Trends in compensation for deaths from occupational cancer in Canada: a descriptive study
Bibliographic record
Abstract
BACKGROUND: Occupational cancer is the leading cause of work-related deaths, yet it is often unrecognized and under reported, and associated claims for compensation go unfiled. We sought to examine trends in deaths from occupational cancer, high-risk industries and exposures, and commonly compensated categories of occupational cancers. In addition, we compared deaths from occupational lung cancer for which compensation had been given with total deaths from lung cancer. METHODS: We used data from the Association of Workers' Compensation Boards of Canada pertaining to the nature and source of the injury or disease and the industry in which it occurred (by jurisdiction) to describe trends in compensated claims for deaths from occupational cancer in Canada for the period 1997-2010. We used data published by the Canadian Cancer Society in Canadian Cancer Statistics to compare compensated occupational lung cancer deaths with total estimated lung cancer deaths for the period between 2006 and 2010. RESULTS: Compensated claims for deaths from occupational cancer have increased in recent years and surpassed those for traumatic injuries and disorders in Canada, particularly in Ontario. Between 1997 and 2010, one-half of all compensated deaths from occupational cancer in Canada were from Ontario. High-risk industries for occupational cancer include manufacturing, construction, mining and, more recently, government services. Deaths from lung cancer and mesothelioma comprise most of the compensated claims for deaths from occupational cancer in Ontario and Canada. These diseases are usually the result of asbestos exposure. The burden of other occupational carcinogens is not reflected in claims data. INTERPRETATION: Although the number of accepted claims for deaths from occupational cancers has increased in recent years, these claims likely only represent a fraction of the true burden of this problem. Increased education of patients, workers at high risk of exposure and health care providers is needed to ensure that people with work-related cancer are identified and file a claim for compensation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.015 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".