Intimate Partner Violence and HIV Sexual Risk Behaviour Among Women Who Inject Drugs in Indonesia: A Respondent-Driven Sampling Study
Bibliographic record
Abstract
Women who inject drugs are disproportionately affected by HIV and intimate partner violence (IPV); however, the link between IPV and HIV remains under-researched among substance-using women in low- and middle-income countries. This study examined associations and additive effects of different forms of IPV victimization (psychological, physical and/or injurious, and sexual) on HIV sexual risk behavior among women who inject drugs in Indonesia. Respondent-driven sampling (RDS) was used to recruit 731 women from Greater Jakarta and Bandung, West Java. RDS-II weighted prevalence of any past-year IPV was 68.9% (95% CI 65.0, 72.6) in Jakarta and 55.9% (95% CI 48.0, 63.5) in Bandung. In separate logistic regressions controlling for socio-demographic covariates, all three forms of IPV showed statistically significant associations with sexual risk behavior. After adjusting for all IPV types, psychological (OR 1.87; 95% CI 1.17, 2.99; p = 0.009) and sexual (OR 1.98; 95% CI 1.22, 3.21; p = 0.006) IPV independently predicted women's sexual risk behavior. Marginal effects models suggested that co-occurrence of multiple forms of IPV had greater adverse consequences: sexual risk behavior was reported by 64.1% of women who did not experience any IPV, but increased to 89.9% among women exposed to all three types. Comprehensive harm reduction services that integrate IPV monitoring and prevention are urgently needed to reduce both HIV and IPV.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".