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Record W4400987653 · doi:10.2196/64373

Transgender-Specific Differentiated HIV Service Delivery Models in the South African Public Primary Health Care System (Jabula Uzibone): Protocol for an Implementation Study

2024· article· en· W4400987653 on OpenAlexvenueno aff
Tonia Poteat, Rutendo Bothma, Innocent Maposa, Cheryl Hendrickson, Gesine Meyer‐Rath, Naomi Hill, Audrey Pettifor, John Imrie

Bibliographic record

VenueJMIR Research Protocols · 2024
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsnot available
FundersNational Institute of Mental Health
KeywordsProtocol (science)Service delivery frameworkHuman immunodeficiency virus (HIV)TransgenderPublic healthMedicineTransgender womenPrimary careFamily medicineService modelNursingMen who have sex with menService (business)Alternative medicineSociologyBusinessGender studies

Abstract

fetched live from OpenAlex

BACKGROUND: Almost 60% of transgender people in South Africa are living with HIV. Ending the HIV epidemic will require that transgender people successfully access HIV prevention and treatment. However, transgender people often avoid health services due to facility-based stigma and lack of availability of gender-affirming care. Transgender-specific differentiated service delivery (TG-DSD) may improve engagement and facilitate progress toward HIV elimination. Wits RHI, a renowned South African research institute, established 4 TG-DSD demonstration sites in 2019, with funding from the US Agency for International Development. These sites offer unique opportunities to evaluate the implementation of TG-DSD and test their effectiveness. OBJECTIVE: The Jabula Uzibone study seeks to assess the implementation, effectiveness, and cost of TG-DSD for viral suppression and prevention-effective adherence. METHODS: The Jabula Uzibone study collects baseline and 12-month observation checklists at 8 sites and 6 (12.5%) key informant interviews per site at 4 TG-DSD and 4 standard sites (n=48). We seek to enroll ≥600 transgender clients, 50% at TG-DSD and 50% at standard sites: 67% clients with HIV and 33% clients without HIV per site type. Participants complete interviewer-administered surveys quarterly, and blood is drawn at baseline and 12 months for HIV RNA levels among participants with HIV and tenofovir levels among participants on pre-exposure prophylaxis. A subset of 30 participants per site type will complete in-depth interviews at baseline and 12 months: 15 participants will be living with HIV and 15 participants will be HIV negative. Qualitative analyses will explore aspects of implementation; regression models will compare viral suppression and prevention-effective adherence by site type. Structural equation modeling will test for mediation by stigma and gender affirmation. Microcosting approaches will estimate the cost per service user served and per service user successfully treated at TG-DSD sites relative to standard sites, as well as the budget needed for a broader implementation of TG-DSD. RESULTS: Funded by the US National Institutes of Mental Health in April 2022, the study was approved by the Human Research Ethics Committee at University of Witwatersrand in June 2022 and the Duke University Health System Institutional Review Board in June 2023. Enrollment began in January 2024. As of July 31, 2024, a total of 593 transgender participants have been enrolled: 348 are living with HIV and 245 are HIV negative. We anticipate baseline enrollment will be complete by August 31, 2024, and the final study visit will take place no later than August 2025. CONCLUSIONS: Jabula Uzibone will provide data to inform HIV policies and practices in South Africa and generate the first evidence for implementation of TG-DSD in sub-Saharan Africa. Study findings may inform the use of TG-DSD strategies to increase care engagement and advance global progress toward HIV elimination goals. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/64373.

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)0.051
metaresearch head score (Gemma)0.030
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 score0.084
Threshold uncertainty score0.282

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0510.030
Meta-epidemiology (narrow)0.0030.004
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0030.003
Science and technology studies0.0060.003
Scholarly communication0.0030.003
Open science0.0040.004
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0840.012

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 head0.456
GPT teacher head0.577
Teacher spread0.121 · 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

Citations4
Published2024
Admission routes1
Has abstractyes

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