Understanding the role of peer group membership in reducing HIV-related risk and vulnerability among female sex workers in Karnataka, India
Bibliographic record
Abstract
In Karnataka state, South India, we analyzed the role of membership in peer groups in reducing HIV-related risk and vulnerability among female sex workers (FSWs). Data from three surveys conducted in Karnataka, a behavioral tracking survey and two rounds of integrated biological and behavioral assessments (IBBAs), were analyzed. Using propensity score matching, we examined the impact of group membership on selected outcomes, including condom use, experience of violence, access to entitlements, and the prevalence of sexually transmitted infections, including HIV infection. Focus group discussions were conducted with the FSWs to better understand their perceptions regarding membership in peer groups. Peer group members participating in the IBBAs had a lower prevalence of gonorrhea and/or chlamydia (5.2 vs 9.6%, p<0.001), and of syphilis (8.2 vs 10.3%, p<0.05), compared to non-members. The average treatment effect for selected outcome measures, from the propensity score matching, showed that FSWs who were members of any peer group reported significantly less experience of violence in the past six months, were less likely to have bribed police to avoid trouble in the past six months, and were more likely to have obtained at least one formal identification document in the past five years, compared to non-members. In focus group discussions, group members indicated that they had more confidence in dealing with situations of forced sex and violence. Including community mobilization and peer group formation in the context of HIV prevention programing can reduce HIV-related risk and vulnerability among FSWs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".