“This Is Actually a Really Unique Moment in Time”: Navigating Long-Acting HIV Treatment and HIV Cure Research with Analytical Treatment Interruptions—A Qualitative Interview Study in the United States
Bibliographic record
Abstract
Advancements in long-acting (LA) HIV treatment and cure research with analytical treatment interruptions (ATIs) have generated important scientific and implementation questions. There is an urgent need to examine challenges navigating the evolving HIV treatment and cure research landscape. From August to October 2022, we conducted 26 semistructured interviews with biomedical researchers and community members representing a predominantly woman demographic to explore the complexity of navigating the rapidly evolving HIV therapeutic and HIV cure research landscape. We purposively sampled individuals recruited from the AIDS Clinical Trials Group and the Martin Delaney Collaboratories for HIV Cure Research. Audio files were transcribed verbatim and analyzed through a thematic approach, using an inductive and iterative process. Among 26 participants, 10 were biomedical researchers and 16 community members, including 11 were people with HIV. Three main themes emerged: (1) We are at a pivotal moment in the evolving landscape of HIV therapeutics and LA HIV treatment and HIV cure research should not be siloed but considered together; (2) There are challenges with engagement in HIV cure research and in switching between oral daily antiretroviral treatment and LA formulations and, mainly, the prolonged pharmacokinetic tail of these compounds matched with limited patient education about their impacts; and (3) There are unique opportunities as a result of this evolving therapeutic landscape, including the key role of decision support for people with HIV, centering around patient autonomy, and the need to learn from the lived experiences of people with HIV who choose LA treatment and/or participation in HIV cure research. Despite a bias toward the woman gender, our study identifies key considerations for navigating concurrent LA HIV treatment and HIV cure research with ATIs from both community members and biomedical researchers' perspectives. Achieving optimal HIV control remains a formidable challenge, necessitating robust interdisciplinary collaborations and engagement with key stakeholders.
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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 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.022 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.008 |
| 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".