Role of HIV Serostatus Communication on Frequent HIV Testing and Self-Testing Among Men Who Have Sex With Men Who Seek Sexual Partners on the Internet in Zhejiang, China: Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Men who have sex with men (MSM) are increasingly using the internet to meet casual sexual partners. Those who do are at higher risk of sexually transmitted diseases. However, little is known about the rates and associations of frequent HIV testing and self-testing among such MSM. OBJECTIVE: We aimed to examine HIV serostatus communication and perceptions regarding the HIV infection risk of internet-based partners, along with their associations with frequent HIV testing and self-testing. METHODS: A cross-sectional study was conducted between May 2018 and April 2019 in Zhejiang Province, China. The study participants were assigned male at birth, were aged 18 years or older, had had casual sex with another male found through the internet in the last 6 months, and were HIV-negative. Information was obtained on HIV-testing behavior, along with demographic characteristics, HIV-related knowledge, internet-based behaviors, sexual behaviors with male partners, HIV serostatus communication, and perceptions regarding the HIV infection risk of internet-based partners. Uni- and multivariate logistic regression models were used to measure the associations of HIV testing and self-testing. RESULTS: The study recruited 281 individuals who had sought casual sexual partners through the internet during the previous 6 months. Of the participants, 61.9% (174/281) reported frequent HIV testing (twice or more frequently) and 50.9% (119/234; 47 with missing values) reported frequent HIV self-testing. MSM who always or usually communicated about the HIV serostatus of internet-based partners in the previous 6 months had 3.12 (95% CI 1.76-5.52) and 2.45 (95% CI 1.42-4.22) times higher odds of being frequently tested or self-tested for HIV, respectively, compared with those who communicated about this issue minimally or not at all. CONCLUSIONS: There remains a need to improve the frequency of HIV testing and self-testing among internet-based MSM. HIV serostatus communication should be improved within the context of social networking applications to promote frequent HIV testing among internet-based MSM, especially for those who communicated about this issue minimally or not at all.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".