The Healthy Immigrant Effect in Canada: A Systematic Review
Bibliographic record
Abstract
Canada’s immigration admissions policy calls for individuals with high human capital (Knowles, 2007). Given the strong links between human capital and health (Jasso et al., 2004) and previous research which suggested the presence of a seemingly universal foreign-born health advantage among Canada’s migrant population, we expected to see the healthy immigrant effect across the life-course and for multiple health outcomes. What we found instead was a pattern much more complex than previously envisioned. Our review uncovered a clear survival advantage for immigrants, owing in part to positive self and state selection processes (at least for non-refugee migrants). However, there is greater variation in the healthy immigrant effect for morbidity. Moreover, viewed through the lens of different life-course stages, we uncovered a strong foreign-born health advantage in adulthood but less so during the perinatal period, childhood/adolescence, and late life. Immigrant selection may be less relevant for the very young and very old, and of course we should thus not expect the presence of a healthy immigrant effect for these groups if that is the case. But even during adulthood when the healthy immigrant effect appears to be most effective, some discrepancies still remain between different immigrant subgroups depending on the type of health measure used (e.g., greater variation for self-rated health but less variation for mental health, disability/functional limitations, risk behaviors, and chronic conditions).
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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 | 0.009 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.013 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".