Increasing the meaningful involvement of women in HIV cure-related research: a qualitative interview study in the United States
Bibliographic record
Abstract
BACKGROUND: Cisgender women represent over half of people living with HIV globally. However, current research efforts toward a cure for HIV focus predominantly on cisgender men. The under-representation of women in HIV cure clinical studies is particularly problematic given data suggesting that sex-dependent phenotypes limit scientific discovery. OBJECTIVE: We aimed to generate considerations to increase the meaningful involvement of women in HIV cure-related research. MATERIALS AND METHODS: We conducted in-depth interviews with biomedical researchers and community members to better understand factors that could increase the meaningful involvement of women in HIV cure clinical trials. Participants were affiliated with academia, industry, community advisory boards, and community-based organizations, and were identified using listings from the AIDS Clinical Trials Group and the Martin Delaney Collaboratories. We used conventional content analysis to analyze the qualitative data. RESULTS: We recruited 27 participants, of whom 11 were biomedical researchers and 16 were community members. Participants included 25 cisgender women, 1 transgender woman, and 1 cisgender man. Key considerations emerged, including the need to ensure that HIV cure studies reflect HIV epidemiologic trends and having accurate representation by sex and gender in HIV cure research. To increase the meaningful involvement of women, recommendations included instituting intentional enrollment goals, frequent and mandatory reporting on enrollment, and incentives for sites to enroll women. Additional themes included the need for agency and self-determination, attention to lived experiences, trauma and healing, and adequate support for women (e.g. logistical, psychosocial, mental, emotional, and physical). Participants noted that women would be willing to participate in HIV cure trials, related procedures (e.g. biopsies), and analytical treatment interruptions. They also expressed a desired for women-centered and holistic clinical trial designs that account for intersectionality. CONCLUSIONS: Our empirical inquiry extends recent calls to action to increase diversity of people involved in HIV cure research. Redressing the under-inclusion of women in HIV cure research is an urgent imperative. The entire field must mobilize and reform to achieve this goal. Meaningfully involving women across the gender spectrum in HIV cure research is needed to ensure that interventions are safe, effective, scalable, and acceptable for all people 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 | 0.038 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.013 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".