Gender Inequity Norms Are Associated with Increased Male-Perpetrated Rape and Sexual Risks for HIV Infection in Botswana and Swaziland
Bibliographic record
Abstract
BACKGROUND: There is limited empirical research on the underlying gender inequity norms shaping gender-based violence, power, and HIV risks in sub-Saharan Africa, or how risk pathways may differ for men and women. This study is among the first to directly evaluate the adherence to gender inequity norms and epidemiological relationships with violence and sexual risks for HIV infection. METHODS: Data were derived from population-based cross-sectional samples recruited through two-stage probability sampling from the 5 highest HIV prevalence districts in Botswana and all districts in Swaziland (2004-5). Based on evidence of established risk factors for HIV infection, we aimed 1) to estimate the mean adherence to gender inequity norms for both men and women; and 2) to model the independent effects of higher adherence to gender inequity norms on a) male sexual dominance (male-controlled sexual decision making and rape (forced sex)); b) sexual risk practices (multiple/concurrent sex partners, transactional sex, unprotected sex with non-primary partner, intergenerational sex). FINDINGS: A total of 2049 individuals were included, n = 1255 from Botswana and n = 796 from Swaziland. In separate multivariate logistic regression analyses, higher gender inequity norms scores remained independently associated with increased male-controlled sexual decision making power (AORmen = 1.90, 95%CI:1.09-2.35; AORwomen = 2.05, 95%CI:1.32-2.49), perpetration of rape (AORmen = 2.19 95%CI:1.22-3.51), unprotected sex with a non-primary partner (AORmen = 1.90, 95%CI:1.14-2.31), intergenerational sex (AORwomen = 1.36, 95%CI:1.08-1.79), and multiple/concurrent sex partners (AORmen = 1.42, 95%CI:1.10-1.93). INTERPRETATION: These findings support the critical evidence-based need for gender-transformative HIV prevention efforts including legislation of women's rights in two of the most HIV affected countries in the world.
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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 | 0.001 | 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 | 0.001 |
| 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".