Health Disparities for Immigrants: Theory and Evidence from Canada
Bibliographic record
Abstract
Abstract Few empirical studies have been conducted to analyse the disparities in health variables affecting immigrants in a given country. To our knowledge, no theoretical analysis has been conducted to explain health disparities for immigrants between regions in the same country that differs in term of languages spoken and income. In this paper, we use the Canadian Community Health Survey (CCHS) to compare multiple health measures among immigrants in Quebec, immigrants in the rest of Canada and Canadian-born individuals. We propose a simple structural model and conduct an empirical analysis in order to assess possible channels that can explain the health disparities for immigrants between two regions of the same country. Our results show that well-being and health indicators worsen significantly for immigrants in Quebec, compared to their counterparts in the rest of Canada and Canadian-born individuals. Additional econometric analysis also shows that life satisfaction is statistically and significantly associated with health outcomes. The proposed structural model predicts that, when the decision to migrate to a particular area is based on income alone, and if the fixed costs associated with the language barrier are large, immigrants may face health issues.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".