Associations Between Online Casual Sexual Behavior and HIV-Related Risk Behaviors Among Men Who Have Sex With Men in Southeast China: Cross-Sectional Study
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».