Male sex workers: Are we ignoring a risk group in Mumbai, India?
Bibliographic record
Abstract
BACKGROUND: Male sex workers (MSWs) have recently been recognized as an important risk group for sexually transmitted infections (STIs) including human immunodeficiency virus (HIV) infection. Although there are global studies on MSWs, few such studies describe the behavioral patterns and STIs among this population in India. METHODS: MSWs were evaluated at the Humsafar trust, a community based organization situated in suburban Mumbai, India. We report on the demographics, sexual behaviors, and STIs including HIV of these sex workers. RESULTS: Of the 75 MSWs, 24 were men and 51 were transgenders. The mean age of the group was 23.3 (+ 4.9) years. About 15% were married or lived with a permanent partner. Of these individuals, 85% reported sex work as a main source of income and 15% as an additional source. All the individuals reported anal sex (87% anal receptive sex and 13% anal insertive sex). About 13% of MSWs had never used a condom. The HIV prevalence was 33% (17% in men vs 41% in transgenders, P = 0.04). The STI prevalence was 60% (58% in men vs 61% in transgenders, P = 0.8). Syphilis was the most common STI (28%) in these MSWs. HIV was associated with being a transgender (41 vs 17%, P = 0.04), age > 26 years (57 vs 28%, P = 0.04), more than one year of sex work (38 vs 8%, P = 0.05), and income P = 0.02). CONCLUSIONS: The MSWs have high-risk behaviors, low consistent condom use, and high STI/HIV infections. These groups should be the focus of intensive public health interventions aimed at reduction of risky sexual practices, and STI/HIV prevention and care.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".