Countries with women inequalities have higher stroke mortality
Bibliographic record
Abstract
Background Stroke outcomes can differ by women's legal or socioeconomic status. Aim We investigated whether differences in women's rights or gender inequalities were associated with stroke mortality at the country-level. Methods We used age-standardized stroke mortality data from 2008 obtained from the World Health Organization. We compared female-to-male stroke mortality ratio and stroke mortality rates in women and men between countries according to 50 indices of women's rights from Women, Business and the Law 2016 and Gender Inequality Index from the Human Development Report by the United Nations Development Programme. We also compared stroke mortality rate and income at the country-level. Results In our study, 176 countries with data available on stroke mortality rate in 2008 and indices of women's rights were included. There were 46 (26.1%) countries where stroke mortality in women was higher than stroke mortality in men. Among them, 29 (63%) countries were located in Sub-Saharan African region. After adjusting by country income level, higher female-to-male stroke mortality ratio was associated with 14 indices of women's rights, including differences in getting a job or opening a bank account, existence of domestic violence legislation, and inequalities in ownership right to property. Moreover, there was a higher female-to-male stroke mortality ratio among countries with higher Gender Inequality Index (r = 0.397, p < 0.001). Gender Inequality Index was more likely to be associated with stroke mortality rate in women than that in men (p < 0.001). Conclusions Our study suggested that the gender inequality status is associated with women's stroke outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".