ADVANCING EQUITY IN LUPUS CLINICAL TRIALS THROUGH COMMUNITY ENGAGEMENT: PERSPECTIVES FROM QUALITATIVE COMMUNITY FEEDBACK SESSIONS WITH CLINICAL TRIAL INVESTIGATORS AND RESEARCH STAFF
Bibliographic record
Abstract
PV083 / #390 Poster Topic: AS11 - Epidemiology and Public Health Background/Purpose Lupus disproportionately affects racial and ethnic minoritized populations, yet there is a significant disparity between those affected and those enrolled in clinical trials. The aim of the present work is to characterize the perspectives, preferences, and unmet needs of key community members to enhance the participation of underrepresented groups in lupus clinical trials. Methods Three Community Feedback Sessions (CFSs) were held over Zoom with experienced investigators and research staff from prominent academic lupus clinical trial centers in North America in January 2024. CFSs were led by trained facilitators utilizing discussion guides developed to gather actionable feedback on: challenges and facilitators for recruiting and enrolling racial and ethnic minority patients into lupus clinical trials; effective communication with diverse patients about clinical trials; and strategies and solutions to promote participation of underrepresented patients in lupus clinical trials. The sessions were recorded, and feedback was summarized to explore key takeaways and recommendations to advance equity in lupus clinical trials. Results Nine investigators and 7 research staff participated in the feedback sessions, representing 14 centers across North America. Key barriers discussed included socioeconomic factors and inadequate consideration of patient’s time and resource constraints within clinical trial designs (Figure 1). Illustrative quotes were selected to portray emergent perspectives, outlined in Figure 2. Building relationships with patients and involving trusted community partners (eg, primary care providers, community health workers, patient advocates) was frequently emphasized as a productive approach to addressing historical and current mistrust in research and medicine. Respondents identified collaborative approaches to enhance continual education and patient engagement to improve communication about clinical trials within and between clinical professionals, patients, and communities. Investigator discussions focused on mentorship and innovative communication methods, while research staff emphasized specific training programs and resources for better understanding lupus and addressing community concerns. All discussions highlighted the necessity of incorporating culturally and linguistically appropriate language and diction when discussing clinical trials, particularly for participants who reported serving large Spanish-speaking patient populations. Figure 1. Perspectives from Investigators (n=9) and Research Staff (n=7) on Barriers and Facilitators to Advance Diversity and Representation in Lupus Clinical Trials Figure 2. Illustrative Quotes from Investigators and Research Staff Perspectives Advancing Diversity and Representation in Lupus Clinical Trials Conclusions Addressing the underrepresentation of diverse populations in lupus clinical trials requires a multifaceted approach. While investigators and research staff shared common concerns and suggestions for advancing equity in lupus clinical trials, each group contributed unique insights and identified unmet needs based on their roles and prior experiences. Engaging and incorporating perspectives from the entire clinical trial research team can help identify comprehensive and actionable strategies to promote equity in lupus clinical trials.
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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 | 0.155 | 0.181 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.023 | 0.020 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.005 | 0.009 |
| 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".