Inequitable access to PrEP among gay, bisexual, and other men who have sex with men in Canada: A network analysis of social indicators
Bibliographic record
Abstract
This paper examines demographic, structural, and syndemic variables to map facilitators and barriers to accessing pre-exposure prophylaxis (PrEP) among gay, bisexual, and other men who have sex with men (GBM) in the three largest cities in Canada, Vancouver, Toronto, and Montreal. Focusing on factors in the later stages of the PrEP cascade, this study first performed a logistic regression analysis and reports adjusted odds ratios, then entered statistically significant social indicators into a network analysis to profile the interrelated and sometimes mutually reinforcing social conditions that shape inequitable access to PrEP among Canadian GBM. Barriers to accessing a gay-friendly health care provider (HCP) and financial barriers remain primary nodes associated with inequitable PrEP access. These two nodes are, in turn, linked to other social indicators: experiencing stigma from an HCP, being less likely to be out to an HCP (most common among bisexual and queer men), and not being able to find an HCP accepting of their sexuality (most common among Indigenous, Black, and some other GBM of colour). The cost of PrEP was also a barrier, especially for less educated and un- or under-employed GBM, as well as newcomers to Canada, who more often lacked insurance for medications. These findings point toward the importance of having a primary HCP and finding an HCP who is culturally competent regarding the sexualities of gay and bisexual men, queer and trans people. These barriers may also explain other social inequities experienced by Black, Indigenous, and other GBM of colour, and by people who do not have health insurance that covers all or most of the cost of PrEP. This pattern of inequitable access to PrEP points toward the urgency of providing universal access without financial barriers (currently available in some Canadian provinces but not others) and supporting access points with a reputation for welcoming GBM in their full diversity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".