Prescribing pre-exposure prophylaxis (PrEP) for HIV prevention: A cross-sectional survey of General Practitioners in Australia
Bibliographic record
Abstract
ABSTRACT Background Pre-exposure prophylaxis (PrEP) is a safe and effective medication for preventing HIV acquisition. We examined Australian general practitioners’ (GP) knowledge of PrEP efficacy, characteristics associated with ever prescribing PrEP, and barriers to prescribing. Methods We conducted an online cross-sectional survey of GPs working in Australia between April and October 2022. We performed univariable and multivariable logistic regression analyses to identify factors associated with: 1) the belief that PrEP was at least 80% efficacious; and 2) ever prescribed PrEP. We asked participants to rate the extent to which barriers affected their prescribing of PrEP. Results 407 participants with a median age of 38 years (interquartile range 33-44). Half of the participants (50%, 205/407) identified how to correctly take PrEP, 63% (258/407) had ever prescribed PrEP, and 45% (184/407) felt confident with prescribing PrEP. Ever prescribing PrEP was associated with younger age (AOR 0.97, 95%CI: 0.94-0.99), extra training in sexual health (AOR 2.57, 95%CI: 1.54-4.29), and being a S100 Prescriber (OR 2.95, 95%CI: 1.47-5.90). The main barriers to prescribing PrEP included: ‘Difficulty identifying clients who require PrEP/relying on clients to ask for PrEP’ (76%, 310/407), ‘Lack of knowledge about PrEP’ (70%, 286/407), and ‘Lack of time’ (69%, 281/407). Conclusion Less than half of our GP respondents were confident in prescribing PrEP, and most had difficulty identifying who would require PrEP. Specific training on PrEP, which focuses on PrEP knowledge, identifying suitable clients, and making it time efficient is recommended, with GPs being remunerated for their time.
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 | 0.001 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".