The impact of considering birthplace in analyses of immigrant health.
Bibliographic record
Abstract
BACKGROUND: Despite the heterogeneity of Canada's immigrant population, small sample sizes often prevent health researchers from studying specific subgroups. This report demonstrates how combining cycles of the Canadian Community Health Survey (CCHS) makes it possible to move beyond the Canadian-born/immigrant dichotomy to more refined analyses of immigrant health. DATA AND METHODS: Based on combined data from the 2003, 2005, and 2007/2008 CCHS, this analysis compares the age-standardized prevalence of fair/poor self-perceived health, diabetes and arthritis among immigrants and the Canadian-born population at three progressively more precise breakdowns of immigrants by birthplace. RESULTS: Overall, immigrants were more likely than the Canadian-born to report poor health and diabetes, but less likely to report arthritis. This association changed when the immigrant group was disaggregated. This report demonstrates the importance of analyzing immigrants' health outcomes by birthplace and duration of residence in Canada. INTERPRETATION: Studies based on the immigrant/non-immigrant dichotomy combine immigrants with different risk factors, settlement experiences and health behaviours, and can yield findings that appear contradictory. Analysis of more specific immigrant subgroups improves understanding of immigrants' health relative to that of the Canadian-born population.
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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 | 0.066 | 0.142 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".