Influence of Types of Stigmas on Risk of HIV Transmission, Prevention, and Treatment among Men Having Sex with Men (MSM) in Canada: A Scoping Review Protocol
Bibliographic record
Abstract
Men who have sex with men (MSM) in Canada remain disproportionately affected by HIV despite the availability of effective biomedical prevention and treatment strategies such as Pre-exposure prophylaxis, (PrEP), post-exposure prophylaxis (PEP), and antiretroviral therapy (ART). A growing body of research suggests that stigma, particularly when intersecting across HIV status, sexual orientation, and racial or ethnic identity, acts as an important barrier to HIV prevention, testing, and treatment. However, a comprehensive understanding of how different types of stigma influence HIV-related health outcomes among MSM in Canada remains underexplored. This study employs a scoping review approach (Arksey & O’Malley, 2005; Tricco et al., 2018), following Joanna Briggs Institute (JBI) (Aromataris et al., 2024) frameworks, to map existing literature on the influence of stigma on HIV transmission risk, prevention uptake, treatment adherence, and care engagement among MSM in Canada. Peer-reviewed studies and grey literature published between 2002 and 2025 will be systematically searched across six databases and multiple grey literature sources. Inclusion criteria specify Canadian-based studies involving MSM that address one or more forms of stigma (e.g., HIV-related stigma, homophobia, racism). Data will be extracted and synthesized across stigma types, healthcare stages, demographic groups, and regional contexts to identify patterns and knowledge gaps relevant to HIV care among MSM in Canada.
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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 | 0.049 | 0.061 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.028 | 0.022 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".