DOI: 10.13189/ujph.2014.020104 Prevalence and Correlates of Experience of Physical and Sexual Intimate Partner Violence among Men and Women in Eastern DRC
Bibliographic record
Abstract
Abstract This manuscript uses large-scale survey data to examine the prevalence and correlates of intimate partner violence (IPV) in the eastern part of the Democratic Republic of the Congo (DRC), namely in North Kivu and South Kivu provinces. We examine two form of IPV: physical and sexual. The data show that two of every five women and more than one quarter of men had reportedly ever experienced physical IPV while one quarter of women and 15.7 percent of men reported ever experiencing sexual IPV. The correlates of IPV differ for men and women and depend on the type of IPV. For men, the strongest correlates of physical IPV include current employment, education and recent experience of sexual IPV. The strongest correlates for experiencing sexual violence among men were young age, problematic use of alcohol, gender-equitable attitudes, province of residence, and recent experience of physical IPV. For women, young age, low education, gender-equitable attitudes, partner problematic use of alcohol, partner controlling behaviors, recent experience of sexual IPV, and recent experience of public humiliation were the correlates of physical IPV. The strongest correlates of sexual IPV for women include province of residence, partner problematic use of alcohol, partner controlling behaviors, and recent experience of physical IPV. The programmatic implications of the findings are discussed.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.035 | 0.005 |
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".