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Record W3084906565 · doi:10.2196/18118

Protocol Development for HMU! (HIV Prevention for Methamphetamine Users), a Study of Peer Navigation and Text Messaging to Promote Pre-Exposure Prophylaxis Adherence and Persistence Among People Who Use Methamphetamine: Qualitative Focus Group and Interview Study

2020· article· en· W3084906565 on OpenAlexvenueno aff
Vanessa McMahan, Noah D. Frank, Smitty Buckler, Lauren R. Violette, Jared M. Baeten, Caleb J. Banta‐Green, Ruanne V. Barnabas, Jane M. Simoni, Joanne D. Stekler

Bibliographic record

VenueJMIR Formative Research · 2020
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsnot available
FundersNational Institute of Allergy and Infectious DiseasesNational Institute on Drug AbuseNational Institutes of Health
KeywordsPsychological interventionFocus groupFormative assessmentMen who have sex with menMedicineProtocol (science)MethamphetaminePsychologyQualitative researchPeer educationPeer reviewHuman immunodeficiency virus (HIV)Family medicinePublic healthHealth educationNursingAlternative medicinePsychiatryPolitical science

Abstract

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BACKGROUND: Cisgender men who have sex with men (MSM) and transgender people (TGP) who use methamphetamine are disproportionately impacted by HIV acquisition. Pre-exposure prophylaxis (PrEP) is highly effective at preventing HIV, and interventions that support PrEP persistence and adherence should be evaluated among MSM and TGP who use methamphetamine. OBJECTIVE: We conducted formative work to inform the development of text messaging and peer navigation interventions to support PrEP persistence and adherence among MSM and TGP who use methamphetamine. In this paper, we describe how the findings from these focus groups and interviews were used to refine the study interventions and protocol for the Hit Me Up! study (HMU!; HIV Prevention in Methamphetamine Users). METHODS: Between October 2017 and March 2018, we conducted two focus groups and three in-depth interviews with MSM and TGP who use methamphetamine or who have worked with people who use methamphetamine. During these formative activities, we asked participants about their opinions on the proposed interventions, education and recruitment materials, and study design. We focused on how we could develop peer navigation and text messaging interventions that would be culturally appropriate and acceptable to MSM and TGP who use methamphetamine. Transcripts were reviewed by two authors who performed a retrospective content analysis to describe which specific opinions and recommendations influenced protocol development and the refinement of the interventions. RESULTS: Overall, participants thought that MSM and TGP would be interested in participating in the study, although they expected recruitment and retention to be challenging. Participants thought that the peer navigator should be someone who is nonjudgmental, has experience with people who use methamphetamine, and is patient and flexible. There was consensus that three text messages per day were appropriate, adherence reminders should be straightforward, all messages should be nonjudgmental, and participants should be able to tailor the timing and content of the text messages. These suggestions were incorporated into the study interventions via the hiring and training process and into the development of the text library, platform selection, and customizability of messages. CONCLUSIONS: It is important to include the opinions and insights of populations most impacted by HIV to develop PrEP interventions with the greatest chance of success. Our formative work generated several recommendations that were incorporated into the interventions and protocol development for our ongoing study. TRIAL REGISTRATION: ClinicalTrials.gov NCT03584282; https://clinicaltrials.gov/ct2/show/NCT03584282.

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)0.166
metaresearch head score (Gemma)0.162
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.166
Threshold uncertainty score0.880

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1660.162
Meta-epidemiology (narrow)0.0040.005
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0060.004
Science and technology studies0.0070.004
Scholarly communication0.0040.005
Open science0.0050.005
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0680.014

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 head0.156
GPT teacher head0.478
Teacher spread0.322 · 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 designQualitative
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

Citations9
Published2020
Admission routes1
Has abstractyes

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