Official language proficiency and self-reported health among immigrants to Canada.
Bibliographic record
Abstract
BACKGROUND: New immigrants to Canada initially report better health than does the Canadian-born population. With time, this "healthy immigrant effect" appears to diminish. Limited ability to speak English or French has been identified as a possible factor in poor health. This analysis explored the relationship between self-reported official language proficiency and transitions to poor self-reported health. DATA AND METHODS: Statistics Canada's Longitudinal Survey of Immigrants to Canada tracked a sample of the 2001 immigrant cohort for four years (6, 24 and 48 months after arrival). Data from each of the three survey waves were available for 7,716 respondents. Bivariate and multivariate analysis were used to examine associations between official language proficiency and self-reported health, by sex, controlling for selected pre-migration and post-migration factors. The prevalence of poor health among immigrants was compared with rates among the Canadian-born population, based on data from the Canadian Community Health Survey. RESULTS: Among a representative sample of recent immigrants, the prevalence of poor self-reported health had risen substantially, especially among women, after four years in Canada. Prolonged limited official language proficiency was strongly associated with a transition to poor health among male and female immigrants who had earlier reported good health. Other factors significantly associated with an increase in the prevalence of poor self-reported health differed by sex. Refugee status, self-reported discrimination, and living in Vancouver were significant for men. Age, health care access problems, and limited friendliness of neighbours were significant for women.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".