Examining HIV Testing Coverage and Factors Influencing First-Time Testing Among Men Who Have Sex With Men in Zhejiang Province, China: Cross-Sectional Study Based on a Large Internet Survey
Bibliographic record
Abstract
BACKGROUND: Men who have sex with men (MSM) constitute a significant population of patients infected with HIV. In recent years, several efforts have been made to promote HIV testing among MSM in China. OBJECTIVE: This study aimed to assess HIV testing coverage and factors associated with first-time HIV testing among MSM to provide a scientific basis for achieving the goal of diagnosing 95% of patients infected with HIV by 2030. METHODS: This cross-sectional study was conducted between July 2023 and December 2023. MSM were recruited from the "Sunshine Test," an internet platform that uses location-based services to offer free HIV testing services to MSM by visiting the WeChat official account in Zhejiang Province, China. Participants were required to complete a questionnaire on their demographic characteristics, sexual behaviors, substance use, and HIV testing history. A logistic regression model was used to analyze first-time HIV testing and its associated factors. RESULTS: A total of 7629 MSM participated in the study, with 87.1% (6647) having undergone HIV testing before and 12.9% (982) undergoing HIV testing for the first time. Multivariate logistic regression analysis revealed that first-time HIV testing was associated with younger age (adjusted odds ratio [aOR] 2.55, 95% CI 1.91-3.42), lower education (aOR 1.39, 95% CI 1.03-1.88), student status (aOR 1.35, 95% CI 1.04-1.75), low income (aOR 1.55, 95% CI 1.16-2.08), insertive anal sex role (aOR 1.28, 95% CI 1.05-1.56), bisexuality (aOR 1.69, 95% CI 1.40-2.03), fewer sex partners (aOR 1.44, 95% CI 1.13-1.83), use of rush poppers (aOR 2.06, 95% CI 1.70-2.49), unknown HIV status of sex partners (aOR 1.40, 95% CI 1.17-1.69), lack of awareness of HIV pre-exposure prophylaxis (aOR 1.39, 95% CI 1.03-1.88), and offline HIV testing uptake (aOR 2.08, 95% CI 1.80-2.41). CONCLUSIONS: A notable 12.9% (982/7629) of MSM had never undergone HIV testing before this large internet survey. We recommend enhancing HIV intervention and testing through internet-based platforms and gay apps to promote testing among MSM and achieve the target of diagnosing 95% of patients infected with HIV by 2030.
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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 | 0.000 |
| 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".