Claim Suppression of Occupational Injuries and Illnesses Among Precariously Employed Immigrant Workers in Ontario
Bibliographic record
Abstract
Employers sometimes hinder the appropriate reporting of claims to workers' compensation, a phenomenon termed claim suppression. While the magnitude of claim suppression is difficult to quantify, various reports have identified it as a significant concern. In response, several Canadian jurisdictions, such as Ontario in 2015, introduced legislation addressing claim suppression. This article first discusses the legislative and policy context that influences claim suppression in Ontario, including concerns concerning the scope, interpretation, and enforcement of the law. It then presents qualitative findings from a community-based study with members of the Toronto Bangladeshi immigrant community that documented varied forms of employer claim suppression in precarious work, as well as facilitators of claim suppression within the workers' compensation and health care systems. Our findings and those of other research suggest that the scope of claim suppression is broader than that contemplated by the legislation. Our article proposes recommendations for the conceptualization of claim suppression and for legislation, policies, practices, and interventions that are grounded in workers' lived experiences.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".