Mistrust among injured immigrant workers: A conceptual framework
Bibliographic record
Abstract
BackgroundIn Quebec, injured workers have access to medical assistance, wage replacement indemnities, and rehabilitation measures, with most injuries resolved easily. However, a small percentage of cases require a lengthy rehabilitation process, which is particularly complex when involving immigrants. The therapeutic alliance is at the heart of the process, involving mutual trust and confidence and a degree of consensus about the therapeutic interventions to be used and, ultimately, the return to work.ObjectiveThis article delves into the concept of mistrust as more than the absence of trust, and sheds light on its role in the rehabilitation process of immigrant workers.MethodDrawing from a literature review on mistrust of immigrants in the healthcare setting and comprehensive studies on the rehabilitation process, a conceptual framework for understanding mistrust in this context is proposed.ResultsVulnerable situations, such as precarious employment and/or temporary immigration status, create fertile ground for mistrust. Negative experiences with healthcare professionals or systems further exacerbate mistrust, as do social stigmas within the rehabilitation system. Cross-cultural miscommunications such as differences in communication styles or beliefs about illnesses further complicate matters, emphasizing the need for culturally sensitive approaches.ConclusionAddressing mistrust requires understanding its origins and consequences, as well as providing guidelines for practitioners to enable them to recognize and manage it effectively. Further research is crucial to deepen our understanding of mistrust among immigrant workers and develop strategies for promoting trust and cooperation throughout the rehabilitation process.
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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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.018 | 0.041 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.003 |
| 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".