Reported incidence and precipitating factors of work-related stress and mental ill-health in the United Kingdom (1996–2001)
Bibliographic record
Abstract
BACKGROUND: Work-related mental ill-health appears to be increasing. Population-based data on incidence are scarce but in the United Kingdom occupational physicians and psychiatrists report these conditions to voluntary surveillance schemes. AIMS: To estimate the incidence of work-related stress and mental illness reported 1996-2001 by occupational physicians and 1999-2001 by psychiatrists. METHODS: Estimated annual average incidence rates were calculated by sex, occupation and industry against appropriate populations at risk. An in-house coding scheme was used to classify and analyse data on precipitating events. RESULTS: An estimated annual average of 3,624 new cases were reported by psychiatrists, and 2,718 by occupational physicians; the rates were higher for men in reports based on the former and for women on the latter. Most diagnoses were of anxiety/depression or work-related stress, with post-traumatic stress accounting for approximately 10% of cases reported by psychiatrists. High rates of ill-health were seen among professional and associated workers and in those in personal and protective services. Factors (such as work overload) intrinsic to the job and issues with interpersonal relations were the most common causes overall. CONCLUSIONS: The steep increase in new cases of work-related mental ill-health reported by occupational physicians since 1996 may reflect a greater willingness by workers to seek help but may also signify an increasing dissonance between workers' expectations and the work environment. Greater expertise is needed to improve the workplace by adjustment of job demands, improvement of working relations, increasing workers' capacities and management of organizational change.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".