The Role of Individual, Community and Societal Gender Inequality in Forming Women's Attitudes toward Intimate-Partner Violence against Women: A Multilevel Analysis
Bibliographic record
Abstract
BACKGROUND: Establishing risk factors for intimate partner violence against women (IPVAW) is crucial for addressing women's health and development. Acceptance of IPVAW has been suggested as one of the strongest predictors of IPVAWs. The aim of this study was to examine the independent contributions of individual, community, and societal measures of gender inequality in forming women's attitudes toward IPVAW. METHODS: We applied multivariable multilevel logistic regression analysis to Demographic and Health Survey data for 120,467 women nested within 7463 communities from 17 countries in sub-Saharan Africa. RESULTS: We found that women whose husband had higher education (odds ratio [OR] =1.06; 95% confidence interval [CI] 1.02 to 1.10) and women whose husband had more than one wife (OR=1.14; 95% CI 1.09 to 1.19) were more likely to accept IPVAW than other women. Unemployed women with an unemployed partner were more likely to justify IPVAW than employed women with working partners (OR=1.32; 95% CI 1.08 to 1.61). Both community and societal measures of gender inequality were associated with women's attitudes toward IPVAW, even after controlling for gender inequality at the individual level. There was evidence of clustering of women's attitudes within communities and within countries. CONCLUSION: We provide evidence that community and societal forms of gender inequality influence women's attitudes toward IPVAW beyond individual factors. Choices women make are important, but community and society also impose restraints on women's attitudes toward IPVAW. Thus, policies and programs aimed at reducing or eliminating IPVAW must address people, the communities and societies in which they live in order to be successful.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".