Factors Influencing Clinicians’ Willingness to Prescribe Pre-exposure Prophylaxis for Persons at High Risk of HIV in China: Cross-sectional Online Survey Study
Bibliographic record
Abstract
BACKGROUND: Pre-exposure prophylaxis (PrEP) is an effective HIV prevention measure. Clinicians play a crucial role in PrEP implementation, and their knowledge, attitudes, and career experience may affect their willingness to prescribe PrEP. However, little is known about the attitudes and willingness of clinicians to prescribe PrEP in countries without PrEP-specific guidelines. OBJECTIVE: We aimed to determine the factors associated with clinicians being willing to prescribe PrEP in China. METHODS: Between May and June 2019, we conducted an online cross-sectional survey of clinicians in 31 provinces across the six administrative regions in China on the WeChat smartphone app platform. Multivariable logistic regression was used to determine factors associated with willingness to prescribe PrEP. RESULTS: Overall, 777 HIV clinicians completed the survey. Most of the respondents had heard of PrEP (563/777, 72.5%), 31.9% (248/777) thought that PrEP was extremely effective for reducing the risk of HIV infection, and 47.2% (367/777) thought that it was necessary to provide PrEP to high-risk groups. After adjusting for age, gender, ethnicity, and educational background of the clinicians, the following factors significantly increased the odds of the clinicians being willing to prescribe PrEP: having worked for more than 10 years, compared to 5 years or less (adjusted odds ratio [aOR] 2.82, 95% CI 1.96-4.05); having treated more than 100 patients living with HIV per month, compared to 50 patients or fewer (aOR 4.16, 95% CI 2.85-6.08); and having heard of PrEP (aOR 7.32, 95% CI 4.88-10.97). Clinicians were less likely to be willing to prescribe PrEP if they were concerned about poor adherence to PrEP (aOR 0.66, 95% CI 0.50-0.88), the lack of PrEP clinical guidelines (aOR 0.47, 95% CI 0.32-0.70), and the lack of drug indications for PrEP (aOR 0.49, 95% CI 0.32-0.76). CONCLUSIONS: About half of all clinicians surveyed were willing to prescribe PrEP, but most surveyed had a low understanding of PrEP. Lack of PrEP clinical guidelines, lack of drug indications, and less than 11 years of work experience were the main barriers to the surveyed clinicians' willingness to prescribe PrEP. Development of PrEP clinical guidelines and drug indications, as well as increasing the availability of PrEP training, could help improve understanding of PrEP among clinicians and, thus, increase the number willing to prescribe 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".