HIV testing among transgender and non-binary individuals in Canada
Bibliographic record
Abstract
Transgender and non-binary populations experience sexual health inequities, including increased risk of HIV acquisition and barriers to accessing sexual healthcare. However, there is little evidence describing HIV testing prevalence among this population in Canada. This study analyzed data from Trans PULSE Canada, a national convenience sampling health survey of transgender and non-binary people, to assess the prevalence and predictors of HIV testing. Data from 679 participants who reported past-year condomless anal or vaginal sex with a partner were analyzed to evaluate lifetime and past-year HIV testing. Exploratory block-wise modified Poisson regression models were used to identify predictors of past-year testing. Overall, 62% of participants had ever been tested for HIV, and 33% had been tested in the past year. Participants who were single or in non-monogamous relationships (vs. monogamous) were more likely to have been tested in the past year (adjusted prevalence ratio [aPR]: 2.15, 95% CI [1.57, 2.94], and aPR: 2.43, 95% CI [1.81, 3.28], respectively), as were those who reported past-year in-person sex work (aPR: 1.83, 95% CI [1.39, 2.40]). Past-year testing did not significantly differ across sociodemographic, personal health, primary care, gender affirmation, or discrimination variables. HIV testing estimates were low among transgender and non-binary individuals at sexual risk of HIV acquisition in Canada relative to national testing guidelines. The likelihood of past-year testing was similar across most subgroups of transgender and non-binary participants. Encouragingly, indicators of greater HIV-related sexual risk were associated with a higher likelihood of past-year testing.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| 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".