Abstract 626: Black/African American participation in cancer clinical trials: Healthcare professional perspectives
Bibliographic record
Abstract
Abstract Purpose: The purpose of this community-based participatory research (CBPR) was to identify barriers and facilitators to Black/African American engagement and inclusion in cancer clinical trials in the Detroit Metropolitan Area. This presentation focuses on data from healthcare professionals at Henry Ford Cancer Institute. Design: The CBPR approach included the establishment of a Steering Committee composed of researchers, health professionals, and representatives of eight diverse organizations representing African American, African Caribbean, and Ghanaian communities. A qualitative methodology was employed to obtain contextual and experiential data. Fourteen healthcare professionals participated in individual interviews including physicians, surgeons, research nurses and navigators, clinical research coordinators, and cancer trial investigators. Interview guides were developed based on an extensive literature review. Interviews were audio recorded and transcribed. Data were coded and searches were conducted to identify themes within the coded data. Outcomes: Data were categorized into six constructs: 1. disparities; 2. trust; 3. advantages and disadvantages to trial participation; 4. clinical and system challenges for physicians’ trial engagement; 5. trial participant selection criteria; and 6. clinical trial recruitment protocols. Themes within disparities include inequitable access to healthcare and patient selection bias. Patient and community trust and distrust were discussed within historical and experiential abuses and neglect in Black/African American communities. Physicians perceived trial advantages to include access to new therapies and patient-internal rewards, e.g., contributing to wellbeing of future generations. Disadvantages included participants’ financial, social, and psychological costs. Physician challenges included awareness of trials, time allocation, and systemic expectations of output. Selection criteria themes were focused on the internal procedures required to identify eligible patients and the barriers associated with trial sponsor requirements. Recruitment barriers included complex consenting procedures and resources to support informed patient decision-making. Study respondents provided recommendations to address barriers for both patients and healthcare providers. Conclusions: Increasing diversity in clinical trials requires understanding barriers and facilitators at multiple levels including communities, patients and their families, healthcare professionals, health system leadership, and trial sponsors. Recognizing the experiences and perceptions of healthcare professionals is one step toward addressing the complex social environments in which cancer clinical trials reside. Recommendations from this study are being piloted. Citation Format: Linda Kaljee, Sylvester Antwi, Doreen Dankerlui, Donna Harris, Barbara Israel, Denise White-Perkins, Valerie Ofori Aboah, Livingstone Aduse-Poku, Harriet Larrious-Lartey, Barbara Brush, Chris Coombe, La'Toshia Patman, Nayomi Cawthorne, Sophia Chue, Zachary Rowe, Cassandra Mills, Kurt Fernando, Gwendolyn Daniels, Eleanor Walker, Evelyn M. Jiagge. Black/African American participation in cancer clinical trials: Healthcare professional perspectives [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 626.
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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 | 0.030 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".