Knowledge of viral load, PrEP, and HIV-related sexual risk among men who have sex with men in the Waterloo region
Bibliographic record
Abstract
Gay, bisexual, and other men who have sex with men (GBMSM) remain most disproportionately affected by HIV in Canada. HIV-related sexual risk behaviours have been linked to high HIV risk among GBMSM, but prior research has not focused on knowledge of viral load, and the risk it presents for HIV acquisition. The purpose of this study was to explore the relationship between HIV-related sexual risk behaviour and knowledge of viral load among GBMSM. A cross-sectional survey was conducted using a convenience sample of individuals age 16 and older who self-identified as LGBTQ and lived, worked, or resided in the Waterloo region, an urban-rural area in southwestern Ontario ( N = 526). Responses were analyzed from those identifying as GBMSM ( N = 269). Logistic regression models were created to explore sociodemographic, outness, social support, and HIV-related sexual risk variables associated with knowledge of viral load. Multivariable regression models were built to explore the same associations while controlling for confounders. HIV risk was not associated with knowledge of viral load in bivariate or multivariable analyses. Point estimates for low/negligible (odds ratio [OR] 1.10; 95% CI 0.46–2.51) and high risk (OR 1.88; 95% CI 0.68–5.20) suggest trends of higher knowledge with increased HIV risk. Men who engage in sexual risk behaviour may have increased sexual health literacy and awareness of biomedical interventions (e.g., pre-exposure prophylaxis, or PrEP) that reduce HIV risk. Policies are needed that promote acceptance of sexual orientation, improve awareness and access to PrEP, and ensure optimal delivery of HIV education to at-risk groups prior to engagement in higher risk activities.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".