Challenges of providing HIV pre-exposure prophylaxis across Australian clinics: qualitative insights of clinicians
Bibliographic record
Abstract
Background HIV pre-exposure prophylaxis (PrEP) has been rapidly implemented in Australia, initially through restricted access in demonstration studies, and then through prescribing across sexual health clinics and general practice settings. In 2018, PrEP was publicly subsidised for people with Medicare (universal health insurance for citizens, permanent residents and those from countries with reciprocal arrangements). There is little research examining the experiences of PrEP providers in Australia, and existing research has been primarily conducted before public subsidy. METHODS: In this qualitative study, we examine the challenges that have emerged for PrEP-providing clinicians after public subsidy for PrEP was introduced. We conducted 28 semi-structured interviews in 2019-20 with PrEP providers in two Australian states, and analysed data thematically. Participants included general practitioners (GPs), sexual health nurses and sexual health physicians. RESULTS: Sexual health services have been reconfigured to meet changing patient demand, with an emphasis on ensuring equitable financial access to PrEP. Restrictions to nurse-led PrEP frustrated some participants, given that nurses had demonstrated competence during trials. GPs were believed to be less effective at prescribing PrEP, but GP participants themselves indicated that PrEP was an easy intervention, but difficult to integrate into general practice. Participants expressed discomfort with on-demand PrEP. CONCLUSIONS: Our findings indicate that supporting ways for patients without Medicare to access PrEP inexpensively, advocating for nurse-led PrEP, and developing guidelines adapted to general practice consultations could ensure that PrEP is delivered more effectively and equitably. Additionally, PrEP providers require encouragement to build confidence in providing on-demand PrEP.
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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 | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".