Use of HIV pre-exposure prophylaxis among men who have sex with men: low uptake and retention despite high-risk indications
Bibliographic record
Abstract
HIV PrEP is over 99% effective in preventing HIV when medication adherence is high. Despite this, uptake and retention in PrEP care remains less than optimal. We investigated whether gbMSM with objective risk factors for HIV who were automatically offered PrEP would have higher uptake and retention in PrEP care. For this, gbMSM with clinical evidence of HIV risk received a reflexive offer for PrEP from a nurse. The number of offers, referral acceptance, presentation to the first appointment, initiation and retention at 6 months were examined. Of 1181 gbMSM with objective HIV risk factors who were offered PrEP, only 50% accepted, 28% initiated and 16% remained on PrEP at 6 months. Loss across the cascade was more pronounced for youth. We found a notable disconnect between recent STI diagnosis and acceptance, initiation and retention in PrEP. This notwithstanding, 137 at-risk individuals were retained on PrEP because of an active offer. PrEP delivered by nurses was as effective as that delivered by infectious disease physicians. While active offer PrEP successfully brought at-risk individuals into care, more work is required to understand the perceptions of risk, the benefits and challenges of PrEP use, and how stigma and structural barriers affect retention among diverse groups affected by HIV.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".