O2-S1.05 Vulnerability re-assessed: the changing face of sex work in Guntur District, Andhra Pradesh
Bibliographic record
Abstract
Guntur district in Andhra Pradesh, south India, has over 10 000 female sex workers (FSWs), of whom one fifth are estimated to be HIV infected. Following implementation of the Bill & Melinda Gates Foundation funded India AIDS prevention initiative, Avahan, we conducted qualitative studies with FSWs in Guntur district to examine in-depth, perceptions, behaviour and issues pertaining to their vulnerability to HIV infection. Three focus group discussions and 40 in-depth interviews were conducted with 60 FSWs, selected by random sampling. The study found evidence that client solicitation practices are changing in Guntur district, with sex workers now soliciting clients in their homes, often using mobile phones, or at their workplace, rather than in brothels or in public places. In addition, clients are most frequently requesting anal instead of vaginal sex, possibly resulting from recent exposure to pornography. FSWs were frequently unaware that unprotected anal intercourse put them at risk of HIV or other STIs, and the majority reported unprotected anal intercourse with their clients. Older FSWs and HIV-infected FSWs reported living in extreme poverty and were particularly vulnerable, often agreeing to unprotected vaginal and anal intercourse to earn enough money to survive. This, coupled with sharing of clients by home-based FSWs, suggests continued and changing vulnerability in this setting where many FSWs are already HIV infected and where understanding of risk appears deficient. Monitoring the changing sex work environment and adapting programmes accordingly will be crucial if HIV prevention programmes are to continue to reach these target populations effectively.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.399 | 0.042 |
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".