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Record W4414491563 · doi:10.2196/73895

Cultural Adaptation of Together+, a Status-Neutral mHealth Intervention to Improve HIV Prevention and Care for Adolescent and Young Men Who Have Sex With Men in Vietnam: Protocol for a Co-Design Study

2025· article· en· W4414491563 on OpenAlexvenueno aff
Minh X. Nguyen, William C. Miller, Lê Minh Giang, Patrick S. Sullivan

Bibliographic record

VenueJMIR Research Protocols · 2025
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsmHealthProtocol (science)Intervention (counseling)Men who have sex with menHuman immunodeficiency virus (HIV)PopulationAdaptation (eye)Health careSexual behavior

Abstract

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BACKGROUND: Adolescent and young men who have sex with men (AYMSM) are experiencing an ongoing epidemic in Vietnam. HIV testing and preexposure prophylaxis uptake among AYMSM remain low in Vietnam, especially compared to older men. AYMSM living with HIV are also less likely to initiate HIV care. M-Cubed is a status-neutral mobile intervention developed in the United States focusing on HIV prevention and care among men who have sex with men. The app has the potential to significantly contribute to improving the HIV prevention and care continuum for AYMSM. OBJECTIVE: We propose to adapt the M-Cubed app for AYMSM in Vietnam to create Together+, a status-neutral app that promotes HIV testing, preexposure prophylaxis use, and HIV care. METHODS: Adaptation will focus on ensuring that the content, features, and design of the app are culturally relevant to AYMSM in Vietnam. The adaptation process will comprise five phases: (1) adaptation and creation of videos and messages in Vietnamese, (2) in-depth interviews to further inform app adaptation, (3) app prototype development, (4) app theater testing, and (5) beta testing of the adapted app. AYMSM aged between 15 and 19 years and health care staff in Hanoi, Vietnam, will be recruited for in-depth interviews in phase 1 and focus group discussions in phase 4. Qualitative data will be analyzed thematically, and results will be generated after reviewing memos, code reports, and the matrix. To evaluate the feasibility and usability of the Together+ app, we will provide access to 30 AYMSM and encourage them to use the app for 30 days. We will assess observed use and collect quantitative and qualitative data from test users. After 30 days, we will evaluate the usability and feasibility of the Together+ app through in-app analytics as well as online quantitative surveys and individual exit interviews with participants. RESULTS: As of September 2025, we are in the process of adapting the set of 15 videos for Vietnamese AYMSM (phase 1) and analyzing qualitative data from in-depth interviews (phase 2). Data collection for the pilot phase will be completed by August 2026. CONCLUSIONS: Adaptations of proven effective interventions are a promising and efficient way to develop interventions for new service populations but require formal adaptation and evaluation in the new service population. Once culturally adapted for AYMSM in Vietnam, the Together+ app has the potential to significantly contribute to improving the HIV prevention and care continuum for this population. The findings of the adaptation process will document the level of usability and feasibility of the Together+ app and shed light on the perceptions of AYMSM and other stakeholders in Vietnam regarding status-neutral mobile health interventions. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/73895.

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)0.029
metaresearch head score (Gemma)0.027
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 score0.043
Threshold uncertainty score0.152

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.027
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0020.001
Science and technology studies0.0040.002
Scholarly communication0.0020.002
Open science0.0030.003
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0430.007

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 head0.159
GPT teacher head0.554
Teacher spread0.395 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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