Associations Between Online Casual Sexual Behavior and HIV-Related Risk Behaviors Among Men Who Have Sex With Men in Southeast China: Cross-Sectional Study
Bibliographic record
Abstract
Background: With the growing popularity and convenience of the internet, an increasing number of men who have sex with men (MSM) are seeking casual sexual partners online. However, the effect of online casual sexual behavior on other HIV-related risk behaviors remains unclear. Objective: This study aims to explore the characteristics of internet-based casual sexual behavior and its relationship with HIV-related risk behaviors among MSM. Methods: This cross-sectional study was conducted between June and December 2018 in 4 cities in Zhejiang Province, China. Peer-driven sampling was used for recruitment. Announcements were disseminated by 4 community-based organizations and 10 voluntary counseling and testing clinics online and offline. After informed consent, participants completed an electronic questionnaire covering demographic characteristics, casual sexual behaviors, HIV-related risk behaviors, and HIV prevention. SPSS (version 19.0; IBM Corp) was used to conduct chi-square tests, univariate and multivariate logistic regression analyses using a backward stepwise method based on the likelihood ratio test, and Poisson regression with robust variance to identify associations between finding casual sexual partners online and other risk behaviors. P values of <.05 were considered statistically significant. Results: In the past 6 months, 40.2% (302/751) of participants reported finding casual sexual partners online; 18.9% (142/751) reported finding casual sexual partners offline; 7.6% (57/751) reported having sexual intercourse with MSM without condoms after drinking alcohol; and 6.9% (52/751) reported condomless sex after using stimulants. Among those who found partners online, 62.5% (188/301) did so more than once per month and 39.5% (113/286) had more than one online sexual partner. In total, 39.3% (114/290) had sex with online partners at home and 10.1% (30/297) sought partners in other cities. Compared with participants who engaged in receptive anal intercourse (or both roles), those who engaged only in insertive intercourse reported a higher proportion of finding partners online more than once per month (72.7% vs 57.4%, P=.01), having more than 2 online sexual partners (52.1% vs 33.3%, P=.002), and conducting inconsistent condom use with online sexual partners (40.0% vs 25.8%, P=.01). Regression analysis showed that, compared with MSM who did not find partners online, those who did were more likely to report finding casual sexual partners offline (adjusted odds ratio [aOR] 9.398; 95% CI 5.956-14.829), having sex without condoms after drinking alcohol (adjusted prevalence ratio [aPR] 1.788; 95% CI 1.062-3.011), and having sexual intercourse without condoms after using stimulants (aPR 2.064; 95% CI 1.178-3.617). Conclusions: Internet-based casual sexual behavior is increasingly common among MSM. Finding partners online was associated with offline partner-seeking and condomless use after alcohol or stimulant use. Future HIV prevention efforts should emphasize behavioral interventions tailored to MSM who use dating apps.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".