The false promise of return to work for migrant workers injured on the job in Canada: When public policies intersect to create exclusion
Bibliographic record
Abstract
BackgroundMigrant workers in Canada work in the most dangerous industries, with documented difficulties with occupational health and safety (OHS) and workers' compensation (WC). Little is known, however, about migrant workers' experiences with "return-to-work", the ultimate objective of WC for those who recover from their illness or injury.ObjectiveThe objective of this study was to understand migrant workers' "return-to-work" experiences after facing occupational injury/illness for in Québec, Canada.MethodsPart of a broader multi-disciplinary project exploring how Canadian policies can ensure "return-to-work" for injured workers, this article draws on policy analysis and semi-structured interviews with 21 migrant workers in Quebec who experienced workplace illness or injury. Interviews (in Spanish, English, and French) lasted 1-2 h and were transcribed verbatim and thematically coded.ResultsThe first group of workers with occupational injuries never left their job out of fear of consequences. A second group of workers stopped working, with some receiving WC, while others did not. Most workers in this group never returned to work. Among the reasons provided by migrant workers, the data suggest following as the most common: abandoning the job out of fear of re-injury; repatriation and not being called back; and lasting repercussions of the injury leaving them unable to perform the same work. Others returned but experienced a range of problems: working without being fully recovered; constructive dismissal; or quitting because of ongoing risk or exacerbation of health problems. None were able to access retraining programs.ConclusionsThe structural components of Canada's migrant worker programs create conditions of unfree labor that impede the possibility of true "return-to-work". OHS and WC regimes must implement specific protections for migrant workers to increase their access to protections.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".