High heterogeneity of HIV-related sexual risk among transgender people in Ontario, Canada: a province-wide respondent-driven sampling survey
Bibliographic record
Abstract
BACKGROUND: Studies of HIV-related risk in trans (transgender, transsexual, or transitioned) people have most often involved urban convenience samples of those on the male-to-female (MTF) spectrum. Studies have detected high prevalences of HIV-related risk behaviours, self-reported HIV, and HIV seropositivity. METHODS: The Trans PULSE Project conducted a multi-mode survey using respondent-driven sampling to recruit 433 trans people in Ontario, Canada. Weighted estimates were calculated for HIV-related risk behaviours, HIV testing and self-reported HIV, including subgroup estimates for gender spectrum and ethno-racial groups. RESULTS: Trans people in Ontario report a wide range of sexual behaviours with a full range of partner types. High proportions - 25% of female-to-male (FTM) and 51% of MTF individuals - had not had a sex partner within the past year. Of MTFs, 19% had a past-year high-risk sexual experience, versus 7% of FTMs. The largest behavioural contributors to HIV risk were sexual behaviours some may assume trans people do not engage in: unprotected receptive genital sex for FTMs and insertive genital sex for MTFs. Overall, 46% had never been tested for HIV; lifetime testing was highest in Aboriginal trans people and lowest among non-Aboriginal racialized people. Approximately 15% of both FTM and MTF participants had engaged in sex work or exchange sex and about 2% currently work in the sex trade. Self-report of HIV prevalence was 10 times the estimated baseline prevalence for Ontario. However, given wide confidence intervals and the high proportion of trans people who had never been tested for HIV, estimating the actual prevalence was not possible. CONCLUSIONS: Results suggest potentially higher than baseline levels of HIV; however low testing rates were observed and self-reported prevalences likely underestimate seroprevalence. Explicit inclusion of trans people in epidemiological surveillance statistics would provide much-needed information on incidence and prevalence. Given the wide range of sexual behaviours and partner types reported, HIV prevention programs and materials should not make assumptions regarding types of behaviours trans people do or do not engage in.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".