Bibliographic record
Abstract
Aim: To use workers' compensation claims for occupational disease and illness over a 25-year period as an indicator for women's exposures at work.Methods: Accepted compensation claims and labour force statistics for workers in the Canadian province of British Columbia were used to calculate annual rates of occupational disease and illness for women compared to men from 1992 to 2016.Results: Over 86,000 compensation claims for occupational disease and illness were accepted during the study period, but 7% were missing data on gender. Infectious disease rates increased over time and were always higher for women than men (29 versus 4 cases per 100,000 in the last five years of follow-up). Hearing loss rates decreased over time and were always higher for men than women (24 versus 1 case per 100,000 in the last five years). Skin condition rates decreased over time and were similar for men and women (3 cases per 100,000 men and women in the last five years). Mental disorder rates increased over time but more so for women than men (from 9 cases per 100,000 in the first five years of follow-up for both men and women to 19 cases for men and 26 cases for women in the last five years). Small cell sizes (<5 cases annually) for cardiovascular and respiratory diseases and for neoplasms precluded rate calculations for women. Small cell sizes were also an issue for environmental exposures but, where comparisons were feasible, men always had higher rates than women.Discussion: Women were more likely to have claims related to infectious and mental stress exposures, while men were more likely to have claims related to environmental and noise exposures. An exception was skin conditions (dermatitis) with similar rates for men and women over time. Despite significant shifts in labour force participation by women, claims rates for occupational conditions remain highly gendered. Next steps include stratification of disease and illness rates by occupation.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".