HIV prevalence among a retrospective clinical cohort of transgender women in Canada: Results of the Montreal-Toronto Trans study, collected 2018–2019
Bibliographic record
Abstract
BACKGROUND: HIV prevalence data among transgender (trans) people are not routinely collected in national estimates, including Canada, contributing to gender-based inequities. We examined HIV prevalence and associated factors among trans women in clinical care in two large Canadian cities. METHODS: Retrospective chart data of trans women aged 16+ were collected from six family medicine and/or HIV clinics in Montreal and Toronto, Canada, 2018-2019. Multinomial logistic regression was used to analyze factors associated with documented HIV positive or missing HIV status relative to documented HIV negative status. RESULTS: Among 1,059 patients, 7.5% were HIV positive, 54.4% HIV negative, and 38.1% missing HIV data. Findings showed lower odds of being HIV positive for those <30 years or 30-50 years (vs. >50 years); higher odds were seen for those: of Black race/ethnicity (vs. white), landed immigrant or refugee (vs. Canadian citizen), receiving social assistance (vs. not), and whom ever having used recreational drugs. CONCLUSIONS: Albeit high, the prevalence of HIV was lower than expected based on global estimates. Missing HIV status data suggest gaps in testing. Findings highlight socioeconomic and clinical realities among trans women in Canada and inform future HIV prevention and support.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".