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Record W4385070321 · doi:10.2196/50866

Evaluating the Feasibility and Acceptability of a Digital Pre-Exposure Prophylaxis Navigation and Activation Intervention for Racially and Ethnically Diverse Sexual and Gender Minority Youth (PrEPresent): Protocol for a Pilot Randomized Controlled Trial

2023· article· en· W4385070321 on OpenAlexvenueno aff
Jacob B Stocks, Sam Calvetti, Matthew T Rosso, Lindsay Slay, Michele D. Kipke, Manuel Puentes, Lisa Hightow‐Weidman

Bibliographic record

VenueJMIR Research Protocols · 2023
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsnot available
FundersNational Institute on Drug AbuseNational Institutes of HealthChildren's Hospital Los Angeles
KeywordsPsychological interventionFocus groupRandomized controlled trialIntervention (counseling)mHealthHealth careMedicineReproductive healthTransgenderPsychologyNursingFamily medicinePopulationEnvironmental health

Abstract

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BACKGROUND: To end the HIV epidemic by 2030, we must double down on efforts to tailor prevention interventions to both young men who have sex with men and transgender and nonbinary youth. There is an urgent need for interventions that specifically focus on pre-exposure prophylaxis (PrEP) uptake in sexual and gender minority youth (SGMY) populations. There are several factors that impact the ability of SGMY to successfully engage in the HIV prevention continuum, including uptake of PrEP. Patient activation, having the knowledge, skills, and self-efficacy to manage one's health, is an important indicator of willingness and ability to manage one's own health and care autonomously. Patient navigation also plays an important role in helping SGMY access PrEP and PrEP care, as navigators help guide patients through the health care system, set up medical appointments, and get financial, legal, and social support. OBJECTIVE: This study aims to evaluate the feasibility and acceptability of a digital PrEP navigation and activation intervention among a racially and ethnically diverse sample of SGMY living in the Los Angeles area. METHODS: In phase 1, we will conduct formative research to inform the development of PrEPresent using qualitative data from key informant interviews involving PrEP care providers and navigators and working groups with SGMY. In phase 2, we will complete 2 rounds of usability testing of PrEPresent with 8-10 SGMY assessing both the intervention content and mobile health delivery platform to ensure features are usable and content is understood. In phase 3, we will conduct a pilot randomized controlled trial to evaluate the feasibility and acceptability of PrEPresent. We will randomize, 1:1, a racially and ethnically diverse sample of 150 SGMY aged 16-26 years living in the Los Angeles area and follow participants for 6 months. RESULTS: Phase 1 (formative work) was completed in April 2021. Usability testing was completed in December 2021. As of June 2023, 148 participants have been enrolled into the PrEPresent pilot randomized controlled trial (phase 3). Enrollment is expected to be completed in July 2023, with final results anticipated in December 2023. CONCLUSIONS: The PrEPresent intervention aims to bridge the gaps in PrEP eligibility and PrEP uptake among racially and ethnically diverse SGMY. By facilitating the delivery of PrEP navigation and focusing on improving patient activation, the PrEPresent intervention has the potential to positively impact the PrEP uptake cascade in the HIV care continuum as well as serve as a model for the tailoring of PrEP interventions based on behavior-based qualifications for PrEP instead of generalized gender-based eligibility. TRIAL REGISTRATION: ClinicalTrials.gov NCT05281393; https://clinicaltrials.gov/ct2/show/NCT05281393. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/50866.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.042
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.058
Threshold uncertainty score0.225

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0420.032
Meta-epidemiology (narrow)0.0050.003
Meta-epidemiology (broad)0.0080.005
Bibliometrics0.0030.003
Science and technology studies0.0040.004
Scholarly communication0.0040.004
Open science0.0040.003
Research integrity0.0070.008
Insufficient payload (model declined to judge)0.0580.008

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.

Opus teacher head0.441
GPT teacher head0.603
Teacher spread0.162 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreProtocol

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".

Quick stats

Citations3
Published2023
Admission routes1
Has abstractyes

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