Integration of Gender-Affirming Primary Care and Peer Navigation With HIV Prevention and Treatment Services to Improve the Health of Transgender Women: Protocol for a Prospective Longitudinal Cohort Study
Bibliographic record
Abstract
BACKGROUND: Public health strategies are urgently needed to improve HIV disparities among transgender women, including holistic intervention approaches that address those health needs prioritized by the community. Hormone therapy is the primary method by which many transgender women medically achieve gender affirmation. Peer navigation has been shown to be effective to engage and retain underserved populations living with HIV in stable primary medical care. OBJECTIVE: This study aims to assess the feasibility and acceptability of an integrated innovative HIV service delivery model designed to improve HIV prevention and care by combining gender-affirming primary care and peer navigation with HIV prevention and treatment services. METHODS: A 12-month, nonrandomized, single-arm cohort study was implemented in Lima, Peru, among adult individuals, assigned a male sex at birth, who identified themselves as transgender women, regardless of initiation or completion of medical gender affirmation, and who were unaware of their HIV serostatus or were living with HIV but not engaged in HIV treatment. HIV-negative participants received quarterly HIV testing and were offered to initiate pre-exposure prophylaxis. HIV-positive participants were offered to initiate antiretroviral treatment and underwent quarterly plasma HIV-1 RNA and peripheral CD4+ lymphocyte cell count monitoring. All participants received feminizing hormone therapy and adherence counseling and education on their use. Peer health navigation facilitated retention in care by visiting participants at home, work, or socialization venues, or by contacting them by social media and phone. RESULTS: Patient recruitment started in October 2016 and finished in March 2017. The cohort ended follow-up on March 2018. Data analysis is currently underway. CONCLUSIONS: Innovative and culturally sensitive strategies to improve access to HIV prevention and treatment services for transgender women are vital to curb the burden of HIV epidemic for this key population. Findings of this intervention will inform future policies and research, including evaluation of its efficacy in a randomized controlled trial. TRIAL REGISTRATION: ClinicalTrials.gov NCT03757117; https://clinicaltrials.gov/ct2/show/NCT03757117. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/14091.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.015 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.036 | 0.007 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".