Physician diagnosed mental ill-health in male and female workers
Bibliographic record
Abstract
BACKGROUND: Although there have been many studies of work demands and self-reported job strain, few have examined incident physician-diagnosed mental ill-health (MIH) by detailed occupational group. AIMS: To investigate whether linkage of occupation from worker compensation claims to diagnoses from administrative health records can give credible information on occupation and incidence of MIH by diagnostic group and gender. METHODS: Information on occupation from all worker compensation claims 1995-2004 in Alberta, Canada were linked to administrative health records of MIH diagnoses. Relative risks for affective, substance use and psychotic disorders by four digit occupational codes were calculated for men and women aged 18-65 years in a log-binomial regression adjusting for age and stratifying by sex. RESULTS: There were 327883 male and 88483 female compensation claims available for the analysis of incident cases. Affective disorders (5.2% men, 11.5% women) were much more common than substance use disorders or psychotic disorders (both ≤1%) in this population of working people. In men, the type of work appeared to either protect from or precipitate affective disorders, but no protective effect was seen for women. Substance use disorders clustered mainly in physically demanding occupations typically involving employment outside the urban areas. New onset psychotic disease was rare but seen in excess in painters, boilermakers and chefs. CONCLUSIONS: Data linkage of occupation close to the time of new onset MIH can provide important insight into the relation between work and physician-diagnosed MIH and indicate areas in which intervention might be appropriate.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".