Tailoring HIV Care for Black Populations: A Pilot Feasibility Prospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Research has shown that integrating community health workers (CHWs) into the formal health care system can improve outcomes for people living with HIV, yet there is limited literature exploring this framework among marginalized minority populations. OBJECTIVE: Herein, we discuss the feasibility of a clinic-embedded CHW strategy to improve antiretroviral therapy adherence among Black people living with HIV in Miami-Dade County, Florida, a designated priority region for the US Department of Health and Human Services' Ending the HIV Epidemic Initiative. METHODS: From December 2022 to September 2023, three CHWs were trained and integrated into the hospital workflow to provide support as members of the clinical team. Ten Black adults with an HIV viral load over 200 copies/mL were enrolled to received 3 months of CHW support focused on navigating the health system and addressing poor social determinants of health. Intervention feasibility was based on 4 criteria: recruitment rate, demographic composition, study fidelity, and qualitative feedback on CHW perceptions. RESULTS: Participants were recruited at a rate of 5.7 participants per month, with the sample evenly distributed between men and women. Retention was moderately strong, with 7 (70%) of the 10 participants attending more than 75% of CHW sessions. Qualitative feedback reflected CHW perceptions on clinical interactions and intervention length. CONCLUSIONS: Outcomes indicate that a clinic-integrated CHW approach is a feasible and acceptable methodology to address adverse social determinants and improve HIV treatment adherence. By offering targeted social and clinical support, CHWs may be a promising solution to achieve sustained viral suppression and care engagement for Black people living with HIV.
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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 | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".