Differences between women and men in the relationship between psychosocial stressors at work and work absence due to mental health problem
Bibliographic record
Abstract
OBJECTIVES: Women have a higher incidence of mental health problems compared with men. Psychosocial stressors at work are associated with mental health problems. However, few prospective studies have examined the association between these stressors and objectively measured outcomes of mental health. Moreover, evidence regarding potential differences between women and men in this association is scarce and inconsistent. This study investigates whether psychosocial stressors at work are associated with the 7.5-year incidence of medically certified work absence due to a mental health problem, separately for women and men. METHODS: Data from a prospective cohort of white-collar workers in Canada (n=7138; 47.3% women) were used. We performed Cox regression models to examine the prospective association between self-reported psychosocial stressors at work (job strain model) at baseline and the 7.5-year HR of medically certified work absence of ≥5 days due to a mental health problem. RESULTS: During follow-up, 11.9% of participants had a certified work absence, with a twofold higher incidence among women. Women (HR 1.40, 95% CI 1.01 to 1.93) and men (HR 1.41, 95% CI 0.97 to 2.05) exposed to high strain (high demands and low control) had a higher incidence of work absence compared with those unexposed. Among women only, those exposed to an active job situation (high demands and high control) also had a higher risk (HR 1.82, 95% CI 1.29 to 2.56). CONCLUSIONS: Prevention efforts aimed at reducing psychosocial stressors at work could help lower the risk of work absence for both women and men. However, important differences between women and men need to be further studied in order to orient these efforts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".