Examining the Gender, Ethnicity, and Age Dimensions of the Healthy Immigrant Effect: Implications for Health Care Policy
Bibliographic record
Abstract
Using data from the 2005 Canadian Community Health Survey, the current study expands on previous research on the healthy immigrant effect (HIE) in adult populations by considering the effects of both immigrant and visible minority status on health for males and females in mid- (45- 64) and later life (65+). The findings indicate that the HIE applies to recent immigrant men in midlife; that is, new male immigrants – those who immigrated less than 10 years ago – have better health compared to their Canadian-born counterparts, and that the effect is particularly strong for visible minorities. The picture is similar for older women who have recently immigrated, however this advantage largely disappears when a number of socio-demographic, socio-economic, and lifestyle factors are controlled. For older men and middle-aged women of color, however, the reality is strikingly different: both groups report health disadvantages compared to their Canadian-born counterparts, with both recent and longer-term midlife women having poorer health. Findings are discussed in terms of their implications for health care policy for immigrant adults.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".