Impact of common mental disorders on sickness absence in an occupational cohort study
Bibliographic record
Abstract
OBJECTIVES: Common mental disorders are associated with impaired functioning and sickness absence. We examine whether sub-clinical as well as clinical psychiatric morbidity predict long spells of sickness absence for both psychiatric and non-psychiatric illness. We also examine whether recent common mental disorders and those present on two occasions have a stronger association with sickness absence than less recent and single episodes of disorder. METHODS: Common mental disorders measured by the General Health Questionnaire were linked with long spells of sickness absence in 5104 civil servants from the longitudinal Whitehall II Study. Negative binomial models were used to estimate rate ratios for long spells of sickness absence with and without a psychiatric diagnosis (mean follow-up 5.3 years). RESULTS: Clinical but not sub-threshold common mental disorders were associated with increased risk of long spells of psychiatric sickness absence for men, but not for women, after adjusting for covariates (rate ratios (RR) 1.67, 95% CI 1.13 to 2.46). Risk of psychiatric sickness absence was associated with recent common mental disorders (RR 2.08, 95% CI 1.29 to 3.35) and disorder present on two occasions (RR 1.65, 95% CI 0.98 to 2.71) for men only. Common mental disorders were not associated with increased risk of non-psychiatric sickness absence after adjustment for covariates. CONCLUSIONS: Identification and treatment of common mental disorders may reduce the economic burden of long term psychiatric sickness absence. Our results suggest that public health and clinical services should focus on the identification of workers with elevated mental health symptoms. Studies are needed of the efficacy of early identification and management of mental health symptoms for the prevention of long spells of sickness absence.
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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 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".