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Record W4404066373 · doi:10.2196/52121

Tailoring a Skills-Based Serostatus Disclosure Intervention for Transgender Women in South Africa: Protocol for a Usability and Feasibility Study

2024· article· en· W4404066373 on OpenAlexvenueno aff
Joseph Daniels, L Leigh Ann van der Merwe, Sarah Portle, Cikizwa Bongo, Shiv Nadkarni, Remco P. H. Peters

Bibliographic record

VenueJMIR Research Protocols · 2024
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsnot available
FundersNational Institute of Mental Health
KeywordsUsabilityPreprintProtocol (science)TransgenderIntervention (counseling)PsychologyApplied psychologyMedical educationMedicineComputer scienceNursingWorld Wide WebAlternative medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Transgender women have few interventions to support their HIV prevention and treatment outcomes in South Africa. Further, increased focus should be on intervention development that will reduce HIV transmission within HIV-discordant partnerships, especially for transgender women who navigate gender, sexuality, and relationship stigma. The Speaking Out and Allying Relationships (SOAR) intervention has been developed for sexual minority men to address these outcomes in South Africa. It is a behavioral intervention that is delivered in groups via videoconference to develop coping skills to manage HIV-related stress, assist with disclosure to partners, and establish and maintain safer sex practices with partners. Tailoring SOAR may be feasible for transgender women to support their HIV care while reducing transmission within their relationships. OBJECTIVE: This study aims to (1) adapt SOAR for transgender women and test its usability, then (2) assess its feasibility. METHODS: To achieve aim 1, we will use a human-centered design approach to tailor the existing SOAR intervention for transgender women. Interviews and a survey will be administered to transgender women (N=15) to assess intervention preferences. Findings will be used to tailor content like roleplays, scenarios, and media to align with transgender women's lived experiences navigating HIV and relationships. Afterward, we will conduct a usability test with 7 (47%) of the 15 participants to determine intervention understanding and satisfaction. Participants will be transgender women living with HIV and in a relationship with a man who has unknown HIV status or is HIV-negative. All participants will be recruited using community-based approaches. In aim 2, we will examine SOAR feasibility using a 1-arm pilot study. Transgender women (N=20) will be recruited using aim 1 methods and eligibility criteria, with participants completing feasibility surveys and interviews, as well as behavioral and biomedical assessments. RESULTS: Intervention adaptation began in May 2023 with interviews. Feasibility pilot testing was conducted with 14 transgender women, with study completion in January 2025. CONCLUSIONS: Transgender women need more intervention options that engage their relationships since these can present barriers to HIV treatment outcomes like hindering viral suppression in South Africa. Delivering an existing yet tailored intervention via videoconference expands its reach to transgender women and allows them to engage with others and learn new skills in a secure setting like their homes. SOAR has the potential to improve relationship dynamics and reduce violence, which will in turn enhance HIV treatment and prevention engagement. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/52121.

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)0.031
metaresearch head score (Gemma)0.026
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 score0.058
Threshold uncertainty score0.192

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0310.026
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0030.001
Science and technology studies0.0040.003
Scholarly communication0.0030.003
Open science0.0030.003
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0580.009

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 head0.325
GPT teacher head0.581
Teacher spread0.256 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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