The Intersection between Sex Work and Reproductive Health in Northern Karnataka, India: Identifying Gaps and Opportunities in the Context of HIV Prevention
Bibliographic record
Abstract
Objective. To examine the reproductive health practices of female sex workers (FSWs) in the context of an HIV prevention program in Karnataka, India. Methods. Data obtained from a survey of 1,011 FSWs registered with an HIV prevention program. We examined reproductive health indicators, and performed multivariate logistic regression among primiparous FSWs to assess sex work during pregnancy and antenatal HIV testing. Results. Among primiparous FSWs (N = 251), 92.0% continued sex work during pregnancy, and 55.4% received antenatal HIV testing. A longer duration in sex work (AOR 2.7, 95% CI: 1.0-7.5), rural residence (AOR 3.3, 95% CI: 1.2-8.9), and antenatal HIV testing (AOR 6.3, 95% CI: 2.0-20.1) were associated with continued sex work during pregnancy. Older FSWs (age >25 years, AOR 0.12, 95% CI: 0.05-0.33), who delivered at home (AOR 0.14, 95% CI: 0.09-0.34), were least likely to receive antenatal HIV testing. Antenatal HIV testing was associated with awareness of methods to prevent vertical HIV transmission (AOR 3.9, 95% CI: 1.9-14.1). Conclusions. Antenatal HIV testing remains low in the context of ongoing sex work during pregnancy. Existing HIV prevention programs are well positioned to immediately integrate reproductive health care with HIV interventions targeted to 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".