Gay, bisexual, and other men who have sex with men: Barriers and facilitators to healthcare access in Ottawa
Bibliographic record
Abstract
Despite access to healthcare that is similar to the entire population, gay, bisexual, and other men who have sex with men (gbMSM) have worse rates for many pathologies, including sexually transmitted infections (STIs) and HIV, mental health, and substance use. To better understand the barriers to care that occur during healthcare interactions—in an effort to make recommendations to improve these clinical encounters—we engaged in focus groups with three groups of gbMSM. We explored the experiences and concerns of these men regarding healthcare. In total, we interviewed 32 men. Our findings identified that the two most pronounced barriers to delivery of comprehensive and impactful healthcare for gbMSM were (1) wariness and distrust of the healthcare system and fears of neglect and overt discrimination, and (2) a lack of knowledge among healthcare professionals about specific health concerns for MSM. Facilitators as identified by participants were (1) creating safe spaces where MSM are recognized, (2) not only developing but demonstrating an accepting and supportive therapeutic relationship with patients, and (3) attention to issues of disclosure and confidentiality. These findings highlight both ongoing issues in the delivery of healthcare for gbMSM in Canada, and tangible solutions that practitioners can implement immediately to address these problems.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".