Social support and HIV prevention behaviors among urban HIV-negative gay, bisexual, and other men who have sex with men.
Bibliographic record
Abstract
OBJECTIVE: Supportive social relationships can have direct positive effects on health and mitigate the negative impact of stressors. This study investigated the main effect of perceived social support on STI/HIV risk and prevention behaviors. The buffering effect of perceived social support on the impact of proximal minority stressors, like internalized homonegativity, was also examined on one risk behavior specifically, condomless anal sex (CAS) without HIV preexposure prophylaxis (PrEP) use. METHOD: = 1,409). GBM completed measures of perceived social support, proximal minority stress, and engagement in STI/HIV risk and prevention behaviors. RESULTS: Higher perceived social support was positively associated with a several health behaviors, including recent STI and HIV testing, discussing HIV status with prospective partners, the use of behavioral HIV-risk reduction strategies during sexual encounters, and a lower likelihood of engaging in CAS without PrEP. There was evidence of moderation as well. Among GBM with higher perceived social support, internalized homonegativity was no longer associated with increased odds of engaging in CAS without PrEP. CONCLUSIONS: The results of the current study advance social support theory to GBM in the context of biomedical prevention, showing both evidence of both direct associations and buffering effects on STI/HIV risk and prevention behaviors. This highlights the importance of promoting social support seeking in interventions aimed at improving GBM health. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".