Understanding the Health Transitions of Immigrants to Canada: Research Priorities
Bibliographic record
Abstract
Understanding changes in the health of immigrants has been an important area of research in Canadian public health. Recent years have seen important developments, with studies moving away from what might be called 'sick immigrant' versus 'healthy immigrant' debates towards analyzing transitions and how they are influenced by a diverse set of social determinants. The release of data from all three waves of the Longitudinal Survey of Immigrants to Canada has also spurred new theoretical understandings of why immigrants' initial health advantage is lost over time, with the experience of discrimination becoming an increasingly important predictor. Three research priorities are emerging as particularly important in this area. These are the need for multilevel analyses that incorporate contextual effects, the need for comparative international studies, and the need to refine the conceptualization of race/ethnicity to take advantage of developments in social theory.
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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 | 0.031 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.008 | 0.016 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".