Mapping and size estimation of men who have sex with men in virtual platforms in Delhi, India
Bibliographic record
Abstract
INTRODUCTION: In India, the HIV epidemic is concentrated among Key Populations (KPs), such as men who have sex with men (MSM), who bear a disproportionate burden of HIV disease. Conventional targeted interventions (TI) mitigate HIV transmission among MSM by focusing on physical hotspots. As increasingly, there is a shift within India's MSM community to connect with sex partners online, novel approaches are needed to map virtual platforms where sexual networks are formed. The objective of this study was to estimate the number of MSM in Delhi using virtual platforms to connect for sex and to describe patterns of their use. METHODS: The study was conducted in the state of Delhi among MSM over 18 years of age who used virtual platforms to look for sexual partners. Virtual platforms were identified through community consultations. Size estimation was carried out by enumerating the number of online users, accounting for duplication across sites and time and based on interviews with 565 MSM. RESULTS: 28,058 MSM (95% CI: range 26,455-29,817) use virtual sites to find sexual partners. We listed 14 MSM specific virtual sites, 14 general virtual sites, 19 social networking pages and 112 messenger groups, all used by MSM. Five virtual sites met feasibility criteria to be included in the virtual mapping. Of the MSM on these sites, 81% used them at night and 94% used them on Sundays, making these the peak time and day of use. Only 16% of users were aware of organizations providing HIV services and 7% were contacted by peer educators in the preceding three months. Two-fifths (42%) also visited a physical location to connect with sexual partners in the month prior to the study. DISCUSSION: TI programs that focus on physical hotspots do not reach the majority of MSM who use virtual sites. MSM active on virtual sites have a low awareness of HIV services. Virtual mapping and programmatic interventions to include them must be incorporated into current public health interventions to reach MSM at risk of HIV.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".