Overtime Work as a Predictor of Major Depressive Episode: A 5-Year Follow-Up of the Whitehall II Study
Bibliographic record
Abstract
BACKGROUND: The association between overtime work and depression is still unclear. This study examined the association between overtime work and the onset of a major depressive episode (MDE). METHODOLOGY/PRINCIPAL FINDINGS: Prospective cohort study with a baseline examination of working hours, psychological morbidity (an indicator of baseline depression) and depression risk factors in 1991-1993 and a follow-up of major depressive episode in 1997-1999 (mean follow-up 5.8 years) among British civil servants (the Whitehall II study; 1626 men, 497 women, mean age 47 years at baseline). Onset of 12-month MDE was assessed by the Composite International Diagnostic Interview (CIDI) at follow-up. In prospective analysis of participants with no psychological morbidity at baseline, the odds ratio for a subsequent major depressive episode was 2.43 (95% confidence interval 1.11 to 5.30) times higher for those working 11+ hours a day compared to employees working 7-8 hours a day, when adjusted for socio-demographic factors at baseline. Further adjustment for chronic physical disease, smoking, alcohol use, job strain and work-related social support had little effect on this association (odds ratio 2.52; 95% confidence interval 1.12 to 5.65). CONCLUSIONS/SIGNIFICANCE: Data from middle-aged civil servants suggest that working long hours of overtime may predispose to major depressive episodes.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".