Migrations and gradations : reappraising the health profile of immigrants to Canada
Bibliographic record
Abstract
New immigrants to Canada typically have a more favourable health profile than \nthe non-immigrant population. This phenomenon, known as the 'healthy immigrant \neffect', has been attributed to both the socioeconomic advantage (ie. educational \nattainment, occupational opportunity) of non-refugee immigrants and existing screening \nprotocols that admit only the healthiest of persons to Canada. It has been suggested that \nthis health advantage diminishes as the time of residence in Canada increases, due in part \nto the adoption of health-risk behaviours such as alcohol and cigarette use, an increase in \nexcess body weight, and declining rates of physical activity. However, the majority of \nhealth research concerning immigrants to Canada has been limited to cross-sectional \nstudies (Dunn & Dyck, 2000; Newbold & Danforth, 2003), which may mask an \nimmigrant-specific cohort effect. Furthermore, the practice of aggregating foreign-bom \npersons by geographical regions or treating all immigrants as a homogeneous group may \nalso obfuscate intra-immigrant differences in health. Accordingly, this study uses the \nCanadian National Population Health Surveys (NPHS) and data from the United Nations \nDevelopment Program (UNDP) to prospectively evaluate factors that predict health status \namong immigrants to Canada. Each immigrant in the NPHS was linked to the UNDP \nHuman Development Index of their country of birth, which uses a combined measure of \nhealth, education, and per capita income of the populace. The six-year change in health \nfunction, psychological distress, and self-rated health were considered from a population \nhealth perspective (Evans, 1994), using generalized-estimating equations (GEE) to \nexamine the compounding effect of past and recent predictors of health. Demographic
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".