Exploring the role of sex-seeking apps and websites in the social and sexual lives of gay, bisexual and other men who have sex with men: a cross-sectional study
Bibliographic record
Abstract
Background The objective of this study was to explore the relationship between online sex-seeking, community/social attachment and sexual behaviour. METHODS: Respondent-driven sampling was used to recruit 774 sexually active gay and bisexual men in Vancouver, Canada, aged ≥16 years. Multivariable logistic regression compared men who had used online sex-seeking apps/websites in the past 6 months (n=586) with those who did not (n=188). RESULTS: Multivariable results showed that online sex seekers were more likely to be younger [adjusted odds ratio (aOR)=0.95, 95% CI: (0.93-0.96)], college educated [aOR=1.60, 95% CI: (1.07, 2.40)], have more Facebook friends [aOR=1.07, 95% CI: (1.01, 1.13)], spend more social time with other gay men [aOR=1.99, 95% CI: (1.33-2.97)], and were less likely to identify emotionally with the gay community [aOR=0.93, 95% CI: (0.86-1.00)]. Further, they had displayed high sensation-seeking behaviour [aOR=1.08, 95% CI: (1.03-1.13)], were more likely to engage in serodiscordant/unknown condomless anal sex [aOR=2.34, 95% CI: (1.50-3.66)], use strategic positioning [aOR=1.72, 95% CI: (1.08-2.74)], ask their partner's HIV-status prior to sex [aOR=2.06, 95% CI: (1.27-3.37)], and have ever been tested for HIV [aOR=4.11, 95% CI: (2.04-8.29)]. CONCLUSION: These findings highlight the online and offline social behaviour exhibited by gay and bisexual men, pressing the need for pro-social interventions to promote safe-sex norms online. We conclude that both Internet and community-based prevention will help reach app/web users.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".