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Record W4296717278 · doi:10.2196/41602

Community-Engaged Intervention Mapping for Cardiovascular Disease Prevention in Black and Latinx Sexual Minority Men With HIV in New York City: Protocol for a Web-Based Mixed Methods Study

2022· article· en· W4296717278 on OpenAlexfundvenueno aff
S. Raquel Ramos, Marilyn Fraser, Faven Araya, Hyun Young Kim, Jon Andre Sabio Parrilla, Kalla Maxine Sy, Riya Tongson Nagpal, Marlene Camacho‐Rivera, Mohamed Boutjdir

Bibliographic record

VenueJMIR Research Protocols · 2022
Typearticle
Languageen
FieldMedicine
TopicHIV-related health complications and treatments
Canadian institutionsnot available
FundersNational Heart, Lung, and Blood InstituteNational Institutes of HealthYork UniversityYale University
KeywordsIntervention (counseling)Protocol (science)Human immunodeficiency virus (HIV)Community-based participatory researchSexual minorityGerontologyMedicinePsychologyFamily medicineNursingAlternative medicineSociologySocial psychologyParticipatory action researchSexual orientation

Abstract

fetched live from OpenAlex

BACKGROUND: Approximately every 37 seconds, someone in the United States dies of cardiovascular disease (CVD). It has emerged as an important contributor to morbidity among persons with HIV. Black and Latinx sexual minority men are at higher risk of both HIV and CVD when compared to heterosexual, nonethnic or minority men. Persons with HIV have a 1.5 to 2-times risk of having CVD than do HIV-negative persons. Data suggest that by the year 2030, an estimated 78% of persons with HIV will have CVD. The relationship between HIV and CVD in marginalized populations is not well understood because overall awareness of HIV and CVD as comorbid conditions is low, which further heightens risk. This has created a critically pressing issue affecting underrepresented ethnic and racial populations with HIV and requires immediate efforts to mitigate risk. OBJECTIVE: The purpose of this formative, mixed methods study is to use a community-engaged approach to map a behavioral intervention for CVD prevention in Black and Latinx sexual minority men with HIV in New York City. METHODS: Literature reviews focused on behavioral prevention studies using intervention mapping. In Aim 1, we will use qualitative interviews with HIV program managers and community members to understand facilitators and barriers to CVD prevention, chronic illnesses of concern, and early design elements needed for a web-based CVD prevention intervention. In Aim 2, we will conduct qualitative interviews and administer cross-sectional validated surveys with 30 Black and Latinx sexual minority men with HIV. We will assess illness perceptions of chronic conditions, such as HIV, hypertension, and diabetes. A total of 40 participants (program managers and community members) for Aims 1 and 2 will be enrolled to participate. To develop the protocol, we will follow steps 1 through 3 (needs assessment, change objectives, implementation strategy) of intervention mapping, using mixed methods. RESULTS: The study was approved by New York University Institutional Review Board in February 2021 (IRB-FY2021-4772) and also by the Yale University Institutional Review Board in June 2022 (#2000031577). We anticipate completing data collection on or before December 2022. Early analyses suggested concerns about illnesses outside of HIV and associated comorbid conditions, such as COVID-19 and monkeypox. Additionally, we noted a strong interest in using a web-based platform for CVD prevention education. CONCLUSIONS: Web-based, behavioral, CVD prevention interventions may be promising modalities to closing the cardiovascular health disparities gap in Black and Latinx sexual minority men with HIV by extending the reach of prevention interventions using community-informed approaches and technological modalities that have been underused in this population. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/41602.

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)0.053
metaresearch head score (Gemma)0.045
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 score0.081
Threshold uncertainty score0.279

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0530.045
Meta-epidemiology (narrow)0.0040.004
Meta-epidemiology (broad)0.0050.005
Bibliometrics0.0040.003
Science and technology studies0.0070.003
Scholarly communication0.0040.004
Open science0.0050.005
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.0810.011

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 head0.411
GPT teacher head0.576
Teacher spread0.165 · 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

Citations7
Published2022
Admission routes2
Has abstractyes

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