The Association Between Child Marriage and Domestic Violence in Afghanistan
Bibliographic record
Abstract
Child marriage and intimate partner violence have been globally recognized as human rights violations. Both indicators can derail an individual’s future and have various public health implications. Previous studies have shown an association between child marriage and domestic violence in low- and middle-income countries; however, data in Afghanistan are not known. This study aimed to assess the association between child marriage and domestic violence in Afghanistan. We used nationally representative data collected by the Demographic and Health Surveys to conduct logistic regression analyses. Child marriage was separated into three categories: very early marriage (<15 years), early marriage (15–17 years), and adult marriage (≥18 years). Domestic violence was the response variable and was assessed as any violence, physical violence, emotional violence, and sexual violence. Of the sample ( N = 21,324), 15% of the respondents were married before the age of 15; 35% were married between the ages of 15 and 17; and 50% were married as adults. After adjusting for current age, place of residence, and socioeconomic status, the odds of sexual violence were 22% higher among women who married before age 15 compared with those married as adults (OR = 1.22, 95% CI = [1.05, 1.40], p = .005). However, the odds of reporting any violence, physical violence, and emotional violence among those who married as children did not differ compared with those who married as adults. This may be due to a shift in traditional norms or underreporting in Afghanistan. This study adds to the body of research on child marriage and intimate partner violence, and specifically provides novel information on this association in Afghanistan.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".