Mitigating effect of immigration on the relation between income inequality and mortality: a prospective study of 2 million Canadians
Bibliographic record
Abstract
BACKGROUND: The relation between income inequality and mortality in Canada is unclear, and modifying effects of characteristics such as immigration have not been examined. METHODS: Using a cohort of 2 million Canadians followed for mortality from 1991-2001, we calculated HRs and 95% CIs for income inequality of 140 urban areas (Gini coefficient, Atkinson index, coefficient of variation; expressed as continuous variables) and working age (25-64 y) or post-working age (≥65 y) mortality in men and women according to immigration status, accounting for individual and neighbourhood income, and sociodemographic characteristics. Major causes of mortality were examined. RESULTS: Relative to low income inequality, high inequality was associated with greater working age mortality in male (HR(Gini) 1.08, 95% CI 1.04 to 1.13) and female (HR(Gini) 1.12, 95% CI 1.06 to 1.18) non-immigrants for all income inequality indictors. Results were similar for female post-working age mortality. There was no relation between income inequality and mortality in immigrants. Among Canadian-born individuals, associations were greater for alcohol-related mortality (both sexes) and smoking-related causes/transport injuries (women). CONCLUSION: Income inequality is associated with mortality in Canadian-born individuals but not immigrants.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".