Cancer clinical trial participation: a qualitative study of Black/African American communities’ and patient/survivors’ recommendations
Bibliographic record
Abstract
BACKGROUND: Black/African Americans experience disproportionate cancer burden and mortality rates. Racial and ethnic variation in cancer burden reflects systemic and health-care inequities, cancer risk factors, and heredity and genomic diversity. Multiple systemic, sociocultural, economic, and individual factors also contribute to disproportionately low Black/African American participation in cancer clinical trials. METHODS: The Participatory Action for Access to Clinical Trials project used a community-based participatory research approach inclusive of Black/African American community-based organizations, Henry Ford Health, and the University of Michigan Urban Research Center. The project aims were to understand Black/African Americans' behavioral intentions to participate in cancer clinical trials and to obtain recommendations for improving participation. Audio-recorded focus group data were transcribed and coded, and searches were conducted to identify themes and subthemes. Representative text was extracted from the transcripts. RESULTS: Six community focus group discussions (70 participants) and 6 Henry Ford Health patient/survivor focus group discussions (29 participants) were completed. General themes related to trial participation were identified, including (1) systemic issues related to racism, health disparities, and trust in government, health systems, and clinical research; (2) firsthand experiences with health care and health systems; (3) perceived and experienced advantages and disadvantages of clinical trial participation; and (4) recruitment procedures and personal decision-making processes. Specific recommendations on how to address barriers were obtained. CONCLUSIONS: Community-based participatory research is effective in bringing communities equitably to the table. To build trust, health systems must provide opportunities for patients and communities to jointly identify factors affecting cancer clinical trial participation, implement recommendations, and address health disparities.
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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 | 0.061 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.022 | 0.013 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".