Child marriage and women's attitude towards wife beating in a nationally representative sample of currently married adolescent and young women in Pakistan
Bibliographic record
Abstract
BACKGROUND: Child marriage (before 18 years) is widely prevalent in Pakistan, and disproportionately affects young girls in rural, low income and poorly-educated households. Our study aims to determine the association of child marriage and attitude towards wife beating among currently married Pakistani women aged 15-24 after controlling for social equity indicators (education, wealth index, rural residence). METHODS: We limited the data from Pakistan Demographic and Health Survey, 2012-2013 to currently married women aged 15-24 years (n=2648). Five specified dichotomous variables indicating women's attitude towards wife beating (goes out without telling husband, neglects the children, argues with husband, refuses to have sex with husband, burns the food) were considered as outcome variables. The likelihood (OR and 95% CI) of each outcome variable for the child marriage group was estimated using logistic regression models. RESULTS: The prevalence of child marriage was significantly higher among women having no education and Balochi ethnicity, living in Khyber Pakhtunkhwa region and rural area, and belonging to the poorest quintile of wealth index. Women married as children compared with women married as adults were more likely to justify wife beating for all five specified reasons. However, these associations were lost when social equity indicators and national region of residence were adjusted in the regression models. CONCLUSIONS: Highly prevalent child marriage practice among women can be minimized by promoting education and providing economic opportunities in Pakistan.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".