Wife Beating and the Link with Poor Sexual Health and Risk Behavior Among Men in Urban Slums in India
Bibliographic record
Abstract
Recent research evidence on domestic and sexual violence have linked violence with increased risk of acquiring HTV and adverse health in women. This paper explores the link between wife abuse and different aspects of male sexual health using data from 2 surveys among 1279 married men and 553 married women in a Mumbai slum community. Three categories of sexual health problems were considered: symptoms indicative of sexually transmitted infections (STIs), performance related problems and the prevalent South Asian semen anxiety. Ten percent of both men and women reported wife beating in the last year. The severity of abuse reported by women is clearly correlated with the prevalence of all three categories of the husband’s sexual problems as perceived by the wife. Among perpetrators of abuse we show the expected correlation of reported STI symptoms, extramarital sex and domestic violence. However, the semen-related problems are also associated with increased risk behaviour. Performance related problems are shown to be strongly associated with domestic violence among perpetrators. Other important correlates of abuse are related to personal history. Both perpetrators of violence and beaten women were more likely to come from families with a history of abuse. Having in-laws who were dissatisfied with the dowry increased the likelihood of experiencing physical abuse by nearly 4 times. Husbands and wife’s sexual and reproductive health are clearly related in complex ways, and pathways not limited to sexual risk behaviour and transmission of STIs only. Wife beating is an important factor affecting women’s health, and importantly linked with the sexual fears and inadequacies in men.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".