Are Healthcare Systems Failing Immigrants? Transnational Migration and Social Exclusion in the Workers’ Compensation Process in Québec
Bibliographic record
Abstract
Background: The changing world of work, which increasingly depends on the use of temporary and atypical forms of employment, has had a disproportionate effect on the health and well-being of immigrants. When they have to find a health professional for the first time or report an accident at work, the journey through the maze of medical-administrative bureaucracy can be long and arduous. The aim of this article is to describe the analytical contribution of systems thinking by presenting three situations that illustrate the importance of connecting the individual, organizational, and societal levels, especially focusing on the interplay between these levels. Methods: The data analyzed in this article are taken from an initial qualitative exploratory study of a purposive sample of 40 individuals: (1) clinicians ( N = 15), (2) claims consultants and rehabilitation counselors ( N = 14), (3) employers ( N = 2), and (4) immigrant workers ( N = 9). Situations were analyzed using insights from grounded theory by identifying the interconnectedness of individual, organizational, and system-based factors that can have an impact on the return-to-work process. Results: By looking specifically at the context of occupational rehabilitation in contemporary Québec and the challenges faced by immigrant workers faced with multiple factors of precariousness, this article sets out to show how local healthcare systems are poorly equipped to respond to the new reality of transnational migration. Conclusion: Drawing from recent research in the area of systemic theory, this article posits that systems, which are poorly adapted to the new reality of transnational migration, have the unintended consequence of creating new forms of discrimination and social exclusion.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.010 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".