Breaking barriers: Effective interventions amplifying minority participation in cancer clinical trials.
Bibliographic record
Abstract
201 Background: Cancer-related mortality is disproportionately higher in marginalized populations, necessitating adequate representation in cancer clinical trials. Historically, this goal has been challenging, despite recognition of the importance for clinical practice. Moreover, minority recruitment has decreased in oncology clinical trials published from 2003-2016, indicating an urgent need for effective interventions for this issue. Methods: We conducted a systematic review of interventions focused on increasing minority recruitment/retention in cancer clinical trials between 2012-2023, or on improving attitudes of minority groups towards enrollment. We queried Embase and Scopus databases for articles/abstracts that focused on underrepresented ethnic minority enrollment in cancer clinical trials, in English language, were conducted in the US, Canada, Australia, New Zealand, or Western Europe, and were investigating effectiveness of interventions to improve minority enrollment in adult populations. Results: 261 unique articles/abstracts met our search criteria, with an additional 55 duplicates. 117 were removed for being irrelevant or too old, 31 for being reviews, 85 due to not containing an intervention, 5 for not reporting minority data, and 6 for not adequately comparing the strength of interventions. 17 met all inclusion criteria. Several interventions were identified, including targeted patient education (n=7), assigning patient navigators (n=6), community organization engagement (n=3), nested family/community approaches (n=2), and financial assistance (n=1). Targeted education involved video, audio, or interactive mediums. Most studies involved Black (n=12) and Hispanic (n=5) populations, with a smaller number focusing on Asian/Asian Pacific Islanders (n=2), Arabs (n=1), and Indian/South Asians (n=1). Geographically, these studies were notably concentrated in the American South (n=7). The Midwest and Mid-Atlantic regions contributed equally (n=3 each), and fewer studies were from the Northeast and West (n=2 each). Conclusions: Racial minorities continue to be underrepresented in cancer clinical trials, underscoring the need for concerted efforts to promote healthcare equity and enhance these trials’ generalizability. In our systematic review, targeted education, patient navigators, and active community engagement emerged as efficacious strategies, and were the most frequently utilized to bolster minority enrollment. Future studies should prioritize investigating and implementing these interventions, to ensure diverse, representative, and applicable clinical trial participation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.095 | 0.671 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".