Trial staff and community member perceptions of barriers and solutions to improving racial and ethnic diversity in clinical trial participation; a mixed method study
Bibliographic record
Abstract
Background: The lack of racial and ethnic diversity in clinical trials leads to skewed findings, limited generalizability, inequitable health outcomes for people of color, and insufficient access to innovative therapies. Our objective was to compare perceptions of barriers to participation in trials for people of color and trial staff to provide tangible solutions for improving diversity among study participants. Methods: This mixed method study utilized semi-structured interviews and surveys to evaluate barriers to participation and solutions to improve racial and ethnic diversity in clinical trials among healthcare system trial staff and community members from the same region. Through thematic analysis via coded transcripts and quantitative analysis via survey data, social support theory constructs were identified to evaluate where perceptions of barriers and solutions overlap and where they diverge. Results: A total of 55 trial staff and 75 community members participated in the study. Trial staff identified logistics and patients' unwillingness to receive additional treatments as perceived barriers to participation, while community members stated lack of information and lack of trust in their care team. Both groups identified hesitance toward research as a prominent barrier. Solutions related to informational support demonstrated the most overlap between groups, while instrumental support showed the most discordance. Conclusion: Solutions for improving racial and ethnic diversity in clinical trial participation are multi-faceted and have various levels of impact. Overlap and discordance of opinions regarding solutions should be further evaluated, and implementation of solutions should be carefully considered.
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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 | 0.073 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".