Compensation for Musculoskeletal Disorders in Quebec: Systemic Discrimination against Women Workers?
Bibliographic record
Abstract
Criteria for evaluating workers' compensation claims for occupational disease are strongly linked to medical expertise as supported by scientific study, yet decision-makers are not necessarily familiar with the meaning of these studies. While this is a problem for all claimants, who bear the burden of proving that work caused their injury, the adverse impact of misunderstanding of scientific data can have particular consequences for women, whose work more often appears to be benign. This article reports on a study of empirical data drawn from analysis of 314 workers' compensation appeal tribunal decisions on compensation claims, in Quebec, for musculoskeletal disorders alleged to be related to repetitive work. The study considers randomly selected decisions rendered between 1994 and 1996 on diagnoses of tendonitis, epicondylitis, and carpal tunnel syndrome and, in a follow-up, looks at significant legal decisions by the same tribunals, rendered over a longer period (1987-96). Results indicate that women workers are significantly less likely than their male counterparts to have their occupational disease claims accepted by the appeal tribunal. Evidence suggests that inappropriate overreliance on scientific studies for adjudication purposes contributes to a greater rate of refusal of claims by women workers.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".