Project WIMH: Post #2

The article vetting process continues.

It can be hard to read through some of the research on this topic. Here is a quote from a qualitative study by Cacciarro & Kirsh (2006) on the mental health needs of injured workers:

“It’s just a domino effect. It’s affecting my children. My kids come home from school and I haven’t got dinner made because I haven’t got the energy to make it. And they’re, we want mom back. We want our old mom back.”

(p.182)

Nevermind the lack of compassion that injured workers often feel when dealing with compensation systems, the alienation and stigma surrounding injuries, and having to come to terms with temporary or permanent unemployment.

That aside, my supervisor and I (along with several other colleagues) have started to look at these indirect or vicarious effects of work injuries on the mental health of teenagers and some preliminary findings suggest that living with an injured parent has a bigger detrimental impact on mental health than do injuries experienced directly by young workers. An area of research that I suspect will get increasing attention in the coming years.

References

Cacciacarro, L., & Kirsh, B. (2006). Exploring the mental health needs of injured workers. Candian Journal of Occupational Therapy, 73(3), 178-187.

Project WIMH: Post #1

Back at it again. It’s been a while since I’ve had an opportunity to move this project forward on the empirical side.

To help with this process, I’ve decided to begin blogging my efforts towards completing Project Work Injury and Mental Health (WIMH).

Over the last semester I finally wrapped up the database search, which consisted of a ludicrous amount of screening (~23,000 articles!).

I also had the great opportunity to present some of the preliminary findings at SIOP 2019 in a symposium on mental health at the workplace. I’m very grateful to Dr. Jennifer Dimoff and her PhD student, Stefanie Fox, for organizing the symposium.

Now that I’ve screened all the articles from the database search, it’s time to vet through the articles I pulled. Usually you have to be pretty intellectually ruthless with this process, but sometimes you can’t help spending too much time on interesting articles.

For instance…

…Betters (2010) found that individuals who were injured at work were more likely to gain weight if they thought they would benefit from mental health services (albeit, no effect size was provided), hinting that the pressure to reduce the discprepancy between where they are physically with where they want to be is having psychological consequences.

…Blake and colleagues (2014) found that over close to 50% of individuals who witness work-related fatalities experience probable or sub-threshold PTSD symptoms, which in turn have striking effects on depression, life functioning, and well-being. This research highlights the importance of psychological interventions for dealing with traumatic events at work.

Anyways, back to being ruthless for a bit as I vet through the pulled articles!

References

Betters, C. J. (2010). Weight gain and work comp: A growing problem in the workers’ compensation rehabilitation system. Work, 37(1), 23-27. doi:10.3233/WOR-2010-1053

Blake, R. A., Lating, J. M., Sherman, M. F., & Kirkhart, M. W. (2014). Probable PTSD and Impairment in Witnesses of Work-Related Fatalities. Journal of Loss and Trauma, 19(2), 189-195. doi:10.1080/15325024.2013.775889

Remove Cases in SPSS

Notice some outliers or problematic cases in your dataset and want a shorthand way to quickly remove them while also keeping a record of which cases you removed? No problem, there are numerous ways to approach this.

If it is just one or a few numerical cases, then a great shorthand is:

SELECT IF VARNAME <> CASE.
exe.

OR

SELECT IF (VARNAME ne CASE)
exe.

With this syntax, replace VARNAME with the identifying variables (i.e., the variable that will identify the case you want to remove) and CASE with the specific entry within that variable. For instance, if your VARNAME is ID and the CASE you want to drop is 653, then your syntax would look like this:

SELECT IF ID <> 653.
exe.

OR

SELECT IF (ID ne 653).
exe.

If you have a few cases rather than just one, the latter syntax may be more efficient to use. For example, imagine you also have cases 155, 374, and 416 you want to remove. Here is what the syntax would look like:

SELECT IF (ID ne 653 and ID ne 155 and ID ne 374 and ID ne 416).
exe.

You can also use the the exact same syntax with string variables by adding ‘ ‘ around the entry that would identify the case you want to remove. For example:

SELECT IF NAME <> ‘Dave’.
SELECT IF (NAME ne ‘Dave’).
SELECT IF (NAME ne ‘Dave’ and NAME ne ‘Bob’ and NAME ne ‘Bill’).

If you have a large dataset and want to remove a good chunk of cases – say you have a number of cases that are missing on a key variable – then you can use the following syntax:

SELECT IF (not missing(VARNAME)).
exe.

You may come across circumstances where you need to get more creative with your case removal syntax, but in general these are the basic approaches you’ll most often use. As I come across new strategies for removing cases in SPSS, I will be sure to add them to this post for reference.

[More to come]

Personality and Safety: A Review of Beus, Dhanani, & McCord (2015)

Are there certain features of our personality that increase or decrease the likelihood that we will behave safely or be involved in accidents at work? A number of recent meta-analyses have, with increasing rigor, attempted to combine all we know about the relationship between our Big Five personality characteristics (i.e., Openness to experience, Conscientiousness, Extraversion, Agreeableness, and Neuroticism – think OCEAN) and safety at work (Clarke & Robertson, 2005; Chrisitan et al., 2009; Beus et al., 2015). So, what can we say about this relationship?

First, it exists! There is indeed a relationship between four of the Big Five personality characteristics and safety. Surprisingly, agreeableness has consistently shown itself to be the largest and most robust predictor of safe behaviour. I say surprisingly because I would have guessed that conscientiousness, how orderly and responsible we are, would have shown the strongest relationship. Yet, conscientiousness came a modest second place (similar effect size but less variance explained) in terms of predicting safe behaviour. Finally, extraversion and neuroticism showed much smaller but consequential relationships with workplace safety behaviour, such that the more extraverted and neurotic individuals are, the more likely they are to behave unsafely at work.

The idea that agreeableness is the most robust predictor of unsafe behaviour is fascinating and has many implications. Beus and colleagues discuss many of these implications in their paper and I tend to agree with the idea that agreeableness motivates people to seek communion. Essentially, the goal of communion is to seek meaningful and healthy relationships with others. People are thus less likely to behave unsafely to avoid putting others at risk or to make the group suffer because of their own carelessness. In turn, this finding has tremendous implications for the power of social interventions and social features of the organization for shaping safe behaviour. Reframing safety performance as prosocial and adding information about how behaviour affects others to feedback systems are examples of how organizations can improve safety by activating the motivation towards communion.

While I find the idea that agreeableness is the best personality predictor, I could imagine there are almost certainly boundary conditions to this finding. For instance, I could imagine that an agreeable person who finds him or herself in a group that behaves poorly and unsafely will likely be swayed by the group to also behave in a poor and unsafe manner. I cannot think of any empirical evidence off the top of my mind to support this idea, but it would be a fascinating study and one that is entirely possible to do given the advances in multilevel analyses.

Moving on, it came as no surprise that conscientiousness was important to workplace safety behaviour. What was interesting was the idea that extraversion and neuroticism were related to unsafe work behaviour. Beus and colleagues examined particular features of these broad personality characteristics to provide insight into what is driving these relationships. What they found was that the relationship between extraversion and unsafe behaviour was largely driven by a feature of extraversion called sensation seeking; thrill seekers put themselves into more risky situations on average. Meanwhile, the features of neuroticism that were driving the relationship are anger and impulsiveness, while anxiety actually reduced unsafe behaviour. You could imagine the overlap between sensation seeking and impulsiveness to be a particularly dangerous combination of personality when it comes to safety.

While personality and workplace safety behaviour were shown to be are related, what about personality with accidents and injuries? Beus and colleagues address this question both theoretically and empirically. They proposed that personality would be related to accidents and injuries through unsafe behaviour. That is, behaviour would fully mediate or explain the relationship between personality and accidents and injuries. This is what they found*. If you want to understand how personality is related to accidents and injuries, you need to understand how personality drives workplace safety behaviour.

The role of personality on workplace safety behaviour and safety outcomes has become more clear through these reviews. However, there could still be doubt that personality is rendered unimportant when compared to other predictors of workplace safety. For instance, does personality still matter when compared to a robust predictor of workplace safety such safety climate (i.e., the collective perceptions of an organization’s safety practices, policies, and procedures). Again, Beus and colleagues rose to the occasion to provide an answer. In their meta-analysis they showed that personality still mattered when taking safety climate into account (although slightly less than in isolation).

Based on the reviews, especially that completed by Beus and colleagues, we can be confident in a number of things. First, we know what the most important personality characteristics are in predicting workplace safety behaviour and their relative magnitude. Second, we can confident that personality is indirectly related to accidents and injuries through safety behaviour. Finally, we can be sure that personality can explain safety behaviour when taking other predictors, particularly safety climate, into considering.

 

*NOTE. Well not exactly. Results from Beus et al (2015) suggest that a partially mediated model has a slightly better statistical fit. However, Beus and colleagues suggest that this is only because of how certain personality characteristics are measured, such that some measures are behavioural in nature. Given that the statistical techniques used were not designed to detect and account for methodological contamination, researchers need to make judgement calls as Beus and colleagues did – and to their credit, argued that a fully rather than partially mediated model is appropriate.

References

Beus, J. M., Dhanani, L. Y., & McCord, M. A. (2015). A meta-analysis of personality and workplace safety: Addressing unanswered questions. Journal of Applied Psychology100(2), 481-498.

Clarke, S., & Robertson, I. (2005). A meta‐analytic review of the Big Five personality factors and accident involvement in occupational and non‐occupational settings. Journal of Occupational and Organizational Psychology78(3), 355-376.

Christian, M. S., Bradley, J. C., Wallace, J. C., & Burke, M. J. (2009). Workplace safety: a meta-analysis of the roles of person and situation factors. Journal of Applied Psychology94(5), 1103-1127.

 

 

Criteria for Testing and Evaluating Occupational Safety Interventions: An Overview of Shannon, Robson, & Guastello (1999)

Organizations can sink a lot of resources into improving safety, but how can we make sure that these interventions are effective? Easy – science! Well, not so easy. Especially if you want the science to be done well. Testing safety interventions requires considerable time, effort, and funding. However, if done well, these tests can be invaluable. You may ask, though, what do I mean by ‘science done well’?

Shannon and colleagues (1999) give an inspired answer to this question in their paper by providing a practical but evidence-based list of criteria to consider when testing interventions. I list these criteria below along with some commentary to emphasize the main take-aways.

Criteria for evaluating occupational safety intervention research

 Program objectives and conceptual basis

  • Were the program objectives stated?
  • Was the conceptual basis of the program explained and sound?

These questions are a great place to start. The objectives of the study are key because they will determine which intervention is appropriate and how we would determine whether the intervention was successful or not. Meanwhile, the conceptual basis provides us with a way of organizing our thoughts, making it easier to connect the vast array of moving parts within interventions.

Study design

  • Was an experimental or quasi-experimental design employed instead of a non-experimental design?

As far the hierarchy of rigor goes, experimental randomized controlled trials with baseline measures are at the peak of the pyramid. This is how you would want to design your study to have the most confidence in your results. However this is rarely feasible given the nature of field research. As such, quasi-experimental designs are next down the gradation of the rigor hierarchy.  There are several variations of quasi-experiments that I won’t expand upon here, but there are several great resources to figure out which quasi-experimental features to consider weigh above others (see for example Cook & Campbell, 1979). Finally, non-experimental designs can give you the ability to observe change, but without a control group the ability to infer where the change comes from is hard to determine. These non-experimental designs should therefore remain last case scenarios.

External validity

  • Were the program participants/study population fully described?
  • Was the intervention explicitly described?
  • Were contextual factors described?

Detailed information about the participants (e.g., demographics, means of recruitment, and drop-out rate), intervention (e.g., duration and program content), and context (e.g., current state of safety performance within the given organization) provide crucial insight towards extrapolating any one intervention to other workplaces. As such, it is good practice to be as explicit about these particular features as possible.

Outcome measurement

  • Were all relevant outcomes measured?
  • Were the measurement methods shown to be valid and reliable?
  • Was the outcome measurement standardized by exposure?

The first point brings us back to thinking about the objective of the intervention. In short, your main outcome should be tied directly to your objective. If the goal was to decrease injuries, you should measure injuries. It may also be worth measuring other outcomes, such as those which may help explain how the intervention is working (both the implementation and mechanistic outcomes) and ensuring that it is not having any unintended consequences (reducing major injuries may increase minor injuries or underreporting). Finally, the latter two points ask whether you’re actually measuring what you want to measure (validity), whether your measurement is consistent (reliability), and whether the comparison you’re making is a fair one based on relative exposure to hazards (standardization).

Qualitative data

  • Were qualitative methods used to supplement quantitative data?

By adding some qualitative features, such as interviews, observation, and primary/secondary documents, to the test of the intervention can be very insightful. At a minimum it adds to the richness of the study and at best it can help spot threats to or even strengthen internal validity.

Threats to internal validity

  • Were the major threats to internal validity addressed in the study?

There are a number of potential threats to internal validity (i.e., a condition or event that may lead a research to the wrong answer), with more adding up as the rigor of the research design goes down. For randomized designs, the main threats are those which counteract randomization (e.g., improper randomization), diminish treatment (e.g., unintentional diffusion of treatment to control groups), and reactions of those participating in or administering the study (e.g., going along with or against expectations on purpose). For non-randomized control groups, selection biases are vital to consider. Meanwhile, major threats to the internal validity of before-and-after designs can be those related to the temporal aspect of this design, such as history (i.e., changes attributable to other factors than the intervention), maturation (i.e., natural changes in study group that occur outside the intervention), testing (i.e., essentially the placebo effect), instrumentation (i.e., changes in measurement over the course of the study), and regression to the mean (i.e., individuals in the extremes at one point in the study naturally move towards the average). For more details about these threats, please see Cook & Campbell (1979).

Statistical analysis

  • Were the appropriate statistical analyses conducted?
  • If study results were negative, were statistical power or confidence intervals calculated?

Typically, the analyses for testing the differences between experimental and control groups are fairly straight forward but become slightly more complicated for pre- and post-intervention measurements. Essentially, the less rigorous the study design, the more likely that you will need to add statistical controls to adjust for any differences between groups or within individuals over time.

Conclusions

  • Did conclusions address program objectives?
  • Were the limitations of the study addressed?
  • Were the conclusions supported by the analysis?
  • Was the practical significance of the result discussed?

Finally, the concluding points are good questions to be able to answer after the intervention. By following the criteria above, it will be clear whether the intervention objectives were addressed, what the specific limitations are for the interpretation of the intervention, how this affects the conclusions that can be drawn, and what this means for practice. Striking a good balance will be critical in the conclusion. Field research is still rare, and good field research is even more rare. By following the criteria above and attempting to design a study to maximize the validity of the intervention, the more power it will have towards helping us improve safety in the workplace.

References

Cook, T. D., Campbell, D. T., & Day, A. (1979). Quasi-experimentation: Design & analysis issues for field settings. Boston, MA: Houghton Mifflin.

Shannon, H. S., Robson, L. S., & Guastello, S. J. (1999). Methodological criteria for evaluating occupational safety intervention research. Safety Science31(2), 161-179.

What We Know About Safety Climate and Workplace Injuries: A Summary of Beus, Payne, Bergman, & Arthur (2010)

What is the relationship between workplace injuries and the perceptions employees have of an organization’s safety policies, procedures, and practices? Beus and colleagues attempted to examine this relationship between injuries and safety climate by meta-analyzing the existing literature. The approach that Beus and colleagues took was particularly interesting because they highlighted a number of important but vastly underappreciated distinctions. They strongly emphasized the theoretical motivation for contrasting directional relationships (i.e., whether injury leads to changes in safety climate, or whether safety climate leads to changes in injuries have theoretical differences) and the separation of climate levels (i.e., we could expect the idiosyncrasies of individual-level psychological climate and the group-level organizational safety climate to have non-overlapping variance to be explained). From a research perspective, the emphasis that Beus and colleagues place on these theoretical and analytical distinctions is strengthened by the fact that they are building on clear limitations of previous reviews on the relationship between injuries and safety climate (Clarke, 2006; Christian et al., 2009).

First, the direction of the safety climate and injury relationship contains two different meanings. In one direction, it is expected that safety climate fosters expectations as to what type of behaviours will lead to certain outcomes. An organization marked by a high safety climate will be one where employees believe that unsafe behaviour is highly frowned upon and that management is committed to ensure that employees maintain a safe work environment. As such, the safety climate-to-injuries relationship will be dictated by expectations fostered by the safety climate. In the other direction, observing or experiencing injuries at work will signal important information with regards to the importance of safety to the organization. As such, the presence or absence of injuries will influence perceptions of safety climate.

Second, Beus and colleagues note how climate can be conceptualized at both the individual- and group-level. At the individual-level we have psychological safety climate, and at the group-level we have organizational safety climate. As Beus and colleagues rightfully point out, very similar phenomena can be quite different at different levels. The idiosyncrasies of individuals, such as their unique experience and worldviews, will distinguish them from the group. However, group norms will shape individual behaviours to make group members more similar in comparison to those in other groups. As such, both individual- and group-level safety climate should have unique relationships with injuries.

Finally, Beus and colleagues note a number of conditional factors on the proposed safety climate and injury relationship. In particular, they suggest that the length of time over which injuries are assessed, the contamination and deficiency of safety climate measures, and the operationalization of injuries will shape the strength of relationship between safety climate and injuries. Length of time over which injuries are assessed was tested as an exploratory moderator, while safety climate contamination (i.e., incorrectly adding modifications) and deficiency (i.e., insufficiently measuring safety climate) should attenuate the climate-injury relationship. Meanwhile, stricter injury operationalization should result in stronger injury-to-safety climate relationships, while broader injury operationalization should produce stronger safety climate-to-injury relationships.

What were the results? Fortunately, there were enough studies to test the injury to psychological (r = -.16) and organizational safety climate (r = -.29) and the organizational safety climate to injury (r = -.24) relationships, but not the psychological safety climate to injury relationship. These results suggest that there is a relationship in both directions, and that the group-level safety climate appears to have a stronger relationship with injuries than the individual-level safety climate.

What about the moderators? Length of time over which injuries were assessed only explained some variance for the organizational safety climate to injury relationship, with longer times of measurement producing smaller effect sizes. Contamination appeared to inflate the prospective relationship between injury to both psychological and organizational safety climates (contrary to expectations), while deficiency weakened these same relationships (as expected). Finally, the operationalization of injuries had a pattern to suggest that stricter operationalization was found to have a stronger relationship in the injury-to-climate relationships, and the opposite for climate-to-injury relationships. However, the confidence intervals overlapped, suggesting that the statistical difference between them is weak.

What is something that managers and organizations can do to reduce injuries and improve safety climate based on the findings of this paper? One finding is particularly important to answering this question. The most common dimension of safety climate is management’s commitment to safety. Beus and colleagues found that this was a much stronger predictor for reducing injuries than vice versa (although the opposite relationship was still significant) and that this was the strongest predictor in reducing injuries than any other safety climate measure. This means that the most important thing that managers and organizations can do is effectively communicate their commitment to employee safety. This can be done in numerous ways, such as providing engaging methods of safety training, safety programs and initiatives, and encouraging the reporting and discussion of safety, accidents, and injuries.  There is little else that can compare to a sincere and invested effort on behalf of organizations to improve the health and safety of employees.

References

Beus, J. M., Payne, S. C., Bergman, M. E., Arthur, W. (2010). Safety climate and injuries: An examination of theoretical and empirical relationships. Journal of Applied Psychology, 95, 713-727.

Christian, M. S., Bradley, J. C., Wallace, J. C., & Burke, M. J. (2009). Workplace safety: A meta-analysis of the roles of person and situation factors. Journal of Applied Psychology, 94, 1103-1127.

Clark, S. (2006). The relationship between safety climate and safety performance. A meta-analytic review. Journal of Occupational Health Psychology, 11, 315-327.