Variability in the sexual structure in a rural Indian setting: implications for HIV prevention strategies
Bibliographic record
Abstract
OBJECTIVES: To describe the sexual structure, including numbers and distribution of female sex workers (FSWs) and male sexual behaviours in the Bagalkot district of the state of Karnataka in south India. METHODS: Village health workers and peer educators enumerated FSWs in each village by interviewing key informants and FSWs. Urban FSW populations were estimated using systematic interviews with key informants to identify sex work sites and then validating FSW populations at each sex work site. Male sexual behaviours were measured through confidential polling booth surveys in randomly selected villages. HIV prevalence was estimated through a community-based survey using randomised cluster sampling. Lorenz curves and Gini coefficients were used to describe the degree of clustering of FSW populations. RESULTS: Of an estimated 7280 FSWs in Bagalkot district (17.1/1000 adult males), 87% live and work in rural areas. The relative size of the FSW population varies from 9.6 to 30.5/1000 adult males in the six subdistrict administrative areas (talukas). The FSW population was highest in the three talukas with more irrigated land and fewer and larger villages. FSW populations are highly clustered; 93 (15%) of the villages accounted for 54% of all rural FSWs. There is a high degree of FSW clustering in all talukas, and talukas with fewer and larger villages have larger clusters and more FSWs overall. General population HIV prevalence is highest in the taluka with the highest relative FSW population. CONCLUSIONS: Prevention programmes in India should be scaled up to reach FSWs in rural areas. These programmes should be focused on those districts and subdistrict areas with large concentrations of FSWs. More research is required to determine the distribution of FSWs in rural areas in other regions of India.
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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 | 0.001 |
| 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) | 0.002 | 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".