HIV-Related Sexual Risk Among Transgender Men Who Are Gay, Bisexual, or Have Sex With Men
Bibliographic record
Abstract
BACKGROUND: This study is among the first to examine factors associated with HIV-related sexual risk among transgender men and other transmasculine persons who are gay, bisexual, or have sex with men (T-GBMSM). METHODS: In 2009-2010, 433 transgender people in Ontario, Canada, participated in a multimode respondent-driven sampling survey, including 158 T-GBMSM. Analyses were weighted using respondent-driven sampling II methods to adjust for differential recruitment probabilities; confidence intervals (CI) were adjusted for clustering by shared recruiter. Prevalence ratios (PR) for associations with past-year high sexual risk (condomless intercourse outside a seroconcordant monogamous relationship) were estimated using average marginal predictions from logistic regression. RESULTS: Of T-GBMSM (mean age = 29.8; 52% living full time in felt gender; 25% Aboriginal or persons of color; 0% self-reported HIV positive), 10% had high sexual risk activity in the past year. Among the 34% with a past-year cisgender (non-transgender) male sex partner, 29% had high sexual risk. In multivariable analyses, older age, childhood sexual abuse (adjusted PR, APR = 14.03, 95% CI: 2.32 to 84.70), living full time in one's felt gender (APR = 5.20, 95% CI: 1.11 to 24.33), and being primarily or exclusively attracted to men (APR = 5.54, 95% CI: 2.27 to 13.54) were each associated with sexual risk. Of psychosocial factors examined, past-year stimulant use (APR = 4.02, 95% CI: 1.31 to 12.30) and moderate depressive symptoms (APR = 5.77, 95% CI: 1.14 to 29.25) were associated with higher sexual risk. CONCLUSIONS: T-GBMSM seem to share some HIV acquisition risk factors with their cisgender counterparts. HIV prevention interventions targeting T-GBMSM who are predominantly attracted to men and interventions addressing sequelae of childhood sexual abuse may be warranted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".