5 Acute 18 15 4.9

(5.7)

4.0

(3.3)

1.7

(0.8)

1.5

(0.4)

Stays medium

6 Acute 9 17 7.1

(8.3)

4.9

(4.3)

1.9

(1.1)

1.7

(0.6)

Stays medium

7 Acute 20 9 4.3

(4.7)

5.5

(4.3)

1.6

(0.6)

1.7

(0.6)

Stays medium

Hea

rt o

f Engla

nd

8 Acute 16 14 6.9

(7.1)

8.0

(4.8)

1.9

(0.9)

2.1

(0.6)

Change: medium to high


274




Area Ward ID

Ward type





9 Older adult

14 20 4.8

(3.4)

4.5

(4.0)

1.6

(0.5)

1.6

(0.5)

Stays medium

10 Rehab 10 13 2.2

(2.1)

3.5

(4.2)

1.3

(0.3)

1.5

(0.6)

Stays low

11 Acute 13 15 5.8

(4.3)

7.7

(8.0)

1.8

(0.6)

2.0

(1.1)

Stays medium

12 Acute 11 6 5.3

(4.2)

2.3

(2.0)

1.7

(0.6)

1.3

(0.3)


13 Acute 11 14 5.3

(5.0)

3.9

(3.3)

1.7

(0.7)

1.5

(0.4)


14 Acute 11 12 8.2

(8.5)

3.3

(2.7)

2.1

(1.1)

1.4

(0.4)

Change: high to low

15 CAMHS 24 21 4.1

(3.9)

4.7

(4.1)

1.5

(0.5)

1.6

(0.5)

Stays medium

Nort

h W

est

London

16 Forensic 18 20 3.9

(7.0)

2.3

(2.2)

1.5

(0.9)

1.3

(0.3)

Stays low


275




Area Ward ID

Ward type





17 Acute 27 20 6.7

(6.3)

6.4

(4.6)

1.9

(0.8)

1.8

(0.6)

Stays medium

18 Forensic 29 20 4.5

(4.3)

3.0

(3.5)

1.6

(0.6)

1.4

(0.5)


19 Rehab 12 6 3.0

(3.2)

2.5

(3.0)

1.4

(0.4)

1.3

(0.4)

Stays low

20 PICU 21 16 8.2

(6.3)

4.4

(5.3)

2.1

(0.8)

1.6

(0.7)


All 311 314 5.5

(5.7)

4.7

(4.6)

1.7

(0.8)

1.6

(0.6)


276

Table A9. Maslach Burnout Inventory Personal Accomplishment domain - ward scores over one year follow-up

Number of respondents Mean MBI score (s.d.) Mean standardised MBI score (s.d)

Area Ward ID

Ward type





1 Acute 7 16 38.0

(4.7)

38.1

(6.2)

4.2

(0.4)

4.2

(0.5)

Stays low

2 Acute 14 18 36.2

(7.3)

34.8

(10.1)

4.0

(0.6)

3.9

(0.8)

Stays low

3 PICU 9 24 35.9

(9.5)

38.1

(7.1)

4.0

(0.8)

4.2

(0.6)

Stays low

4 Acute 17 14 32.8

(9.5)

35.3

(8.3)

3.7

(0.8)

3.9

(0.7)


5 Acute 18 15 33.6

(8.1)

35.8

(8.0)

3.8

(0.7)

4.0

(0.7)


6 Acute 9 17 30.8

(9.6)

32.7

(9.1)

3.6

(0.8)

3.7

(0.8)

Stays medium

7 Acute 20 9 30.0

(9.7)

32.7

(9.1)

3.5

(0.8)

3.7

(0.8)

Stays medium

Hea

rt o

f Engla

nd

8 Acute 16 14 34.7

(10.3)

33.6

(8.1)

3.9

(0.9)

3.8

(0.7)

Change: low to medium


277


Area Ward ID

Ward type





9 Older adult

14 20 31.6

(9.2)

33.7

(7.3)

3.6

(0.8)

3.8

(0.6)

Stays medium

10 Rehab 10 12 36.5

(4.2)

35.8

(6.5)

4.0

(0.3)

4.0

(0.5)

Stays low

11 Acute 13 15 35.3

(8.2)

35.9

(7.4)

3.9

(0.7)

4.0

(0.6)

Stays low

12 Acute 11 6 28.5

(10.2)

30.7

(12.5)

3.4

(0.8)

3.6

(1.0)

Stays medium

13 Acute 11 14 37.0

(7.6)

40.7

(4.3)

4.1

(0.6)

4.4

(0.4)

Stays low

14 Acute 11 12 36.5

(6.6)

36.5

(5.9)

4.0

(0.6)

4.0

(0.5)

Stays low

15 CAMHS 24 21 37.0

(6.7)

38.3

(6.3)

4.1

(0.6)

4.2

(0.5)

Stays low

Nort

h W

est

London

16 Forensic 18 20 35.3

(10.1)

40.8

(7.0)

3.9

(0.8)

4.4

(0.6)

Stays low


278


Area Ward ID

Ward type





17 Acute 27 19 29.5

(10.2)

35.4

(6.9)

3.5

(0.8)

3.9

(0.6)


18 Forensic 28 19 30.6

(9.6)

37.2

(8.3)

3.5

(0.8)

4.1

(0.7)


19 Rehab 12 6 29.0

(10.3)

35.5

(8.2)

3.4

(0.9)

4.0

(0.7)


20 PICU 21 16 33.8

(8.9)

37.2

(7.8)

3.8

(0.7)

4.1

(0.7)


All 310 307 33.2

(9.1)

36.3

(7.7)

3.8

(0.8)

4.0

(0.6)


279

Table A10. Change in Warr scale scores over 1 year follow-up – ward level data

Warr Anxiety-Contentment scale mean score (s.d.)


Warr Depression-Enthusiasm scale mean score (s.d.)


Area Ward ID

Ward Type


Change in mean score*



1 Acute 3.58 (0.58)

n=6

3.40 (0.51)

n=12

-0.18 3.89 (0.70)

n=6

3.96 (0.46)

n=12

+0.07

2 Acute 3.31 (0.72)

n=13

3.24 (0.62)

n=19

-0.07 3.68 (0.55)

n=13

3.45 (0.64)

n=19

-0.23

3 PICU 2.83 (0.78)

n=9

3.05 (0.73)

n=22

+0.22 3.00 (1.21)

n=9

3.44 (0.97)

n=23

+0.44

4 Acute 3.03 (0.68)

n=17

3.44 (0.59)

n=16

+0.41 3.51 (0.68)

n=17

3.65 (0.79)

n=16

+0.14

5 Acute 3.57 (0.63)

n=18

3.59 (0.69)

n=15

+0.02 3.68 (0.67)

n=17

3.80 (0.54)

n=14

+0.12

Hea

rt o

f Engla

nd

6 Acute 2.97 (0.81)

n=9

2.96 (0.55)

n=17

-0.01 3.18 (0.85)

n=9

3.33 (0.74)

n=17

+0.15


280





Area Ward ID

Ward Type





7 Acute 3.02 (0.78)

n=21

2.99 (0.62)

n=7

-0.03 3.27 (0.79)

n=21

3.13 (0.60)

n=7

-0.14

8 Acute 2.86 (0.47)

n=16

2.90 (0.65)

n=14

+0.04 3.14 (0.79)

n=16

3.19 (0.68)

n=14

+0.05

9 Older adult

3.51 (0.36)

n=12

3.37 (0.72)

n=20

-0.14 3.80 (0.35)

n=12

3.48 (0.69)

n=19

-0.32

10 Rehab 3.58 (0.76)

n=10

3.29 (0.63)

n=11

-0.29 4.05 (0.48)

n=10

3.79 (0.59)

n=11

-0.26

11 Acute 2.88 (0.95)

n=13

3.16 (0.65)

n=15

+0.28 3.33 (1.00)

n=13

3.43 (0.66)

n=15

+0.10

Nort

h W

est

London

12 Acute 2.63 (0.75)

n=9

3.40 (0.56)

n=5

+0.77 2.78 (0.94)

n=10

3.33 (0.98)

n=5

+0.55


281





Area Ward ID

Ward Type





13 Acute 3.10 (0.65)

n=10

3.60 (0.53)

n=13

+0.50 3.32 (0.83)

n=10

4.04 (0.42)

n=12

+0.72

14 Acute 3.19 (0.76)

n=11

3.78 (0.49)

n=12

+0.59 3.44 (0.99)

n=11

4.00 (0.61)

n=12

+0.56

15 CAMHS 3.37 (0.69)

n=23

3.49 (0.65)

n=20

+0.08 3.76 (0.57)

n=23

3.96 (0.52)

n=20

+0.20

16 Forensic 3.38 (0.53)

n=19

3.75 (0.61)

n=18

+0.37 3.61 (0.69)

n=18

3.91 (0.61)

n=17

+0.30

17 Acute 2.87 (0.76)

n=25

3.32 (0.81)

n=19

+0.55 3.34 (0.82)

n=25

3.58 (0.73)

n=18

+0.24

18 Forensic 3.12 (0.51)

n=28

3.34 (0.73)

n=18

+0.22 3.43 (0.56)

n=27

3.83 (0.40)

n=18

+0.40


282





Area Ward ID

Ward Type





19 Rehab 3.33 (0.53)

n=12

3.46 (1.01)

n=4

+0.13 3.68 (0.70)

n=12

3.72 (1.28)

n=5

+0.04

20 PICU 3.06 (0.63)

n=21

3.19 (0.74)

n=13

+0.13 3.26 (0.83)

n=21

3.59 (0.78)

n=13

+0.33

All 3.15 (0.70)

n=302

3.33 (0.68)

n=290

+0.18 3.45 (0.77)

n=300

3.64 (0.71)

n=287

+0.19


cclxxxiii

Addendum

This document is an output from a research project that was commissioned by the Service Delivery and Organisation (SDO) programme whilst it was managed by the National Coordinating Centre for the Service Delivery and Organisation (NCCSDO) at the London School of Hygiene & Tropical Medicine. The NIHR SDO programme is now managed by the National Institute for Health Research Evaluations, Trials and Studies Coordinating Centre (NETSCC) based at the University of Southampton.

Although NETSCC, SDO has managed the project and conducted the editorial review of this document, we had no involvement in the commissioning, and therefore may not be able to comment on the background of this document. Should you have any queries please contact [email protected].