Prevalence and Predictors of Violence Against Women in Pakistan
Bibliographic record
Abstract
Violence against Pakistani women occurs at an alarming prevalence that reflects a significant risk to the health of women and families. Understanding violence against women in Pakistan comes with methodological and cultural complexities. Many of the studies examining prevalence and predictors of violence against women tend to utilize convenience samples contributing to the possibility of an inflated prevalence. Due to the patriarchal family structure and cultural context, domestic violence is normalized resulting in extremely low reporting rates. The present study utilizes a sample of ever-married Pakistani women collected across five provinces to shed light on the prevalence and predictors of women that experience domestic violence (emotional or physical abuse). Data were obtained from the 2012-2013 Pakistan Demographic and Health Survey including a large sample of Pakistani women from five provinces (Sindh, Punjab, Balochistan, Khyber Pakhtunkhwa, and Gilgit Baltistan). Binary Linear regressions were conducted to examine how intrinsic variables (age, education, region, urban/rural, type of marital relationship, and wealth) predicted experiencing emotional or physical abuse from one's husband within the past year. Approximately, 20% of women endorsed experiencing physical violence and 28% endorsed experiencing emotional violence. Results found that educational level, wealth, and type of marital relationship were associated with a higher likelihood of experiencing some form of physical or emotional violence. Implications from this study support policy interventions aimed at education within the family, linking women with resources, and continued investment in the education of young women. Interventions would be best targeted in low wealth regions with a special emphasis on rural areas.
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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 | 0.002 |
| 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 | 0.001 | 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".