Enhancing daily oral PrEP adherence with digital communications: Protocol for a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Pre-exposure prophylaxis (PrEP) stands as an effective tool in preventing HIV transmission among individuals at risk of HIV infection. However, the effectiveness of daily oral PrEP is contingent on the adherence of its users, which can pose a challenge for many individuals. Various studies have explored different interventions aimed at bolstering PrEP adherence. One recurring type of intervention revolves around digital communication (e.g., SMS, mobile applications) to send reminders for PrEP usage. The objective of our systematic review and meta-analysis is to address the following research question: What is the effectiveness of digital communication interventions in enhancing daily oral PrEP adherence among individuals at a heightened risk of HIV infection? This paper presents our study protocol. METHOD AND ANALYSIS: We will conduct searches across four health-related databases: Embase, PubMed, Web of Science, and PsycINFO. We will also explore other sources, including clinical trials registries and grey literature. Our search will be restricted to original randomized controlled trials published in English, French, and Spanish conducted since 2012, when PrEP was approved, to today. To ensure rigor, three reviewers will perform the systematic review and meta-analysis. This systematic review will adhere to the guidelines outlined in the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Our primary outcome of interest is proper daily oral PrEP adherence, which we will measure using association metrics (e.g., odds ratios). DISCUSSION: This review will offer insights into the effectiveness of utilizing digital communication methods to assist individuals at risk of HIV in improving their PrEP adherence. PROTOCOL REGISTRATION NUMBER: International Prospective Register for Systematic Reviews (PROSPERO) number CRD42023471269.
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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 | 0.075 | 0.115 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.020 | 0.026 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.089 | 0.009 |
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".