Intimate Partner Violence, Relationship Power Inequity and the Role of Sexual and Social Risk Factors in the Production of Violence among Young Women Who Have Multiple Sexual Partners in a Peri-Urban Setting in South Africa
Bibliographic record
Abstract
INTRODUCTION: This paper aims to assess the extent and correlates of intimate partner violence (IPV), explore relationship power inequity and the role of sexual and social risk factors in the production of violence among young women aged 16-24 reporting more than one partner in the past three months in a peri-urban setting in the Western Cape, South Africa. Recent estimates suggest that every six hours a woman is killed by an intimate partner in South Africa, making IPV a leading public health problem in the country. While there is mounting evidence that levels of IPV are high in peri-urban settings in South Africa, not much is known about how it manifests among women who engage in concomitantly high HIV risk behaviours such as multiple sexual partnering, transactional sex and age mixing. We know even less about how such women negotiate power and control if exposed to violence in such sexual networks. METHODS: Two hundred and fifty nine women with multiple sexual partners, residing in a predominantly Black peri-urban community in the Western Cape, South Africa, were recruited into a bio-behavioural survey using Respondent Driven Sampling (RDS). After the survey, focus group discussions and individual interviews were conducted among young women and men to understand the underlying factors informing their risk behaviours and experiences of violence. FINDINGS: 86% of the young women experienced IPV in the past 12 months. Sexual IPV was significantly correlated with sex with a man who was 5 years or older than the index female partner (OR 1.7, 95% CI 1.0-3.2) and transactional sex with most recent casual partner (OR 2.1, 95% CI 1.1-3.8). Predictably, women experienced high levels of relationship power inequity. However, they also identified areas in their controlling relationships where they shared decision making power. DISCUSSION: Levels of IPV among young women with multiple sexual partners were much higher than what is reported among women in the general population and shown to be associated with sexual risk taking. Interventions targeting IPV need to address sexual risk taking as it heightens vulnerability to violence.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".