Awareness, acceptance, and impact of undetectable equals untransmittable (U = U) among people living with HIV across Canada
Bibliographic record
Abstract
Undetectable equals Untransmittable (U = U) is a pivotal tool for HIV prevention, stigma reduction, and improving quality of life for people living with HIV. This study examined awareness, acceptance, and impact of U = U among people living with HIV across Canada, and explored differences across sociodemographic characteristics. From 2018-2024, 1,083 participants were recruited in-person and online using snowball sampling. Peer researchers conducted interviews, initially in person and later mostly online due to COVID-19. Demographic data and U = U outcomes were collected and analyzed using multivariate logistic regression. Overall, 72% of participants had heard of U = U, 67% strongly accepted it, and 51% had discussed it with a healthcare provider. Awareness and acceptance were lower among heterosexual and bisexual participants. Cis-women and participants who were unemployed were less likely to report positive impacts from U = U. Black-identifying participants were more likely to report benefits, while Indigenous participants were more likely to believe U = U could reduce stigma and shift public opinion. Older participants were less likely to discuss U = U with a healthcare provider. Findings highlight U = U's potential to reduce stigma, but gaps remain in awareness, acceptance, and provider communication. Tailored strategies are needed to engage diverse communities and support healthcare providers in confidently sharing the zero-risk message.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".