Pre-exposure Prophylaxis Awareness and Use Among Transgender and Nonbinary Individuals in Canada
Bibliographic record
Abstract
BACKGROUND: Transgender and nonbinary populations are disproportionately affected by HIV and face barriers to accessing HIV-related services. Pre-exposure prophylaxis (PrEP) may benefit those at risk of HIV acquisition. However, PrEP awareness and uptake, along with potential barriers and facilitators, have not been investigated among transgender and nonbinary individuals living in Canada. SETTING: This study analyzed data from 1965 participants of the 2019 Trans PULSE Canada survey, a national convenience sampling survey of transgender and nonbinary individuals in Canada. METHODS: Data were analyzed to estimate levels of PrEP awareness and uptake and to identify predictors of PrEP awareness among the study population. Prevalence ratios estimated from block-wise modified Poisson regression models were used to assess predictors of PrEP awareness. RESULTS: PrEP awareness, lifetime PrEP use, and current PrEP use were estimated to be 71.0%, 2.2%, and 0.9%, respectively, among the full sample, and 82.3%, 7.3%, and 3.8% among those with indications for PrEP use. Respondents who were aged 45 years or older, transfeminine, Indigenous, living in Atlantic Canada or Quebec, and had high school education or less were significantly less likely to be aware of PrEP. Lifetime sex work, past-year HIV/STI testing, being single or in a nonmonogamous relationship, and higher levels of emotional social support were positively associated with PrEP awareness. CONCLUSIONS: There is a need to improve PrEP awareness and particularly uptake among transgender and nonbinary individuals in Canada. This study revealed inequities in PrEP awareness within this population, which may serve as targets for future public health initiatives.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| 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".