Technology-Based Interventions to Promote the HIV Preexposure Prophylaxis (PrEP) Care Continuum: Protocol for a Systematic Review
Bibliographic record
Abstract
BACKGROUND: Preexposure prophylaxis (PrEP) is a promising biomedical intervention for HIV prevention. Researchers have proposed the PrEP care continuum to guide and evaluate PrEP implementation programs. Technology-based interventions (TBIs) have been widely used in HIV prevention and treatment programs, including for the promotion of the PrEP care continuum. The rapid development of new interventions using technology and electronic health methods emphasizes the need for a review of the effectiveness of these TBIs. OBJECTIVE: The aim of this systematic review is to summarize the effectiveness and acceptability of TBIs used to promote the HIV PrEP care continuum. METHODS: We will conduct a systematic literature search in PubMed, Embase, MEDLINE, PsycINFO, Web of Science, CINAHL, and the Cochrane Central Register of Controlled Trials following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Only intervention studies (ie, studies meeting the criteria of randomized controlled trials or quasi-experimental studies) evaluating the effectiveness of TBIs will be included. We will search the National Institutes of Health Research Portfolio Online Reporting Tools (NIH RePORT) for interventions involving PrEP. At least 2 reviewers will independently screen and select the studies, extract the data, and evaluate the quality of the studies, and discrepancies will be resolved by a senior author. We will provide a narrative synthesis of the included studies and present details about the study populations, interventions, and PrEP-related outcomes of significance. RESULTS: The protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO; CRD42021249562). As of August 2021, we have completed the initial search and identified 1213 records. Study screening and data extracting are in progress. We expect the results to be ready by summer 2022. CONCLUSIONS: The findings of this review will summarize successful experiences and lessons learned from the existing literature and therefore inform the design and implementation of intervention studies for PrEP care promotion. TRIAL REGISTRATION: PROSPERO CRD42021249562; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=249562. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/33045.
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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 | 0.068 | 0.092 |
| Meta-epidemiology (narrow) | 0.007 | 0.007 |
| Meta-epidemiology (broad) | 0.022 | 0.018 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.095 | 0.012 |
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".