Stressful work environment and wellbeing: What comes first?

Journal of Occupational Health Psychology 2015, Vol. 20, No. 3, 289 –300

© 2015 American Psychological Association 1076-8998/15/$12.00 http://dx.doi.org/10.1037/a0038684

This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.

Stressful Work Environment and Wellbeing: What Comes First? Marko Elovainio

Tarja Heponiemi

National Institute for Health and Welfare, Helsinki, Finland and University of Helsinki

National Institute for Health and Welfare, Helsinki, Finland

Markus Jokela and Christian Hakulinen

Justin Presseau

University of Helsinki

Newcastle University

Anna-Mari Aalto

Mika Kivimäki

National Institute for Health and Welfare, Helsinki, Finland

University College London

The association between the psychosocial work environment, including job demands, job control, and organizational justice, and employee wellbeing has been well established. However, the exposure to adverse work environments is typically measured only using self-reported measures that are vulnerable to reporting bias, and thus any associations found may be explained by reverse causality. Using linear regression models and cross-lagged structural equation modeling (SEM), we tested the direction of the association between established job stress models (job demand control and organizational justice models) and 3 wellbeing indicators (psychological distress, sleeping problems, and job satisfaction) among 1524 physicians in a 4-year follow-up. Results from the longitudinal cross-lagged analyses showed that the direction of the association was from low justice to decreasing wellbeing rather than the reverse. Although the pattern was similar in job demands and job control, a reciprocal association was found between job control and psychological distress. Keywords: psychosocial factors, risk factors, sleeping problems, stress, working population

reversed causality. The direction of the association is important in making causal inferences and, therefore, in designing preventive interventions (Hemingway & Marmot, 1999). In this study, we specifically tested the direction of the association between work related psychosocial risks, such as high job demands, low job control, and organizational injustice, and wellbeing problems, including sleeping problems, psychological distress, and job dissatisfaction (Karasek & Theorell, 1990; Robbins, Ford & Tetrick, 2012). The two established occupational stress models used in epidemiological studies, the job demands– control model (Karasek & Theorell, 1990) and the organizational justice model (Colquitt, Greenberg, & Zapata-Phelan, 2005), focus on different environmental factors as wellbeing and health risks. The job demands– control (job-strain) model introduced by Karasek and Theorell (1990) includes two central components: high job demands (the need to work quickly and hard), and lack of control over skill use, time allocation, and organizational decisions. The theory suggests that workers who have concurrent high demands and low job control (experience job strain) cannot moderate the stress caused by the high demands through time management or learning new skills. Thus, they become subject to high stress at work, and if ongoing, are at increased risk of psychological distress and disease. A more recent organizational justice model (Colquitt, Greenberg, & Zapata-Phelan, 2005) focuses on perceived unfairness in resource distribution, decision-making principles, and treatment

A large number of published studies support the risk status of the leading occupational stress models (Elovainio, Kivimaki, & Vahtera, 2002; Kivimäki, Nyberg, Batty, Fransson, & Heikkilä, et al., 2012). Recently, the direction of the association between work-related psychosocial factors and mental health problems has been questioned (Ybema & van den Bos, 2010; Lang, Bliese, Lang, & Adler, 2011). Although the occupational stress models suggest that environmental psychosocial factors affect wellbeing and health, it is reasonable to assume that also health problems and reduced wellbeing may affect the perception of environmental demands and challenges. Using self-reported measures of both environmental factors and potential outcomes creates a risk of

This article was published Online First February 23, 2015. Marko Elovainio, National Institute for Health and Welfare, Helsinki, Finland and Department of Behavioural Sciences, University of Helsinki; Tarja Heponiemi, National Institute for Health and Welfare, Helsinki, Finland; Markus Jokela and Christian Hakulinen, Department of Behavioural Sciences, University of Helsinki; Justin Presseau, Institute of Health and Society, Newcastle University; Anna-Mari Aalto, National Institute for Health and Welfare, Helsinki, Finland; Mika Kivimäki, Department of Epidemiology and Public Health, University College London. This study has been supported by the Finnish Work Environment Fund and the Academy of Finland. Correspondence concerning this article should be addressed to Marko Elovainio, National Institute for Health and Welfare, P.O. Box 30, 00270 Helsinki, Finland. E-mail: [email protected] 289


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practices within organizations (referred to as distributive, procedural and interactional justice; Lind & Tyler, 1988). Research on organizational justice as an individual’s perception of fairness in an organization, has mainly focused on two things: (a) what individuals perceive as being fair in organizations and (b) what the consequences of such perceptions might be. It has been shown that the perception of organizational justice is affected by a combination of rules and norms related to decision-making principles (procedural justice component) and treatment practices (interactional justice component) that people experience as being fair or unfair. When people within an organization perceive that they get what they deserve (their input and what they get back from the organization are in balance), that the rules treat them fairly (the decisions made follow fair rules), and that other people, especially their supervisors, treat them fairly (supervisors can be trusted and are respectful), they quite often think that organizational justice is high (for a review see Cropanzano et al., 2001). The justice researchers have long been interested in the consequences arising from organizational functioning, (Heponiemi et al., 2007; McFarlin & Sweeney, 1992; Moorman, 1991; Skarlicki & Folger, 1997), but only during the last two decades has the scope of research on organizational justice been expanded to employee wellbeing and health outcomes (Elovainio et al., 2001; Elovainio et al., 2002; Hausknecht et al., 2011; Schmitt & Dorfel, 1999; Tepper, 2001). The imbalance between job demands and control and low perceived justice are suggested to undermine wellbeing by generating psychological stress. The appraisal process is a basic assumption of classic and contemporary stress theories, suggesting that when people evaluate their capacity to cope with environmental challenges to be insufficient, they experienced psychological stress which leads to reduced wellbeing and health problems (Lazarus & Folkman, 1984). Psychological stress may affect wellbeing and health outcomes through direct activation of physiological neuroendocrine responses to stressors, or more indirectly through unhealthy behaviors such as smoking, lack of exercise, or excessive alcohol consumption performed to cope with perceived stressors, which increase the risk of deleterious effect on wellbeing and health (Hemingway & Marmot, 1999; Hemingway et al., 2003). One of the main axes of neuroendocrine stress responses is the autonomic nervous system (ANS). Stress may also affect dysregulation of the hypothalamic–pituitary–adrenal axis, which is associated with disturbances in the circadian rhythm of cortisol and the development of the metabolic outcomes. A large body of evidence suggests that the psychosocial work characteristics defined by the job demand– control are associated with wellbeing and health indicators, such as low heart rate variability (Chandola et al., 2008), progression of atherosclerosis (Rosenström et al., 2011), sickness absences (Zapf et al., 1996), poorer cognitive performance (Elovainio et al., 2012), metabolic syndrome (Chandola et al., 2006), and incident obesity (Brunner, Chandola, & Marmot, 2007). Similarly, it has been shown that there is a link between low organizational justice and experienced stress, psychological distress, depressive symptoms, sickness absence, lower cognitive performance, sleeping problems, and even cardiovascular disease and death (Elovainio et al., 2009; Elovainio, Kivimaki, & Helkama, 2001; Elovainio et al., 2002; Elovainio, Leino-Arjas, Vahtera, & Kivimaki, 2006; Elovainio et al., 2012; Ferrie et al., 2006; Kivimaki, Elovainio, Vahtera, & Ferrie, 2003; Kivimaki et al., 2003, 2006; Ndjaboue, Brisson, & Vezina, 2012;

Vermunt & Steensma, 2005; Tepper, 2001; Ybema & van den Bos, 2010). Multiple mechanisms linking psychosocial factors and wellbeing have been proposed and supported. It has been shown that a negative psychosocial work environment provokes negative emotional reactions, low self-esteem, social exclusion, and loss of physical resources, which in turn are closely related to problems in psychological wellbeing (Lind & Tyler, 1988; Karasek & Theorell, 1990; Elovainio et al., 2013). Men who experienced a high level of justice at work had a lower risk of incident metabolic syndrome than employees with a low level of justice (Gimeno et al., 2010), and low organizational justice was associated with increased circulating inflammatory marker C-reactive protein during a 10-year follow-up and increased interleukin-6 during a 5-year follow-up (Elovainio, Ferrie, et al., 2010). Although evidence supports the idea of psychosocial factors as causal factors predicting deteriorating health and wellbeing, alternative explanations are also plausible. It may be that health and wellbeing problems contribute to negative perceptions of the psychosocial environment. This would be especially probable in justice perceptions that are dependent on interpretations of social and relational factors, but may also affect perceptions of job demands and even job control. Ybema and van den Bos (2010) tested the reversed associations between depression, sickness absenteeism, and organizational justice dimensions in a representative sample of 1519 employees. According to their results, stronger evidence supported the role of justice dimensions as a predictor of depressive symptoms and absenteeism than the reverse. Jessica Lang and her coworkers (2011), however, found that among military personnel, depressive symptoms led to subsequent lower organizational justice perceptions. Furthermore, they found no evidence supporting the hypothesis that organizational justice perceptions would predict depressive symptoms. The authors concluded, in accordance with the assumption of affective perception, that depressed individuals perceive events more negatively. They also suggested that depressed individuals are also treated differently in reaction to their depressed state. Severe mental health problems, such as depression, inevitably affect the way an individual perceives and experiences his or her environment (Gotlib & Joormann, 2010). However, little is known about the effects of less severe stress-induced psychological states, such as psychological distress, sleeping problems, and dissatisfaction. According to the equity theory (Adams, 1963), being stressed should motivate people to select less stressful tasks and contexts and in this way try to make their environment less demanding. However, this option is not often possible in real life. It is reasonable to assume that when feeling stressed, people are less able to use adaptive coping, such as actively rearranging or prioritizing tasks. Thus, stress may reduce the sense of or actual resources people have to cope with the demands at work and they may experience their environment as more strenuous and demanding than it otherwise would be. This may be attributable to perception or be the result of less capacity to undertake work tasks because of reduced physical or cognitive capacity. For instance, sleeping problems are often associated with tiredness that in turn may affect actual abilities to cope with the cognitive or physical demands at work (Kristof-Brown et al., 2005). Thus, we formed three hypotheses: (a) negative work-related psychosocial factors (low organizational justice, high demands and


STRESSFUL WORK ENVIRONMENT

low control) predict decreasing wellbeing; (b) problems in wellbeing (psychological distress, sleeping problems and job dissatisfaction) predict increasingly negative perceptions of work-related psychosocial factors; or (c) there are reciprocal associations between negative work-related factors and wellbeing. The current study expands on previous evidence by testing the associations with longer follow-up times (latency period) than previous studies. Previous studies have also tested the reciprocal associations using only depression or depressive symptoms as the wellbeing indicator (Lang et al., 2011; Ybema & van den Bos, 2010). We tested the direction of the association between less severe wellbeing problems, such as sleeping problems, psychological distress, and job dissatisfaction, which have previously been suggested as outcomes of a strenuous work environment (Karasek & Theorell, 1990; Robbins, Ford & Tetrick, 2012). Both job strain and organizational justice have been associated with sleeping problems (de Jonge et al., 2000; Elovainio et al., 2011), psychological distress measured by the GHQ (Stansfeld, North, White, & Marmot, 1995; Elovainio, Kivimäki, & Vahtera, 2002), and job satisfaction (de Jonge et al., 2000; Colquitt, 2001). As for many other psychosocial factors at work, a plausible mechanism through which perceived job strain and organizational injustice may affect mental health, dissatisfaction, and sleeping problems is prolonged stress. Both job strain and perceived injustice has been shown to be associated with experienced stress reactions/distress (Elovainio, Kivimäki, & Helkama, 2001; Stansfeld et al., 1995). Disturbed sleep is another marker of prolonged stress, which is considered a common indicator of prolonged negative emotional states and related physiological changes (Espie, 2002).

Method The present study was part of the Finnish Health Care Professionals Study, in which a random sample of 5000 physicians in Finland (30% of the whole physician population) was drawn from the 2006 database of physicians maintained by the Finnish Medical Association. The register covers all licensed physicians in Finland. Phase 1 data were gathered using postal questionnaires in 2006. Responses were received from 2841 physicians (response rate 57%). The representativeness of the sample (in the first data collection phase) compared with the eligible population was tested and reported in a previous study (Elovainio et al., 2007), suggesting that the sample was representative in terms of age, gender, and employment sector. There were no statistically significant differences between those who participated to the follow-up study and those who participated to the baseline study. In phase 2 (four years later in 2010), the data were gathered by using either a web-based or a traditional postal survey. In phase 1, the consent to receive follow-up surveys was obtained from 2206 participants. Those who had died or who had incorrect address information were excluded (n ⫽ 37); thus, at phase 2 the survey was sent to 2169 physicians. The total number of respondents was 1705 (response rate 79%). Of these, 181 had incomplete data and were excluded and so the final study sample includes 1524 physicians (61% women) aged 24 – 69 (M ⫽ 49.7) years (2010).

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Measures Job demands were measured by 5 items (␣ ⫽ .85 in 2006 and 0.90 in 2010) derived from Harris’ (1989) stress index. The items were I have to work very hard, My job involves an excessive amount of work, I don’t have enough time to get my work done properly, We do not have enough resources to do our job, and We do not have enough personnel to do our job. All items used a 5-point Likert-type response format ranging from 1 (strongly disagree) to 5 (strongly agree). These items were used because they were very similar to those in the original Job Contents Questionnaire used by Karasek (1979) when the concept was first developed. Job control was measured by 3 items (␣ ⫽ .77 in 2006 and 0.76 in 2010) derived from Karasek’s Job Content Questionnaire (JCQ) (Karasek, 1979; Karasek, Baker, Marxer, Ahlbom, & Theorell, 1981), which measured the freedom to make independent decisions and possibilities to choose how to perform work (e.g., I have a lot of say about what happens in my job). The items were rated on a 5-point Likert-scale, ranging from 1 (totally disagree) to 5 (totally agree). Organizational justice was assessed using a short 8-item version (Elovainio, Heponiemi, Kuusio, et al., 2010) of Colquitt’s organizational justice measure (Colquitt, 2001). This shorter version has shown satisfactory psychometric properties (internal consistency and a good model fit to the data) and criterion validity (Elovainio, Heponiemi, Kuusio, et al., 2010). The items were rated on a 5-point Likert-scale, ranging from 1 (totally disagree) to 5 (totally agree). Examples of the items include: Have you been able to express your views and feelings during procedures used to arrive at your outcome?, Has the authority figure who enacted the procedure treated you with dignity?, and Does your outcome reflect the effort you have put into your work? With respect to the internal consistency, the alpha coefficient for this sample was 0.84 in 2006 and 0.86 in 2010. The complete justice scale was divided into three dimensions: procedural justice (three items, ␣ ⫽ .70 in 2006 and 0.74 in 2010), interpersonal justice (three items, ␣ ⫽ .79 in 2006 and 0.91 in 2010), and distributive justice (two items, ␣ ⫽ .94 in 2006 and 0.94 in 2010). Psychological distress was measured with 4 items (␣ ⫽ .84 in 2006 and 0.79 in 2010) from the GHQ-12 (Goldberg, 1972), which measured anxiety/depression, as suggested by Graetz (1991). Graetz’s three-factor structure has been suggested as the most preferable factor model for the GHQ-12 (Penninkilampi-Kerola, Miettunen, & Ebeling, 2006). The respondents rated how much they were affected by each of the 12 listed symptoms of psychological distress over the previous few weeks (1 ⫽ not at all, 2 ⫽ the same as usual, 3 ⫽ rather more than usual, or 4 ⫽ much more than usual). GHQ score (range 1– 4) was the mean of all items. In this study, the scale was used as a continuous variable. Sleeping problems were measured with 4 items (␣ ⫽ .77 in 2006 and 0.77 in 2010) derived from the Jenkins scale (Jenkins, Stanton, Niemcryk, & Rose, 1988). Respondents were asked how often during the last four weeks they had: trouble falling asleep, woken up several times per night, trouble staying asleep including waking up too early, and felt tired after the usual amount of sleep. The scale ranged from 1 (never) to 6 (every night). Job satisfaction was assessed with the mean of 3 items (␣ ⫽ .66 in 2006 and 0.86 in 2010) derived from Hackman and Oldham’s

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(1975) Job Diagnostic Survey on a 5-point scale, ranging from 1 (totally disagree) to 5 (totally agree), with items such as I am generally satisfied with my work. (In 2006 job satisfaction was measured using the original 7-point scale, and it was rescaled to be comparable with the 5-point scale by dividing the items by 7 and multiplying by 5.) Cronbach’s alpha coefficient for this study was 0.66 at phase 1 and 0.88 at phase 2. Covariates included gender, age, and employment sector (hospital, primary care, private, and the category ‘other’) and were measured in 2010.

Table 1 Characteristics of the Study Sample (n ⫽ 1370 –1524) Characteristic Gender Women Men Employment sector Hospital Primary care Private Other


Statistical Analyses To estimate two-way antecedent– consequence relationships between work-related psychosocial factors and wellbeing simultaneously, we fitted cross-lagged structural equation models (SEM). The goodness-of-fit of the models was assessed by (a) the ␹2 test where the higher the p value, the better the fit of the data, and (b) the standardized root means square residual (SRMR). We also used fit indexes less sensitive to number of observations/parameters, such as CFI and TLI (Byrne, 2011). In comparing alternative models, a statistically significant improvement in the ␹2 value indicated a better fit of the model. All models were tested separately for organizational justice, job demands, and job control. The contribution of the potential confounding factors at baseline to the relationships between work-related factors and wellbeing indicators was taken into account by using adjusted values (residual values) for the work-related psychosocial factors and wellbeing outcomes in the SEM models. The contribution of the potential confounding factors at baseline to the relationships between workrelated factors and wellbeing indicators was taken into account by using adjusted values (residual values) for the work-related psychosocial factors and wellbeing outcomes in the SEM models. The adjusted values were predicted using linear regression models performed separately for each work related factors and wellbeing indicators including all potential confounders (gender, age, and employment sector). We controlled the models for gender and age, because it has been show that women and younger, less experienced physicians report more problems in wellbeing and also experience their work as more strenuous than males and more experienced physicians (Elovainio, Heponiemi, Vänskä, et al., 2007). Furthermore, GPs in Finland have reported more workrelated problems and wellbeing problems than other physicians, and thus we adjusted models for employment sector (Heponiemi et al., 2013). The measurement invariance (Schmitt & Kuljanin, 2008) for the organizational justice latent variable over the study phases was tested using strict invariance (loadings, intercepts & residuals are invariant) and the test showed a good fit for the measurement model, ␹2(12) ⫽ 39.04, SRMR ⫽ 0.022, CFI ⫽ .98, TLI ⫽ 0.98). Composite scores on the three justice measures served as indicators of the latent justice constructs whereas individual items served as indicators for job demands and job control. The wellbeing indicators were added as composite scores in the SEM models. The analyses were performed using STATA/SE v.12.0® [StataCorp, 2005].

Results Table 1 shows the characteristics of the study sample. Most of the participants were women and worked at hospitals. The mean

Age 2010 (24–67) Procedural justice T1 Procedural justice T2 Interactional justice T1 Interactional justice T2 Distributive justice T1 Distributive justice T2 Job control T1 Job control T2 Job demands T1 Job demands T2 Distress T1 (1–4) Distress T2 (1–4) Sleeping problems T1 (1–6) Sleeping problems T2 (1–6) Job satisfaction T1 (1–5) Job satisfaction T2 (1–5)

n

%

914 605

60.2 39.8

696 327 256 245

45.7 21.5 16.7 16.1

Mean

SD

49.7 3.31 3.30 3.57 3.51 2.93 3.06 3.92 3.79 3.58 3.01 1.91 1.83 2.30 2.35 3.49 3.04

9.47 0.75 0.76 0.96 1.00 1.02 1.05 0.80 0.83 0.96 0.95 0.61 0.66 1.00 1.05 0.52 0.53

levels of distress, job demands, and job control slightly decreased during the study period, but other average changes were relatively small. Table 2 shows the correlations between the study variables. All of the outcomes were at least modestly associated with all of the work-related psychosocial factors. The cross-lagged structural equation modeling in organizational justice and wellbeing outcomes were fitted with justice as a latent variable (see Figure 1). The model incorporating the path from earlier justice to later distress, ␹2(15) ⫽ 100,35, SRMR ⫽ 0.039, CFI ⫽ .96, TLI ⫽ 0.92, sleeping problems, ␹2(15) ⫽ 53.05, SRMR ⫽ 0.026, CFI ⫽ .98, TLI ⫽ 0.97, and job satisfaction, ␹2(15) ⫽ 83.28, SRMR ⫽ 0.039, CFI ⫽ .96, TLI ⫽ 0.93, all showed a reasonably good fit. The association between justice and distress (coefficients ⫺.11, z ⫽ ⫺3.75, p ⬍ .001) and between justice and job satisfaction (coefficients .22, z ⫽ 6.31, p ⬍ .001) were significant. None of the models that tested the paths also from earlier wellbeing indicators produced better fits (range of ⌬␹2(1) from 0.22 to 1.2; see Figure 1). None of the associations from earlier wellbeing indicators to later justice evaluations (coefficients 0.02, z ⫽ 0.71; 0.02, z ⫽ 0.40; ⫺0.01, z ⫽ ⫺0.46) were significant. The model fit was reasonable for the model testing the associations from earlier job demands with later distress, ␹22(15) ⫽ 157.39, SRMR ⫽ 0.060, CFI ⫽ .97, TLI ⫽ 0.94, and the association between demands and distress was significant (coefficient 0.12, z ⫽ 4.21, p ⬍ .001). The model testing the reciprocal effects was better, ␹2(14) ⫽ 152.03, SRMR ⫽ 0.054, CFI ⫽ .97, TLI ⫽ 0.94, and the effect of earlier distress on later job demands was significant (coefficient 0.07, z ⫽ 2.53. p ⫽ .011). The model incorporating the path from earlier job de-


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Table 2 Correlations (Pearson R) Between Study Variables (n ⫽ 1369 –1524)


Variable 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16.

Procedural justice T1 Procedural justice T2 Interactional justice T1 Interactional justice T2 Distributive justice T1 Distributive justice T2 Job control T1 Job control T2 Job demands T1 Job demands T2 Distress T1 Distress T2 Sleeping prob. T1 Sleeping prob. T2 Job satisfaction T1 Job satisfaction T2

Note.

1

2

3

4

.309 .220 .140 .223 .119 ⫺.097 ⫺.068 ⫺.134 ⫺.082 ⫺.088 ⫺.054 .209 .130

1 .139 .321 .109 .295 ⫺.029 ⫺.135 ⫺.085 ⫺.205 ⫺.051 ⫺.093 .125 .195

5

6

7

8

9

10

11

12

13

14

15

16

1 .309 .493 .233 .357 .177 .413 .191 ⫺.203 ⫺.124 ⫺.211 ⫺.168 ⫺.146 ⫺.122 .288 .173

1 .257 .565 .177 .299 .202 .382 ⫺.053 ⫺.123 ⫺.073 ⫺.194 ⫺.062 ⫺.127 .145 .243

1 1 .358 .263 .125 ⫺.209 ⫺.092 ⫺.154 ⫺.073 ⫺.125 ⫺.069 .165 .107

1 .116 .258 ⫺.094 ⫺.233 ⫺.095 ⫺.172 ⫺.072 ⫺.107 .112 .186

1 .363 ⫺.117 ⫺.136 ⫺.190 ⫺.145 ⫺.166 ⫺.143 .223 .157

1 ⫺.055 1 ⫺.246 .372 1 ⫺.131 .298 .218 1 ⫺.196 .205 .37 .42 1 ⫺.112 .203 .125 .526 .328 1 ⫺.156 .146 .191 .352 .544 .59 1 .174 ⫺.235 ⫺.117 ⫺.210 ⫺.126 ⫺.162 ⫺.157 1 .298 ⫺.092 ⫺.212 ⫺.148 ⫺.207 ⫺.143 ⫺.203 .217 1

All correlations above 0.05 statistically significant.

mands to later sleeping problems showed a reasonable fit, ␹2(15) ⫽ 42.44 SRMR ⫽ 0.028, CFI ⫽ .99, TLI ⫽ 0.98, but the association between demands and sleeping problems was not statistically significant (coefficient 0.025, z ⫽ 1.07, p ⫽ .284). The model testing the reciprocal effects was only slightly better, ␹2(14) ⫽ 41.21, SRMR ⫽ 0.025, CFI ⫽ .99, TLI ⫽ 0.99, and the effect of earlier sleeping problems on later job demands was not significant (coefficients 0.029, z ⫽ 1.10, p ⫽ .272). Earlier job demands predicted later job satisfaction (coefficient ⫺0.13, z ⫽ ⫺4.52. p ⬍ .001) and fit of the model was good, ␹2(15) ⫽ 72.10, SRMR ⫽ 0.039, CFI ⫽ .99, TLI ⫽ 0.97, and slightly worse than the model testing the reciprocal associations, ⌬␹2(1) 0.74). The association between earlier job satisfaction and later job demands was not significant (p ⫽ .494; see Figure 2). The models including the path from earlier job control to later psychological distress, ␹2(15) ⫽ 80.69, SRMR ⫽ 0.036, CFI ⫽ .98, TLI ⫽ 0.96, and sleeping problems, ␹2(15) ⫽ 49.01, SRMR ⫽ 0.027, CFI ⫽ .99, TLI ⫽ 0.98, showed a reasonable fit and the association between job control and distress (coefficients ⫺0.058, z ⫽ ⫺2.10, p ⫽ .036) was significant. The models testing the reciprocal effects gave only slightly better fits, ␹2(14) ⫽ 77.96, SRMR ⫽ 0.033, CFI ⫽ .98, TLI ⫽ 0.96/␹2(14) ⫽ 46.78, SRMR ⫽ 0.025, CFI ⫽ .99, TLI ⫽ 0.98. The association between earlier distress (coefficients ⫺.044, z ⫽ ⫺1.65, p ⫽ .099) and later job control, or between earlier sleeping problems and later job control (coefficients ⫺0.042, z ⫽ 1.52, p ⫽ .056) were not significant. The model testing the association between job control and later job satisfaction show a reasonably good fit, ␹2(15) ⫽ 112.20, SRMR ⫽ 0.049, CFI ⫽ .97, TLI ⫽ 0.95, and testing the reciprocal associations did not much improve the fit, ␹2(14) ⫽ 110.85, SRMR ⫽ 0.048, CFI ⫽ .97, TLI ⫽ 0.93. The association from earlier job control with later job satisfaction was significant (coefficient 0.15, z ⫽ 5.10, p ⬍ .001), whereas the association to opposite direction was not (coefficient 0.036, z ⫽ 1.20, p ⫽ .230; see Figure 3).

Discussion This study addresses the following hypotheses: (a) negative work-related psychosocial factors defined by the organizational justice and job strain models (low organizational justice, high demands and low control) predict decreasing wellbeing and (b) problems in wellbeing (psychological distress, sleeping problems and job dissatisfaction) predict increasingly negative perceptions of psychosocial work environment. We found more evidence supporting the former hypothesis than the latter. In organizational justice the direction of the association was consistently from justice perceptions to later wellbeing. In job demands the associations were less clear. Psychological distress seemed to predict lower job demands in the future, although the association was reciprocal. This may reflect, of course, real changes in job demands or rather perceptions. Distress and subsequent reduced psychological resources may increase the perception of increasing demands or abilities to cope with the existing level of demands. Our results contradict those of Lang and colleagues (2011), which suggested a reversed causation (depressive symptoms predicted justice evaluations), and are more in line with those of Ybema and van den Bos (2010), which offered support for the causal role of justice in predicting later depressive symptoms. The follow-up time in the Lang et al. study (2011) was considerably shorter (less than a year) than the one in the current study (four years), and that may explain some of the differences. It is possible that perceived injustice affects wellbeing only when the exposure is long-lasting and the reversed association masks the causal effect of the perceived psychosocial environment. This reasoning is supported by the fact that the follow-up time of the Ybema and van den Bos study (2010) was also relatively long. The follow-up time of our study was 4 years, which is relatively long, but there are many studies showing effects of, for instance, organizational injustice and sleeping problems (Elovainio et al., 2010) and psychological distress (Ferrie et al., 2006) that have used longer follow-up times. It is probable, in fact, that relatively long exposure is needed for severe forms of distress and sleeping problems to develop.


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Figure 1.

Cross-lagged models of the associations between organizational justice and wellbeing indicators.



Figure 2.

Cross-lagged models of the associations between job demands and wellbeing indicators.

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Figure 3.

Cross-lagged models of the associations between job control and wellbeing indicators.



There may be a problem when testing the direction of the association between two factors if one factor is more stable than the other. However, our cross-lagged models suggested a relatively high test-retest stability for both justice/strain measures and sleeping problems/GHQ. In job satisfaction, this may be a problem because of temporal instability. One explanation for the differences could be that different occupational groups were studied. It may be easier for a physician to change job than for someone working in the military. The most obvious reason is, however, the wellbeing indicator used. Depression changes the way the environment is perceived almost by definition, but the effects of less severe psychological problems are less evident. The previous studies used solely depressive symptoms or depression as the wellbeing indicator, whereas we measured psychological distress, sleeping problems, and job satisfaction. However, the version of the distress measure used was the one that has been shown to overlap with measures of depressive symptoms quite well (Aalto et al., 2012). Thus, future studies should test the direction of the association between psychosocial work environment and wellbeing by using a wide range of wellbeing indicators, so as to offer a more complete theoretical frame for understanding the dynamic nature of the associations between psychosocial work environment and employee wellbeing. Our results can be interpreted as supporting the general stress model, suggesting that environmental stimuli that are appraised as a threat to oneself cause stress (Lazarus, 1993; Lazarus & Folkman, 1984) and that exposure to such threats may lead to health problems and reduced wellbeing (McEwen, 1998, 2000). Most of the leading occupational stress theories suggest that the fundamental stress factor, and in fact the most important effect modifier of any other stress factors, is control over the things affecting one’s work process and environment (Karasek & Theorell, 1990). Similarly, the uncertainty management model of organizational justice is based on the idea that a fundamental dilemma is caused by giving up autonomy, in other words the ability to fully control the environment and decisions concerning factors affecting oneself (see, e.g., van den Bos, 2001; van den Bos & Lind, 2002) and the self-threatening condition that that lack of control creates (see, e.g., Miedema, van den Bos, & Vermunt, 2006). Our findings are also in line with previous studies that have suggested that psychological distress, problems with sleep, and low job satisfaction are common after exposure to high job strain or low justice (Elovainio et al., 2001; Elovainio et al., 2002; Elovainio, Kivimaki, Vahtera, Keltikangas-Jarvinen, & Virtanen, 2003; Elovainio, Leino-Arjas, et al., 2006a; Ferrie et al., 2006; Karasek & Theorell, 1990; Kivimäki et al., 2006; Kivimäki et al., 2012). By characterizing exposure to job strain and organizational justice and the wellbeing outcomes with measures obtained at two time points, we were able to conduct a prospective study of the long-term effects of psychosocial risks. The job strain model is probably the most widely tested model of psychosocial risks in the area of occupational health psychology (for reviews see Kivimäki et al., 2012), and the evidence supporting the model is strong. All previous organizational justice studies that have used a single measure of exposure have shown an association between low organizational justice and a greater number of sleeping problems in two samples of women (Elovainio, Kivimaki, et al., 2003) and between organizational justice and distress (Kivimaki, Elovainio, Vahtera, & Ferrie, 2003; Ylipaavalniemi et al., 2005). Previous

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studies have demonstrated cross-sectional associations between organizational justice and prevalent depressive symptoms (Elovainio et al., 2002), and longitudinal associations between low justice at baseline and psychiatric morbidity two years later (Kivimäki et al., 2003). Previous findings from the Whitehall II study cohort showed that those who reported at baseline that they were treated unfairly by their supervisors had a higher risk of incident psychiatric morbidity three years and six years later (Ferrie et al., 2006). Our results suggest that the predictive effects of justice and job control on wellbeing are apparent also even when accounting for age, sex, and working sector. Especially sex but also age has been associated with all the wellbeing outcomes used in this study (Hemingway & Marmot, 1999). These adjustments are not, of course, sufficient to rule out the possibility of residual confounding. Although the GHQ scale used in this study has been shown to have good predictive validity in previous studies (Goldberg, 1972), use of clinical interviews or more established measures of mental health problems, including distress, would have been preferable. In interpreting the present results, it is important to note some additional limitations. The main limitation of this study is that all the participants were physicians and our findings may not apply to wider populations. Thus, the results should be further tested using other occupational groups. Physicians are, however, a good focal occupation for several reasons. It is useful sometimes to test your hypothesis in one relatively homogeneous occupational group to avoid differences in health and wellbeing problems that are related to socioeconomic differences (Elovainio et al., 2011). Physicians are considered a high socioeconomic group with a strenuous work environment. Physician work is cognitively and mentally demanding, involving long work hours, on-call work, and high responsibility and thus it is no surprise that physicians have been shown to have more wellbeing problems than people working in other high socioeconomic occupations (Virtanen et al., 2007). It is also possible that personality traits, such as neuroticism or trait anxiety, may act as predisposing factors for disturbed sleep (Elovainio, Kivimäki, Vahtera, Virtanen, & Keltikangas-Jarvinen, 2003), distress, or job satisfaction. Such dispositions also affect the way people experience their (psychosocial work) environment as a source of negative emotions and the way they respond with mood changes following stressors in their daily life. However, earlier research has shown that incident mental disorders can be predicted by organizational justice measured not just with individual selfreports but also with work-unit mean scores (Kivimaki et al., 2003). A further potential limitation is common method variance, a potential source of inflated associations. Unfortunately, objective measures of, for instance, sleep problems, such as actigraphy and polysomnography, are very difficult to implement in large epidemiological studies such as this. Besides the two job stress models detailed above, there are numerous other conceptualizations, including the effort–reward imbalance (ERI) model, which suggests that people get stressed when they are placed in demanding situations without being rewarded adequately. ERI is widely tested and supported psychosocial health risk (Siegrist, 1996) that has been shown to predict emotional exhaustion (de Jonge et al., 2000) and cardiovascular heart disease (Kuper et al., 2002). From the justice model perspective, ERI is basically one of the organizational justice dimensions often called distributive justice. Thus, ERI is basically included


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theoretically in the organizational justice model even in the narrow definitions (Greenberg & Colquitt, 2005). This study has the benefit of using longitudinal data. All the analyses were conducted using participants with complete data on the measured variables. This meant that 30% of the original population was lost to follow-up because of missing data. However, any differences in baseline characteristics between those included and excluded were small, suggesting that a major bias is unlikely and thus the results probably were not attributable to selection effect. We do not have information on whether some of the participants had changed their jobs during the follow up. It may be that those who changed jobs and still perceived similar levels of injustice would be more negatively affected compared with those who stayed where they were and experienced similar conditions, but it is difficult to evaluate whether this has any effect on the detected cross-lagged associations. We tested the associations between job characteristics and wellbeing indicators using crosslagged structural equation modeling, which is basically the only method that tests both directions of the associations simultaneously. We were thereby able to model the reciprocal associations between the job characteristics and wellbeing indicators. We had a fixed follow-up time for each participant and thus there was no need for exact discrete modeling, which is capable of taking into account the various follow-up times between longitudinal measures. Over repeated follow-ups, autoregressive effects can only decline and the cross-lagged effects would exhibit a peak at a certain time window and thus using different time frames in discrete modeling may show arbitrarily different associations. The availability of long-term repeated data from only two occasions prevented us from using other, more complicated models, such as latent growth curve models. More follow-up measurement points would have helped to eliminate the possibility that differences in temporal stability between job characteristics and wellbeing would have biased the observed direction of the associations between the two (if in the long run job characteristics would be more stable than wellbeing over time). Although the present study suggests that the direction of the association between adverse psychosocial environments and employee wellbeing is rather in the direction from adverse psychosocial environments toward wellbeing outcomes than the reverse, some evidence supporting strain-to-stressor effects was also found. Thus, the idea best supported by current evidence is that where a stressful environment causes reduced wellbeing, these two may start to reinforce each other in a vicious circle. Our finding suggests, however, that the fundamental perceptions of work related negative factors in our environment may not be understood as an outcome of the psychological states of employees. These findings suggest that the work related psychosocial factors even when measured using self-reported indicators rather precede wellbeing than are outcomes of psychological states. It has been suggested that especially organizational justice perception would be affected by psychological factors, such as depressive symptoms (Lang et al., 2011). This reversed causality may be attributable to more negative perception of environmental factors, such as organizational decision-making procedures among depressed people, or actual differences in treatment of depressed people in organizations. Our results do not support the reversed causality of justice perception and either of these explanations. Reduced wellbeing seems not to affect justice perception, and this means that justice

perception may be a strong candidate for being a work-related psychosocial factor that may have causal effects on employee wellbeing, although there is a subjective component in justice perception that seems not to account for the real wellbeing effects. Our results supported similar conclusions concerning job control. However, perception of job demands may be more vulnerable to subjective bias and thus the association between job demands and wellbeing may be partly explained by reversed causality. It may be that reduced wellbeing (sleeping problems and distress) in fact means that people do not have cognitive or physical resources needed to cope with the demands at work and thus they perceive their job demands higher. It is less probable that people with reduced wellbeing would be systematically selected to more demanding jobs. These results basically suggest that interventions should be targeted to fair managerial and decision making procedures, increasing job control and reducing job demands. In addition, if the aim is specifically to reduce job demands only then it may be useful to find risk groups already experiencing wellbeing problems, such as poor sleep of psychological distress.

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Received October 16, 2013 Revision received October 10, 2014 Accepted November 3, 2014 䡲

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What Comes First, Job Burnout or Secondary Traumatic Stress? Findings from Two Longitudinal Studies from the U.S. and Poland.

What can "thematic analysis" offer health and wellbeing researchers?

What can health and wellbeing boards do for us?

What can "thematic analysis" offer health and wellbeing researchers?

Governance for health, wellbeing and sustainability--what is at stake.

What can "thematic analysis" offer health and wellbeing researchers?

Nonalcoholic fatty liver disease and cholesterol gallstones: which comes first?

Satisfying and stressful experiences of first-time federal grantees.

Hospital nurses' wellbeing at work: a theoretical model.

Healthy work environment--a challenge?