Systematic review of alternative HIV preexposure prophylaxis care delivery models to improve preexposure prophylaxis services
Bibliographic record
Abstract
OBJECTIVES: To identify types, evidence, and study gaps of alternative HIV preexposure prophylaxis (PrEP) care delivery models in the published literature. DESIGN: Systematic review and narrative synthesis. METHODS: We searched in the US Centers for Disease Control and Prevention (CDC) Prevention Research Synthesis (PRS) database through December 2022 (PROSPERO CRD42022311747). We included studies published in English that reported implementation of alternative PrEP care delivery models. Two reviewers independently reviewed the full text and extracted data by using standard forms. Risk of bias was assessed using the adapted Newcastle-Ottawa Quality Assessment Scale. Those that met our study criteria were evaluated for efficacy against CDC Evidence-Based Intervention (EBI) or Evidence-Informed Intervention (EI) criteria or Health Resources and Services Administration Emergency Strategy (ES) criteria, or for applicability by using an assessment based on the Reach, Effectiveness, Adoption, Implementation, and Maintenance framework. RESULTS: This review identified 16 studies published between 2018 and 2022 that implemented alternative prescriber ( n = 8), alternative setting for care ( n = 4), alternative setting for laboratory screening ( n = 1), or a combination of the above ( n = 3) . The majority of studies were US-based ( n = 12) with low risk of bias ( n = 11). None of the identified studies met EBI, EI, or ES criteria. Promising applicability was found for pharmacists prescribers, telePrEP, and mail-in testing. CONCLUSIONS: Delivery of PrEP services outside of the traditional care system by expanding providers of PrEP care (e.g. pharmacist prescribers), as well as the settings of PrEP care (i.e. telePrEP) and laboratory screening (i.e. mail-in testing) may increase PrEP access and care delivery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".