Long-Acting Injectable Antiretrovirals for HIV Treatment: A Multi-Site Qualitative Study of Clinic-Level Barriers to Implementation in the United States
Bibliographic record
Abstract
Long-acting injectable antiretroviral therapy (LAI ART) has the potential to address adherence obstacles associated with daily oral ART, leading to enhanced treatment uptake, adherence, and viral suppression among people living with HIV (PLWH). Yet, its potential may be limited due to ongoing disparities in availability and accessibility. We need a better understanding of the organizational context surrounding the implementation of LAI ART, and to inform its widespread rollout, we conducted 38 in-depth interviews with medical and social service providers who offer HIV care at private and hospital-based clinics across six US cities. Our findings highlight real-world implementation barriers outside of clinical trial settings. Providers described ongoing and anticipated barriers across three stages of LAI ART implementation: (1) Patient enrollment (challenges registering patients and limited insurance coverage), (2) medication delivery (insufficient personnel and resources), and (3) leadership and management (lack of interprofessional coordination and a lack of programming guidelines). Providers described how these barriers would have a disproportionate impact on under-resourced clinics, potentially exacerbating existing disparities in LAI ART access and adherence. Our findings suggest strategies that clinic leadership, policymakers, and other stakeholders can pursue to promote rapid and equitable LAI ART implementation in clinics across the United States. Resource and staffing investments could support clinics to begin, sustain, and scale up LAI ART delivery; additionally, the establishment of guidelines and tools could facilitate wider adoption of LAI ART across clinical settings. These efforts are crucial to promote resourced, standardized, and equitable implementation of LAI ART and maximize its potential to help end the HIV epidemic.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".