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Record W4392105978 · doi:10.2196/56293

Applying an Evidence-Based Community Organizing Approach to Strengthen HIV Prevention for Cisgender Women in US South: Protocol for a Mixed Methods Study

2024· article· en· W4392105978 on OpenAlexvenueno aff
Anandi N. Sheth, Dázon Dixon Diallo, Celeste Ellison, Deja L Er, Adaora Ntukogu, Kelli A. Komro, Jessica M. Sales

Bibliographic record

VenueJMIR Research Protocols · 2024
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsnot available
FundersNational Institute of Mental Health
KeywordsAtlantaPre-exposure prophylaxisHuman immunodeficiency virus (HIV)PsychologyCommunity mobilizationGerontologyMedicineFamily medicineMen who have sex with menPolitical science

Abstract

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BACKGROUND: Most new HIV diagnoses among cisgender women in the United States occur in the South. HIV pre-exposure prophylaxis (PrEP), a cornerstone of the federal Ending the HIV Epidemic (EHE) initiative, remains underused by cisgender women who may benefit. Awareness and access to PrEP remain low among cisgender women. Moreover, improving PrEP reach among cisgender women requires effectively engaging communities in the development of appropriate and acceptable patient-centered PrEP care approaches to support uptake. In a community-clinic-academic collaboration, this protocol applies an evidence-based community organizing approach (COA) to increase PrEP awareness and reach among cisgender women in Atlanta. OBJECTIVE: The aim of this study is to use and evaluate a COA for engaging community members across 4 Atlanta counties with high-priority EHE designation, to increase PrEP awareness, interest, and connection to PrEP care among cisgender women. METHODS: The COA, consisting of 6 stages, will systematically develop the skills of community members to become leaders and advocates for HIV prevention inclusive of PrEP for cisgender women in their communities. We will use the evidence-based COA to develop and implement a PrEP-specific action plan to create broader community change by raising awareness and interest in PrEP, reducing stigma associated with HIV or PrEP, and connecting women to sexual health clinics providing PrEP services. In the first 4 stages, to prepare for and develop action plans, we will gather data from one-on-one interviews with up to 100 individuals across Atlanta to capture attitudes, motivations, and influences related to women's sexual health with a focus on HIV prevention and PrEP. Informed by the community interviews, we will revise a sexual health curriculum inclusive of PrEP and community-centered engagement. We will then recruit and train community action team members to develop action plans to implement the curriculum during community-located events. In the last 2 stages, we will implement and evaluate COA's effect on PrEP awareness, interest, HIV or PrEP stigma, and connection to PrEP care among cisgender women community members. RESULTS: This project was funded by the National Institutes of Health and approved by the Emory University institutional review board in July 2021. Data collection began in December 2021 and is ongoing. COA stage 1 of the study is complete with 70 participants enrolled. Community events commenced in November 2023, and data collection will be completed by November 2025. Stage 1 qualitative data analysis is complete with results to be published in 2024. Full study results are anticipated to be reported in 2026. CONCLUSIONS: Through a community-clinic-academic collaboration, this protocol proposes to mount a coordinated approach across diverse Atlanta counties to strengthen HIV prevention for cisgender women and to create a sustainable systems approach to move new sexual health innovations more quickly to cisgender women. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/56293.

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)0.090
metaresearch head score (Gemma)0.081
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 score0.090
Threshold uncertainty score0.474

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0900.081
Meta-epidemiology (narrow)0.0050.005
Meta-epidemiology (broad)0.0060.006
Bibliometrics0.0050.005
Science and technology studies0.0070.003
Scholarly communication0.0060.005
Open science0.0050.004
Research integrity0.0070.010
Insufficient payload (model declined to judge)0.0810.013

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 head0.643
GPT teacher head0.651
Teacher spread0.009 · 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

Citations4
Published2024
Admission routes1
Has abstractyes

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