Geosocial Networking Smartphone App Use and High-Risk Sexual Behaviors Among Men Who Have Sex With Men Attending University in China: Cross-sectional Study
Bibliographic record
Abstract
BACKGROUND: Gay apps are smartphone-based geosocial networking apps where many men who have sex with men (MSM) socialize and seek sex partners. Existing studies showed that gay app use is associated with greater odds of high-risk sexual behaviors and potentially more HIV infections. However, little is known about this behavior among young MSM. OBJECTIVE: We conducted this study to understand gay app use and its influencing factors among MSM attending university in China. METHODS: From January to March 2019, participants were recruited from 4 regions with large populations of college students in China: Chongqing, Guangdong, Shandong, and Tianjin. The eligibility criteria were MSM aged 16 years or older, self-identified as a university student, and being HIV negative. A self-administered online structured questionnaire was used to collect data on sociodemographic information, sexual behaviors, gay app use, substance use, and HIV testing history. We performed multivariable log-binomial regression to assess correlates of seeking sex partners via gay apps. RESULTS: A total of 447 MSM attending university with an average age of 20.4 (SD 1.5) years were recruited. Almost all participants (439/447, 98.2%) reported gay app use at some point in their life, and 240/439 (53.7%) reported ever seeking sex partners via gay apps. Blued (428/439, 97.5%) was the most popular gay app. Higher proportions of sexual risk behaviors (including seeking sex partners via apps [P<.001], engaging in group sex [P<.001], having multiple sex partners [P<.001], unawareness of sex partners' HIV status [P<.001], and using recreational drugs during sex [P<.02]) were positively associated with the increase in the frequency of gay app use. In multivariable analysis, participants who used gay apps to seek sex partners might be more likely to have multiple sex partners in the past 3 months (adjusted prevalence ratio [APR] 1.53, 95% CI 1.33-1.76; P<.001), engage in group sex in the past 3 months (APR 1.55, 95% CI 1.35-1.78; P<.001), and have sex partners with unknown or positive HIV status (APR 1.72, 95% CI 1.46-2.01; P<.001). CONCLUSIONS: Seeking sex partners via gay apps may associate with the increased high-risk sexual behaviors among MSM attending university. The causality between seeking sex partners via gay apps and increased high-risk sexual behaviors should be further investigated so as to inform potential policies for HIV prevention. TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR1900020645; http://www.chictr.org.cn/showprojen.aspx?proj=34741.
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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 | 0.001 |
| 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 | 0.001 | 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".