Sexual Preferences and Presentation on Geosocial Networking Apps by Indian Men Who Have Sex With Men in Maharashtra
Bibliographic record
Abstract
BACKGROUND: The affordability of smartphones and improved mobile networks globally has increased the popularity of geosocial networking (GSN) apps (eg, Grindr, Scruff, Planetromeo) as a method for men who have sex with men (MSM) to seek causal sex partners and engage with the queer community. As mobile penetration continues to grow in India, it is important to understand how self-presentation on GSN app is relevant because it offers insight into a population that has not been largely studied. There is very little information about how Indian MSM discuss their sexual preferences and condom preferences and disclose their human immunodeficiency virus (HIV) status with potential sex partners on Web-based platforms. OBJECTIVE: The objective of this study was to describe how self-presentation by Indian MSM on GSN apps contributes to sexual preferences, HIV or sexually transmitted infection (STI) disclosure, and if the presentation differs due to proximity to the Greater Mumbai or Thane region. METHODS: Between September 2013 and May 2014, participants were recruited through banner advertisements on gay websites, social media advertisements and posts, and distribution of print materials at outreach events hosted by lesbian, gay, bisexual, transgender (LGBT) and HIV service organizations in Maharashtra, India. Eligible participants self-identified as being MSM or hijra (transgender) women, living in Maharashtra, aged above 18 years, having regular Internet access, and having at least one male sex partner in the previous 90 days. RESULTS: Indian MSM living inside and outside the Greater Mumbai or Thane region reported an average of 6.7 (SD 11.8) male sex partners in the last 3 months; on average HIV status of the sex partners was disclosed to 2.9 (SD 8.9). The most commonly used websites and GSN apps by MSM living inside Greater Mumbai or Thane region were Planetromeo, Grindr, and Gaydar. Results demonstrated that MSM used smartphones to access GSN apps and stated a preference for both condomless and protected anal sex but did not disclose their HIV status. This low level of HIV disclosure potentially increases risk of HIV or STI transmission; therefore, trends in use should be monitored. CONCLUSIONS: Our data helps to fill the gap in understanding how Indian MSM use technology to find casual sex partners, disclose their sexual preference, and their HIV status on Web-based platforms. As mobile penetration in India continues to grow and smartphone use increases, the use of GSN sex-seeking apps by MSM should also increase, potentially increasing the risk of HIV or STI transmission within the app's closed sexual networks.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".