P1-S5.40 Sex cells: a pilot study investigating cell phone-based sexual networks among men who have sex with men in South India
Bibliographic record
Abstract
Background A high HIV prevalence (19–32%) has been reported among men who have sex with men (MSM) in South India. Indeed, sexual networks play a central role in the spread of HIV in these communities but have rarely been studied because of intense social stigma and methodological and ethical challenges. Although cell phones are commonly used among MSM to contact sexual partners in India, few studies have explored the formation of such sexual networks. This study sought to understand the structure, context and evolution of cell phone-based sexual networks of MSM in three South Indian cities. Methods Sampling frames in the three cities were established using MSM contacts stored in the cell phones of community-based researchers (CBRs). Study participant “seeds” were randomly selected from these social networks. Seeds were asked to recruit their sexual partners, who completed surveys about their sexual practices with regular partners and 7-day partners. Network diagrams were constructed using non-nominal codes linking study participants. Results Cell phone contacts represent a useful resource for constructing social networks. Preliminary results indicate the diversity of sexual networks and sexual practices within these networks. Conclusions New community-based methods of exploring sexual networks were assessed, and sexual practices, partner concurrency, and risk behaviours were explored. This information can be used to tailor more specific services for MSM in these communities. As this methodology sampled from social networks, more hidden “individuals who do not access health services were included in the study.”
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".