Variations in Emotional, Sexual, and Physical Intimate Partner Violence Among Women in Uganda: A Multilevel Analysis
Bibliographic record
Abstract
Evidence shows that a significant proportion of ever-partnered women suffer some form of intimate partner violence (IPV) perpetuated by male partners. The prevalence of IPV in sub-Saharan African countries is considerably higher than global estimates. Although existing studies show the effect of women's and intimate male partner's characteristics on IPV, knowledge on how these factors increase or reduce women's risk to specific types of IPV is limited. Using the 2016 Ugandan Demographic and Health Survey (UDHS), we examine regional variations in women's and intimate male partner's characteristics and their effect on emotional, sexual, and physical violence perpetuated by men and experienced by women in Uganda. The result shows that women's educational status is a significant predictor of all forms of IPV, whereas other characteristics, such as employment and housing ownership, have differential effects on specific types of IPV. Less educated women were more likely to experience emotional, sexual, and physical violence. Alcohol abuse was a significant determinant of men perpetuating all types of IPV; other male characteristics had differential effects on specific types of IPV. Male partners who abuse alcohol "often" and "sometimes" were more likely to commit acts of emotional, sexual, and physical violence against their female intimate partners. The findings also show that ~5%, ~8%, and ~2% of the variance in emotional, sexual, and physical violence (respectively; in the final models) are attributable to regional differences. The findings suggest the need for interventions aimed at increasing women's access to higher education, working with men and boys to reduce the occurrence of alcohol abuse and address harmful constructions of masculinity, and promoting gender equality among men as well as women.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".