Effort–reward imbalance and medically certified absence for mental health problems: a prospective study of white-collar workers
Bibliographic record
Abstract
OBJECTIVE: Little is known about the effects of psychosocial work factors on objectively assessed mental health problems leading to medically certified absence. Only one study has evaluated the prospective effects of effort-reward imbalance (ERI) at work with regards to this outcome. The present study aimed to evaluate the effects of ERI on the incidence of medically certified absence for mental health problems. METHODS: The study included 2086 white-collar workers (63.3% women) employed in public organisations in Quebec city. Participants were followed over a 9-year period. Medical absences from work were collected from employers' files and psychosocial factors were measured using the ERI questionnaire. Cox regression models were used to estimate the incidence of certified sickness absence due to mental health problems that lasted 5 workdays or more, while controlling for confounders. RESULTS: Workers exposed to ERI had a higher risk of a first spell of medically certified absence for mental health problems (HR=1.38, 95% CI 1.08 to 1.76) compared with unexposed workers. Low reward was significantly associated with a high risk among men (HR=2.80, 95% CI 1.34 to 5.89) but not in women. (HR=1.24, 95% CI 0.90 to 1.73). Effort at work had no effect on certified absence. All these effects were adjusted for potential confounders. CONCLUSIONS: ERI and low reward at work were prospectively associated with medically certified absence for mental health problems. These effects seem to differ by gender. Primary prevention that is aimed at reducing these stressors should be considered to help reduce the incidence of such severe mental health problems.
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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 | 0.000 |
| 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 | 0.001 |
| 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".