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Record W4297999306 · doi:10.2196/36718

Development of an HIV Prevention Intervention for African American Young Men Who Have Sex With Men (Y2Prevent): Study Protocol

2022· article· en· W4297999306 on OpenAlexvenueno aff
Danny Azucar, Marco A. Hidalgo, Deja Wright, Lindsay Slay, Michele D. Kipke

Bibliographic record

VenueJMIR Research Protocols · 2022
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsnot available
FundersNational Institute on Drug AbuseNational Institutes of Health
KeywordsMen who have sex with menPsychological interventionPre-exposure prophylaxisReferralIntervention (counseling)MedicineVulnerability (computing)Reproductive healthSyndemicGerontologyPsychologyHuman immunodeficiency virus (HIV)Family medicinePopulationPsychiatryEnvironmental healthSyphilis

Abstract

fetched live from OpenAlex

BACKGROUND: African American young men who have sex with men (YMSM) possess many intersecting identities that may increase their vulnerability to social stigmatization and discrimination, which yields a negative influence on their well-being and behaviors. These experiences often manifest as increased general and sexual risk-taking behaviors that place this particular group at an increased risk for HIV. This scenario is exacerbated by the lack of HIV prevention interventions specifically designed for African American YMSM. OBJECTIVE: In this paper, we discuss the development of research designed to refine, pilot, and evaluate the feasibility, acceptability, and preliminary efficacy of a behavioral intervention designed to build resilience and reduce substance use and HIV risk behaviors among African American YMSM. The overarching aim of this research, funded by the National Institutes of Health, is to further refine and pilot test an intervention called Young Men's Adult Identity Monitoring (YM-AIM). YM-AIM is a theory-driven, group-level intervention designed to help African American YMSM develop a healthy vision for their future (or possible future self) by defining a set of short-term and long-term goals in the areas of education, health, family, and intimate relationships. METHODS: Through partnerships with community members and community-based organizations, we will further strengthen and refine YM-AIM to include 3 new components: biomedical HIV prevention strategies (pre-exposure prophylaxis and postexposure prophylaxis); HIV and sexually transmitted infection (STI) testing and HIV care referral, drug screening, and drug treatment referral; and a youth mentoring component. We will recruit African American YMSM, aged 18 to 24 years, into 2 working groups; each group will consist of 6 to 8 members and will convene on a weekly basis, and each meeting will focus on one specific YM-AIM topic. This feedback will be used to further refine the intervention, which will then be evaluated for its feasibility and acceptability. Intervention outcomes include drug use in the past 30 days and 3 months, alcohol use, condomless sex, number of sex partners, and increasing condom use intention, condom use self-efficacy, HIV and STI testing recency and frequency, and linkage to care. RESULTS: As of June 2022, we completed phase 1 of Y2Prevent and launched phase 2 of Y2Prevent to begin recruitment for working group participants. Phase 3 of Y2Prevent is anticipated to be launched in September and is expected to be completed by the end of this project period in December 2022. CONCLUSIONS: Few youth-focused interventions have sought to help youth identify and develop the skills needed to navigate the social and structural factors that contribute to individual-level engagement in prevention among sexual minority youth. This research seeks to promote young men's adoption and maintenance of HIV-protective behaviors (eg, safer sex, pre-exposure prophylaxis use, HIV and STI testing, and health care use). INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/36718.

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)0.018
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.081
Threshold uncertainty score0.270

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.013
Meta-epidemiology (narrow)0.0030.005
Meta-epidemiology (broad)0.0050.003
Bibliometrics0.0020.002
Science and technology studies0.0070.002
Scholarly communication0.0030.003
Open science0.0040.004
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0810.015

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 head0.131
GPT teacher head0.542
Teacher spread0.410 · 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 designNot applicable
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
Published2022
Admission routes1
Has abstractyes

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