A multilevel analysis of the role personality play between work organization conditions and psychological distress
Bibliographic record
Abstract
BACKGROUND: Psychological distress in the workplace is usually attributed to work-related variables as well as non-work-related variables. Individuals working in the same organization can differ in terms of their appraisal of work-related stressors and coping strategies used to face them. The present study aims to evaluate the moderating role personality plays between work organizations conditions and psychological distress in a large sample of Canadian participants working in various occupations and workplaces. METHODS: Multilevel regression analyses were conducted on a sample that followed a hierarchical structure with workers (N1 = 1958) nested in workplaces (N2 = 63). The direct contribution of workplace and personality was tested in a variance component model as a first step. Following this initial step, we introduced interaction variables by blocks of 11. Those interaction variables refer to each interaction combined with a specific personality variable. RESULTS: Psychological demands, number of hours worked, job insecurity, neuroticism, and agreeableness were associated with higher levels of psychological distress. Inversely, decision authority, job recognition, self-esteem, locus of control were associated with lower levels of psychological distress. Lastly, agreeableness played a moderating role between low social support garnered from one's supervisor and psychological distress. CONCLUSIONS: To intervene on work-related variables, organizations could reduce psychological demands, minimize the number of hours worked through job redesign, allow teleworking and encourage work schedule flexibility. To reduce job insecurity, organizations could explicitly communicate future organizational plans. In the same vein, decision authority could be targeted by reducing hierarchical steps and increasing autonomy. Lastly, the results pertaining to agreeableness stand in contrast with those of previous studies. We assumed that workers scoring high on agreeableness tend to put themselves last and please others first. These tendencies could make them more susceptible to health issues. With that said, work environments still need workers who are agreeable and nice to be around. To prevent high levels of agreeableness leading to psychological distress, training and information workshops are recommended. Those include stress management interventions and workshops pertaining to time management and relaxation techniques.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".