Examining the gender, ethnicity, and age dimensions of the healthy immigrant effect: Factors in the development of equitable health policy
Bibliographic record
Abstract
This study expands on previous research on the healthy immigrant effect (HIE) in Canada by considering the effects of both immigrant and visible minority status on self-rated health for males and females in mid-(45-64) and later life (65+). The findings reveal a strong HIE among new immigrant middle-aged men, particularly non-Whites. For older men of color the reality is strikingly different: they are disadvantaged in health compared to their Canadian-born counterparts, even when a number of demographic, economic, and lifestyle factors are controlled. Health outcomes for immigrant women are in contrast to that of immigrant men. Among middle-aged women, immigrants, regardless of their ethnicity or number of years since immigration, are much more likely to report poor health compared to the Canadian-born. And, for older women, recent non-white immigrants are more likely to report better health compared to Canadian-born women, although this finding is explained by differences in demographic, economic, and lifestyle factors. Overall, the findings demonstrate the importance of considering the intersections of age, gender, and ethnicity for policymakers in assessing the health of immigrants.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".