Intimate Partner Violence (IPV) Against HIV-Positive Women in Sub-Saharan Africa: A Mixed-Method Systematic Review and Meta-Analysis
Bibliographic record
Abstract
OBJECTIVES: To systematically analyze and summarize the literature on intimate partner violence (IPV) against HIV-positive women in sub-Saharan Africa (SSA) and to identify their risk factors for IPV. METHOD: A comprehensive review of the literature using the Preferred Reporting Item for Systematic Review and Meta-Analysis (PRISMA) and Meta-Analyses of Observational Studies in Epidemiology (MOOSE) yielded 1,879 articles (PubMed = 1,251, Embase = 491, Web of Science = 132, and identified additional records = 5). Twenty were selected for quantitative and qualitative assessment and synthesis. We employed a random effects model with generic inverse variance method and estimated the odds ratios. FINDINGS: Results indicated a high prevalence of physical, sexual, and emotional violence against women living with HIV/AIDS in SSA. Educational background, alcohol use, marital status, previous experiences with IPV, and employment status were identified as significant risk factors. We also assessed the methodological quality of the articles by examining publication bias and some heterogeneity statistics. CONCLUSION: There is limited research on IPV against HIV-positive women in SSA. However, the few existing studies agree on the importance of targeting HIV-positive women with specific interventions given their vulnerability to IPV and to address factors exacerbating these risks and vulnerabilities.
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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 | 0.020 | 0.053 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.026 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".