La population immigrante est-elle plus à risque d’accidents du travail? Une analyse basée sur des données administratives au Québec
Bibliographic record
Abstract
L'intégration des personnes immigrantes dans le marché du travail québécois soulève des enjeux importants en matière de sécurité au travail. L’objectif de cet article consiste à (1) comparer le risque, pour l’ensemble des accidents du travail acceptés et les accidents graves acceptés, entre la population immigrante et non immigrante au Québec et (2) examiner les différences dans les risques d’accidents selon les catégories professionnelles et de sexe. Un appariement inédit de 3 bases administratives a permis d’identifier 9 783 personnes immigrantes parmi les 82 704 dossiers d’accidents acceptés en 2016. Des régressions binomiales négatives révèlent que les personnes immigrantes admises depuis moins de 5 ans ont un plus grand risque d’accidents comparativement aux personnes non immigrantes. Des analyses portant sur les accidents graves et des modèles stratifiés par catégorie professionnelle et de sexe permettent de nuancer les résultats. Cet article souligne l’importance de considérer la problématique des accidents du travail parmi la population immigrante dans une perspective intersectionnelle et offre des pistes quant aux groupes professionnels de personnes immigrantes à prioriser dans les interventions.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".