Exploring young Black gay, bisexual and other men who have sex with men’s PrEP knowledge in Toronto, Ontario, Canada
Bibliographic record
Abstract
Despite significant advances in the HIV treatment and prevention landscape such as pre-exposure prophylaxis (PrEP), young Black-Canadian gay, bisexual and other sexual minority men continue to experience disproportionately high rates of HIV infection. While research has explored the factors associated with their higher HIV exposure and the efficacy of STI/HIV prevention programmes, there remains a paucity of research on their knowledge of HIV prevention strategies such as PrEP. We interviewed twenty-two young men and used a constructivist grounded theory approach to qualitatively analyse these young men's PrEP knowledge. Intersectionality and the social ecological model allowed us to explore how social locations (e.g. race, sexual orientation), interacted with individual, interpersonal and community contexts to shape their understanding. Our analysis revealed two interrelated barriers to PrEP knowledge and uptake. The first centred on the ineffectiveness of institutions in disseminating PrEP information to participants. The second focused on the impact of participants' social locations and perceptions of PrEP users based on their PrEP knowledge. Findings suggest the need for more targeted, culturally congruent PrEP dissemination strategies and PrEP prescription policies that acknowledge the various social locations and ecologies in which young Black gay, bisexual and other men who have sex with men reside.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".