Job stress as a preventable upstream determinant of common mental disorders: A review for practitioners and policy-makers
Bibliographic record
Abstract
There is growing recognition of the important role of mental health in the workforce and in the workplace. At the same time, there has been a rapid growth of studies linking job stress and other psychosocial working conditions to common mental disorders, and a corresponding increase in public concern media attention to job stress and its impact upon worker health and well-being. This article provides a summary of the relevant scientific and medical literature on this topic for practitioners and policy-makers. It presents a primer on job stress concepts, an overview of the evidence linking job stress and common mental disorders, a summary of the intervention research on ways to prevent and control job stress, and a discussion of the strengths and weakness of the evidence base. We conclude that there is strong evidence linking job stress and common mental disorders, and that it is a substantial problem on the population level. On a positive note, however, the job stress intervention evidence also shows that the problem is preventable and can be effectively addressed by a combination of work- and worker-directed intervention.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".