Gender Inequality and Crude Suicide Rates in Türkiye: A Nationwide Retrospective Ecological Study.
Bibliographic record
Abstract
Introduction: Suicide is a serious public health problem worldwide, with most suicides occurring in low- and middle-income countries. This nationwide ecological study aimed to explore the relationship between crude suicide rates of the sexes and gender inequality. Method: The data on age and gender-stratified crude suicide rates in all 81 provinces of Türkiye were retracted from the Turkish Statistical Institute database. Gender inequality was measured using Türkiye's provincial-level Gender Equality Index (GEI). Additionally, the following variables were considered gender inequality indicators: early marriages, fertility rate, and marriage/divorce rates; data were obtained from the Turkish Statistical Institute database. Data from 2019 were used to avoid the confounding effect of the COVID-19 pandemic. Results: The associations between gender inequality and crude suicide rate differed between women and men. There was a positive correlation between crude suicide rate and GEI in men aged 45-64 (r=0.294 p<0.01), but no correlations were found in other groups of age and gender. Early marriage rates (r=0.341 p<0.01) and fertility rate (r=0.333 p<0.01) were positively associated with crude suicide rates in women aged 15-44, while divorce/marriage rate (r=-0.256 p<0.05) was negatively related to these rates. Divorce/marriage rate was associated with an increase in men's suicides in both the 45-64 (r=0.452 p<0.01) and 65 and over (r=0.290 p<0.01) age groups. Conclusion: Gender inequality indicators were related to suicide. That relationship may vary across different age and gender groups. The findings may be limited to low- and middle-income countries. Suicide prevention interventions should be designed to account for age group, gender, and cultural characteristics of the place of residence concerning gender.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".