Context and Barriers to the Prescription of Nonoccupational Postexposure Prophylaxis Among HIV Medical Care Providers: National Internet-Based Observational Study in China
Bibliographic record
Abstract
BACKGROUND: Nonoccupational postexposure prophylaxis (nPEP) is an effective HIV biomedical prevention strategy. The research and use of nPEP are mainly concentrated in the developed world, while little is known about the knowledge, attitudes, and practices of nPEP among HIV medical care providers in developing countries. OBJECTIVE: We aimed to assess the nPEP knowledge and prescribing practice among HIV medical care providers in mainland China. METHODS: HIV medical care providers were recruited in China during May and June 2019 through an online survey regarding nPEP-related knowledge, attitudes, and clinical prescription experiences. Multivariable logistic regression was performed to identify factors associated with prescribing nPEP among HIV medical care providers. RESULTS: A total of 777 eligible participants participated in this study from 133 cities in 31 provinces in China. Of the participants, 60.2% (468/777) were unfamiliar with nPEP and only 53.3% (414/777) of participants ever prescribed nPEP. HIV care providers who worked in a specialized infectious disease hospital (vs general hospital, adjusted odds ratio [aOR] 2.49; 95% CI 1.85-3.37), had practiced for 6-10 years (vs 5 or fewer years, aOR 3.28; 95% CI 2.23-4.80), had practiced for 11 years or more (vs 5 or fewer years, aOR 3.75; 95% CI 2.59-5.45), and had previously prescribed occupational PEP (oPEP, aOR 4.90; 95% CI 3.29-7.29) had a significantly positive association with prescribing nPEP. However, unfamiliarity with nPEP (aOR 0.08; 95% CI 0.05-0.11), believing nPEP may promote HIV high-risk behavior (aOR 0.53; 95% CI 0.36-0.77) or result in HIV drug resistance (aOR 0.53; 95% CI 0.36-0.77) among key populations, and self-reported having no written oPEP guideline in place (aOR 0.53; 95% CI 0.35-0.79) were negatively associated with nPEP prescription behavior. CONCLUSIONS: HIV medical care providers have insufficient nPEP knowledge and an inadequate proportion of prescribing, which may impede the scale-up of nPEP services to curb HIV acquisition. The implementation of tailored nPEP training or retraining to HIV medical care providers would improve this situation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".