Gender inequality in source country modifies sex differences in stroke incidence in Canadian immigrants
Bibliographic record
Abstract
Abstract Research suggests that gender inequality, measured using the gender inequality index (GII), influences stroke mortality in women compared to men. We examine how source country GII modifies the rate of ischemic stroke in women compared to men after immigration to Canada, a country with low gender inequality. We used linked health data and immigration records of 452,089, stroke-free immigrants aged 40–69 year who migrated from 123 countries. Over 15 years of follow-up, 5991 (1.3%) had an incident ischemic stroke. We demonstrate (a) a lower adjusted rate of stroke in women compared to men (hazard ratio 0.64; 95% CI 0.61–0.67); (b) that sex differences in stroke incidence were modified by source country GII, as the hazard of stroke in women vs. men attenuated by a factor of 1.06 for every 0.1 increase in the GII of the source country (Psex*GII = 0.002); and (c) migration to a country with low GII attenuates the adverse effect of source country GII on sex differences in stroke incidence. Evaluating pathways through which source country gender inequality differentially influences stroke risk in immigrant women compared to men could help develop strategies to mitigate the effects of early-life gender inequality on stroke risk.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".