neuroticism and quality control in health services: a laboratory simulation

Neuroticism and Quality Control in Health Services: A Laboratory Simulation

KRAIG L. SCHELL Ange lo State University

LUZ-EUGENIA COX-FUENZALIDA University o f Oklahoma

This study was designed to examine the effects of neuroticism on performance in checking prescriptions using a pharmacy simulation task. A sample of 91 participants who had completed an inventory to assess neuroticism performed a prescription- checking task while hit rates and false alarms were assessed. ANOVAs were con- ducted and results indicated that higher levels of neuroticism, as compared with lower levels of neuroticism, were related to fewer false alarm rates without corresponding hit rate reductions. In addition, the relationships between dependent variables and signal probability groups supported previous research. Limitations of the study are considered and implications for future research are discussed.

A n increasing number of studies in personality and human factors are exploring the effects of neuroticism on task performance, as it is believed to be a significant

predictor of behavior. Though a number of theories have been proposed to encapsulate this dimension, one of the most dominant theories of neuroticism is that proposed by Hans Eysenck (1967). Eysenck defined neuroticism as a dimension ranging from emotional stability to emotional instability, and suggested that individual differences in neuroticism could be explained in terms of differential arousal mediated by the limbic system. In turn these arousal differences are believed to be manifested in behavioral differences observed between those scoring higher and lower in neuroticism.

Contemporary personality research suggests that the relationship between neuroticism and performance is not a simple one, and is probably moderated by a number of factors such as time of day, workload history, and task difficulty (Mayer, 1977; Eysenck, 1981; Cox-Fuenzalida, Swickert, and Hittner, in press). The literature contains research supporting both detrimental and facilitative effects on performance. For example, some studies have shown that neuroticism (and trait anxiety) can inhibit performance (Spence & Spence, 1966; Eysenck, 1983, 1992, 1997; Eysenck & Eysenck, 1985; Darke, 1988; Newton et al., 1992; Cox-Fuenzalida, Swickert & Hittner, in press), while other studies have found it to improve performance (Eysenck & Calvo, 1992; Zeidner, 1998). It appears that the detrimental effects are most prevalent in

Current Psychology: Developmental �9 Learning �9 Personality �9 Social Winter 2005, Vol. 24, No. 4, pp. 231-241.

232 Current Psychology / Winter 2005

studies employing tasks requiring high levels of attention such as vigilance tasks (for reviews of these studies see Eysenck, 1983; Matthews & Deary, 1998; Matthews et al., 2000), and that improvements in performance are most readily observed in studies using relatively easy tasks (Eysenck & Calvo, 1992; Zeidner, 1998). Mayer (1977) directly manipulated task difficulty and found that neuroticism significantly facilitated performance on tasks requiring simple visual search problems such as searching for a specific letter, while it was associated with a significant decrement in performance for more difficult cognitive tasks (e.g., anagram solving). Consequently, previous research suggests that the effects of neuroticism on performance may be most effectively un- derstood within the context of an individual task.

Given a complex relationship between neuroticism, task variables, and performance, it is important, particularly for safety sensitive occupations, to further examine neuroticism and performance relationships in work simulation environments. Work simulation environments provide task relevant variables that may be more ecologically valid than more basic laboratory tasks. There are many real-world jobs in which these variables may potentially lead to greater predictive efficiency with respect to performance. One such area may be pharmaceutical-prescription filling and, more specifically, the prescription-verification task.

If we consider a pharmacist who must routinely examine and fill prescriptions, given the findings of Mayer (1977) one might argue that neuroticism should be a significant predictor of performance on a prescription verification task. Specifically, individuals higher in neuroticism may have less difficulty monitoring prescriptions for errors compared to those lower in neuroticism, leading to better effectiveness in detecting errors for these individuals. In addition, according to the pharmacy literature, a second factor believed to contribute to pharmacy errors is the frequency of errors in a given set of orders (or signal probability) that a pharmacist must check or fill in a given time (Abood, 1996; Allan-Flynn et al., 1999; Cohen, 1999; Bilsing-Palacio & Schell, 2003). In other words, a relatively error-free set of 100 orders should be related differently to checking performance than a relatively error-prone set of the same number of orders. There is additional evidence in the vigilance literature indicating that signal probabilities can affect detection performance (Davies & Parasuraman, 1982). Therefore, the manipulation of signal probability would seem an important factor in a study designed to examine the effects of neuroticism on error-detection performance.

The current study will examine the performance of participants in a simulated prescription-checking task. Measures of mood and workload will be taken before, during and after the task. Previous studies suggest that post-task measures of mood and workload in this task situation are not related to hit rates and false alarms in the checking task and so we will focus on pre-task and mid-task scores on these factors (Schell et al., 2002; Schell, Cox-Fuenzalida, & Matthews, in review). Mid-task scores, in fact, have been shown to be directly associated with hit rates in this checking task, according to these studies.

ScheU and Cox-Fuenzalida 233

Hypotheses

Given that higher levels of neuroticism have been associated with performance facilitation in relatively easy tasks (Mayer, 1977), it was expected that neuroticism would be significantly and positively related to overall hit rates. In addition, there is evidence that those scoring higher in neuroticism are likely to evaluate themselves more negatively than less anxious individuals (Wells & Matthews, 1994). Therefore, it was also expected that higher levels of neuroticism would be significantly related to higher false alarm rates because of performance concerns regarding errors of omission. Furthermore, based on previous research (Abood, 1996; Allan, 1994; Cohen, 1999; Davies & Parasuraman, 1982) it is expected that neuroticism would be related to false alarms but that the relationship would be different depending on signal probability group. Finally, it is possible that the concern about performance for individuals higher in neuroticism may result in increased tension, depressed mood, and higher levels of perceived workload for high neurotics as compared with low neurotics. Therefore, it was hypothesized that higher levels of neuroticism would be associated with increased mid-task tension, lower mid-task hedonic tone, and higher mid-task perceived workload.

M E T H O D

Participants

The participants were recruited from undergraduate psychology courses at Angelo State University during the fall of 2001 and the spring of 2002. A total of 91 volun- teers participated in this study (27 males and 64 females) and received extra credit for their participation. The age range for the participants was 18 to 43 years of age with a mean age of 20 (SD = 4).

Simulation Work Area

Workstations were located in an 11' x 17' rectangular room with one recessed central fluorescent ceiling light fixture. Each workstation consisted of a table, chair, computer, keyboard, and mouse. A small digital clock was located to the left of each workstation. In order to minimize distractions, white presentation boards were placed around the workstation, helping to limit the subject's field of vision to their workstation. A desk calendar frame consisting of a plastic base with metal notebook-type clips held the simulated labels, which were adhered to sheets of paper measuring approxi- mately 4" by 5" attached to the metal clips.

The pharmacy-simulation task employed has generally been used to investigate pharmacy applications (i.e., Grasha & Schell, 1999; Grasha et al., 2000; Schell et al., 2003). However, it does not require specific knowledge of medications or pharmacy policy, and therefore serves effectively as a general quality-control task. Additionally, the task allows participants to proceed at a comfortable pace so that potential con- founds such as time pressure and task novelty can be minimized.


Given previous work in both the pharmacy and vigilance literatures, signal probability was manipulated in this simulation. Specifically, in an effort to simulate similar tasks in the real world, signal probability levels were set at a median value of 28% for one group (with a range of 26%-30%), and at a median value of 36% (with a range of 34%-38%) for the second group. In addition, the probabilities were varied slightly around the medians as real-world tasks do not have strictly controlled error ratios. This technique also allows for unique comparisons between different signal probabilities.

Order Cards

Order cards measuring 3" by 5" were included in the Ziploc bags, along with standard 30-count prescription bottles. These cards simulated labels found adhered to a prescription bottle, and contained the following fictitious information: customer name, customer address, company affiliation, product name, product quantity, reorder information, and customer telephone number. The cards were handwritten using several handwriting styles to simulate the variety of scripts that pharmacists might see during any given day. Some cards also included distractor information that was irrelevant to the experiment, such as cities, states, and suffixes such as "Dr."

Products Dispensed

Plastic beads, plastic paper clips, and standard hardware washers and nuts were used to simulate different kinds of drugs. Each of these classes of materials varied either in size, shape, or color. In addition, half of the materials were marked with a black permanent marker and given a slightly different name than their unmarked counterparts. This was done to simulate generic and name-brand drug dichotomies and to create an opportunity for "look-alike, sound-alike" errors that are commonly reported in the pharmacy literature (Cohen, 1999). A complete list of the materials used in the simulation is available elsewhere (Schell & Grasha, 2001; Schell et al., 2003).

Names were given to the items based on their physical characteristics. The basic names for each class of material were BEADS, CLIPS, WASHERS, and NUTS. These names were assigned to unmarked varieties of all items. WASHERS, NUTS, and CLIPS were also assigned a nttmber indicating their size relative to the other materials in their category. For example, 1.0 indicated the smallest size of a particular material, 10.0 indicated a medium size, and 100.0 denoted a large size. Also, BEADS and CLIPS came in a variety of colors, and a two-letter abbreviation attached to the name denoted the color of the item. Long cylindrical beads were denoted by the letter L, round beads were assigned the letter R, and doughnut-shaped beads were given the letter D. Thus, a doughnut shaped, red bead would receive the name BEADS-D-RD with the D signifying the shape as doughnut and the RD marking the bead as red. CLIPS-BL-100.0 would signify a large, blue clip, and NUTS 1.0 would represent a small nut.

Marked varieties of the items were assigned names in a similar fashion, but the

ScheU and Cox-Fuenzalida 235

spelling of their names was changed according to a predefined rule. The basic names for these items were BEEDS, WOSHERS, CLEPS, and NOTS. Thus, a marked doughnut-shaped red bead like the one mentioned in the example above would be named BEEDS-D-RD.

Instruments

Pre-tests were administered to participants prior to their participation in the task portion of the experiment. The pre-tests represented three categories: measures of basic personality traits, measures of current stress, and measures of coping ability. This study is part of a larger research effort, and consequently, only the variables and data relevant to this project will be reported. Neuroticism was assessed using the Eysenck Personality Inventory (EPI, Form A; Eysenck & Eysenck, 1968). Items on the Neuroticism Scale measure a participant's level of emotional stability versus emotional instability with higher scores indicating higher levels of neuroticism. EPI reli- ability estimates range from 0.84 to 0.92, and internal consistency coefficients range from 0.89 to 0.95. For construct validity please see the EPI Manual.

Participants were given the Dundee State Stress Questionnaire (DSSQ: Matthews, Jones, & Chamberlain, 1990) at pre-task, mid-task, and post-task measurement points. The DSSQ consists of 29 items and is a multidimensional instrument designed to measure workload, mood and motivational cognitions as well as cognitions associated with stress, arousal and fatigue. The DSSQ has been shown to be reliable and valid in a variety of different task domains (Matthews et al., 1990, 1992; Matthews & Desmond, 1998).

At mid-task and post-task measurement points, participants were given a modified version of the NASA Task Load Index (TLX: Hart & Staveland, 1988), which measures perceptions of workload stemming from a task. Six dimensions of workload are measured on the TLX: Mental Demand, Physical Demand, Temporal Demand, Per- ception of Performance, Effort, and Frustration. The TLX is one of the most widely used measures of perceived workload (Nygren, 1991), but because of possible psychometric problems with its weighting procedure, we treated the six dimensions of workload as separate variables and global workload scores are reported as non-weighted means (Dickinson, Byblow, & Ryan, 1993).

Procedure

Participants were seated at one of two computer workstations. After completing the required pre-tests, they were given the DSSQ prior to the training portion of the verification simulation. Next, training for the simulation was initiated. Verbal and visual training of participants consisted of explanation and overview of the simulation, exposure to the materials used in the simulation, and ten supervised "practice trials."

A trial in this study was defined as the completed verification of one simulated order. The participant selected for verification a bagged order containing a simulated


prescription and a simulated order card in a predetermined sequence. The desk calendar frame with labels corresponding to the bagged orders was located to the participant's left. The participant removed the bottle and the order card from the bag, and examined the information on the card and the items inside the bottle. The participant was instructed to note any errors in the orders, including incorrect items in the bottle when compared against the order card, incorrect amounts of an item as compared against the order card, and information inconsistencies when comparing the order card and the paper label in the desk calendar. When the evaluation was finished, participants en- tered their responses into a customized computer database which was able to record participants' answers to three questions about each order. First, was the item in the bottle the correct item according to the order card? Second, was the amount of the item in the bottle correct according to the order card? Third, did the information on the order card correspond to the information on the paper label? After responding to the questions by responding either "Yes" or "No," the participant pressed the "Next" button on the software and the order was considered complete.

Upon completion of the practice trials, the participant was given the opportunity to ask any questions for clarification purposes. The participant was informed that once the experiment had begun no further questions could be answered. The participant was then presented with a large clear plastic container of the first "block" of orders to be verified. Each block was comprised of 40 orders. The participant was advised that the time limit for the first block of 40 orders was 45 minutes. Also, the participant was instructed to place the container lid within reach for storing the groups of bags as they were completed. A small digital clock was located at each workstation to serve as a gross indicator of time as the experiment progressed. The participant was also given a two-way radio and told to push the "call" button if any technical problems were encountered during the verification task or when a block of orders was completed.

Once the participant completed the first block of orders or the 45-minute time period elapsed, the experimenter administered the DSSQ mood instrument for a second time as well as the TLX perceived workload measure. Then, the second block of orders was presented and the participant was allowed to proceed as before. The second block also consisted of 40 orders and the same time limits were imposed. Upon completion of the second block of prescription orders or expiration of the 45-minute time limit, the participant was given the DSSQ mood instrument and the TLX perceived workload measure for the last time, debriefed, and released.

RESULTS

The two primary dependent variables, hit rates and false alarm rates, were analyzed separately across three types of errors (see Table 1 for the entire sample and for individual experimental conditions). Hit rates represented accurate performance and false alarm rates represented response bias. ANOVAs revealed that the difference in overall hit rates between experimental conditions was significant (F (1.76) = 5.32, p < .02), but the differences in false alarm rates were not. Due to the small number of errors present, percentages within error categories tend to resemble discrete variables

ScheUand Cox.Fuenzalida 237

TABLE 1 Descriptive Statistics for Item, Counting and Label Error Detection Performance for the

Entire Sample and within Signal Probability Groups

Hit Rates False Alarm Rates

Error Type M SD M SD

Item Errors Low SP Group 62.96 37.61 96.78 3.09 High SP Group 45.63 25.71 95.08 4.30 Overall 53.63 32.72 95.86 3.86

Counting Errors Low SP Group 82.69 26.88 95.03 2.95 High SP Group 83.72 17.34 94.85 3.42 Overall 83.24 22.10 94.93 3.19

Label Errors Low SP Group 68.31 24.18 81.73 23.55 High SP Group 50.33 23.18 90.86 16.98 Overall 58.63 25.16 86.65 20.65

Note: Means expressed as percentage of orders correctly identified, thus lower numbers indicate poorer performance.

with a limited number of possible values. As a result we will deal with overall mea-

sures of performance because they most closely resemble continuous measures.

DISCUSSION

This study was designed to examine the effects of Neuroticism on hit rates, false

alarm rates, mood and perceived workload using a task patterned after prescription

TABLE 2 Correlations among Overall Hit Rate, Neuroticism, and Task-Related Affect for

the Entire Sample and for Signal Probability Groups

Hit Rates Neuroticism Variable Signal Prob. All Lo Hi All Lo Hi

Pre-Task Energy 12 27 -07 -24* -20 -28 Tension 03 -05 16 40* 37* 43" Hedonic Tone 06 26 -18 -36* 4 5 * -30 Anger -07 -21 07 20 17 21 Motivation 30* 48* 05 05 -01 07 Mid-Task Energy 09 26 -12 21 -17 -24 Tension -21 -25 -11 20 15 26 Hedonic Tone 17 24 17 -08 -29 03 Anger -25* -31 -17 21 20 22 Motivation 30* 31 28 07 -01 14

Note: Decimals removed for display purposes. * =p < .05.


TABLE 3 Correlations among Overall Hit Rate, Neuroticism, and Perceived Workload

for the Entire Sample and for Signal Probability Groups

Variable Hit Rates Neuroticism

Signal Prob. All Lo Hi All Lo Hi

Mid-Task Mental Demand 02 -09 11 -08 -10 -05 Phys. Demand 02 15 -13 -11 -20 03 Temp. Demand 03 08 -06 13 11 15 Perform. Perception 13 13 02 06 -04 20 Effort 27* 20 26 -03 02 -07 Frustration -12 -20 -03 23 14 33*

Note: High scores on Performance Perception indicate favorable impressions of one's work. Deci- mals removed for display purposes. * =p < .05.

checking behavior in a pharmacy. Also, varying signal probabilities (error ratios) were used to replicate earlier findings and to test for Neuroticism effects on performance as a function o f those probabilities. Signal probabilities were found to affect overall task performance as expected, and findings were consistent with previous research (Abood,

1996; Allan, 1994; Cohen, 1999; Davies & Parasuraman, 1982). Further, as predicted results showed that Neuroticism was related to false alarm rates and mood, and that those relationships were slightly different depending on the signal probability to which participants were exposed.

Hit rates and false alarm rates were significantly different in the expected direc-

tions. These results are consistent with the findings of Bilsing-Palacio and Schell (2003). Specifically, lower signal probabilities were connected to higher hit rates, but results revealed higher false alarm rates as well, suggesting an overall more liberal decision criterion for these participants than those in the high signal probability condi-

TABLE 4 Nonparametric Correlations among Overall False Alarm Rate and Perceived Workload

for the Entire Sample and for Signal Probability Groups

False Alarm Rates

Variable Overall Low SP High SP

Mid-Task Mental Demand -02 17 -16 Phys. Demand 05 13 -02 Temp. Demand -07 07 -24* Performance Perception 03 08 08 Effort -14 -01 -19 Frustration -04 01 - 11

Note: High scores on Performance Perception indicate favorable impressions of one's work. Deci- mals removed for display purposes. * =p < .05.

ScheU and Cox.Fuenzalida 239

tion. Bilsing-Palacio and Schell (2003) offered an explanation that this criterion difference is a shift (that occurs as the participants work through the simulation) based on dynamically formed impressions of the overall error quality of the order set. The data collection constraints of the present study did not allow us to sample performance at different points during the task. However this could be easily addressed and examined in future studies.

Secondly, contrary to expectations, individuals scoring higher in neuroticism generally committed fewer false alarms than those lower in neuroticism. One plausible explanation is that these results reflect an aversive value placed by participants on possible mistakes. In other words, a mistake could be defined as detecting an error that in reality was not present (false alarm). The lower false alarm rates in high neurotics may stem from more intense scrutiny of the stimuli associated with a stronger fear of failure. The lack of significant differences in hit rates between high and low neurotics appears consistent with this explanation, especially because mean hit rates were higher for high neurotics than for their low-neurotic counterparts.

One important point however, is that although a false alarm may well have been perceived as undesirable by participants, in the real world, the impact of undetected errors can be readily identified. In real-world situations, of course, each of these mistakes (false alarms or undetected errors) comes with a different set of conse- quences. False alarms tend to reduce the efficiency of the checking process, and may lead to increased indirect costs down the line as a function of being "overly cautious." Nevertheless, undetected errors, especially in a pharmacy setting, could have far more damaging ramifications, such as patient injury and litigation. Thus, implicit in the real- world version of this task is the understanding that an undetected error is much worse than a detected non-error.

Third, as expected, results revealed a relationship between neuroticism and task- related affect. Specifically, high neurotics reported decreased task engagement and increased tension that is consistent with higher levels of negative affect typically associated with neuroticism. The lack of a significant relationship between neuroticism and perceived workload may have occurred because the task was not demanding enough--and as a result, perhaps not the source of increased negative affect. It has been suggested that tasks have unique qualities that call on different sets of individual difference variables in order to attain quality performance (see Hockey, 1984). Future studies may want to increase the complexity of the simulation and perhaps potential personality-based effects would become more salient.

To conclude, the findings of this study support previous research suggesting that Neuroticism in some circumstances may indeed have a facilitative effect on performance. In fact, it appears that higher levels of Neuroticism may be helpful in this type of error detection task by reducing false alarm rates without corresponding reductions in hit rates.

The prescription verification simulation task employed in this study may begin to bridge a gap between basic research employing more traditional laboratory tasks and real world application. However, before any causal explanations can be made about the facilitative effects of Neuroticism on performance in quality control tasks future


studies might examine variables such as performance on different tasks, and performance of skilled subjects.

N O T E S

The data reported in this study was collected with support from an institutional research grant from Angelo State University. The authors express gratitude to the research team at Angelo State University for their invaluable assistance in data planning and collection.

Address for correspondence: Luz-Eugenia Cox-Fuenzalida, Department of Psychology, University of Oklahoma, Dale Hall Tower 705, Norman, OK 73019. E-mail: [email protected].

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