Disparities in Healthcare, STI Testing, and PrEP Access Among Newcomer Sexual Minority Men in Canada
Bibliographic record
Abstract
Gay, bisexual, and other men who have sex with men (GBM) are more likely to be diagnosed with HIV and other sexually transmitted infections (STIs) compared with the general population. Although newcomers generally experience a health advantage in Canada compared with non-immigrants and more established immigrants (i.e., healthy immigrant effect), they also experience disparities in access to healthcare services. These disparities in turn may lead to unique vulnerabilities for the sexual health of GBM immigrants. We examined disparities in healthcare access, STI testing, and HIV pre-exposure prophylaxis (PrEP) use among immigrant and non-immigrant GBM. Using baseline data (collected between February 2017 and August 2019) from a multisite cohort study of GBM in Toronto, Vancouver, and Montreal (n=2449), we found that newcomer GBM (migrated ≤ 5 years prior) were less likely to report having a primary healthcare provider than non-immigrants. This had a weak indirect effect in mediating both access to STI testing and use of HIV pre-exposure prophylaxis. These disparities dissipated after controlling for migration precarity (e.g., refugees and those without permanent residency), suggesting that disparities in newcomer GBM healthcare access may, in part, be driven by the large number of newcomers with precarious migration statuses.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".