The relationship between work stress and mental disorders in men and women: findings from a population-based study
Bibliographic record
Abstract
OBJECTIVES: [corrected] This analysis estimated the gender-specific associations between work stress, major depression, anxiety disorders and any mental disorder, adjusting for the effects of demographic, socioeconomic, psychological and clinical variables. METHODS: Data from the Canadian national mental health survey were used to examine the gender-specific relationships between work stress dimensions and mental disorders in the working population (n = 24,277). Mental disorders were assessed using a modified version of the World Mental Health - Composite International Diagnostic Interview. RESULTS: In multivariate analysis, male workers who reported high demand and low control in the workplace were more likely to have had major depression (OR 1.74, 95% CI 1.12 to 2.69) and any depressive or anxiety disorders (OR 1.47, 95% CI 1.05 to 2.04) in the past 12 months. In women, high demand and low control was only associated with having any depressive or anxiety disorder (OR 1.39, 95% CI 1.05 to 1.84). Job insecurity was positively associated with major depression in men but not in women. Imbalance between work and family life was the strongest factor associated with having mental disorders, regardless of gender. CONCLUSIONS: Policies improving the work environment may have positive effects on workers' mental health status. Imbalance between work and family life may be a stronger risk factor than work stress for mental disorders. Longitudinal studies incorporating important workplace health research models are needed to delineate causal relationships between work characteristics and mental disorders.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".