Barriers and Facilitators to Adolescent and Young Adult Cancer Trial Enrollment: NCORP Site Perspectives
Bibliographic record
Abstract
Background: Although it is well documented that adolescents and young adults (AYAs) with cancer have low participation in cancer clinical trials (CCTs), the underlying reasons are not well understood. We used the National Cancer Institute Community Oncology Research Program (NCORP) network to identify barriers and facilitators to AYA CCT enrollment, and strategies to improve enrollment at community-based and minority and/or underserved sites. Methods: We performed one-on-one semistructured qualitative interviews with stakeholders (NCORP site principle investigators, NCORP administrators, physicians involved in enrollment, lead clinical research associates or clinical research nurses, nurse navigators, regulatory research associates, patient advocates) in the AYA CCT enrollment process. NCORP sites that included high and low AYA-enrolling affiliate sites and were diverse in geography and department representation (eg, pediatrics, medical oncology) were invited to participate. All interviews were recorded and transcribed. Themes related to barriers and facilitators and strategies to improve enrollment were identified. Results: We conducted 43 interviews across 10 NCORP sites. Eleven barriers and 13 facilitators to AYA enrollment were identified. Main barriers included perceived limited trial availability and eligibility, physician gatekeeping, lack of provider and research staff time, and financial constraints. Main facilitators and strategies to improve AYA enrollment included having a patient screening process, physician endorsement of trials, an "AYA champion" on site, and strong communication between medical and pediatric oncology. Conclusions: Stakeholders identified several opportunities to address barriers contributing to low AYA CCT enrollment at community-based and minority and/or underserved sites. Results of this study will inform development and implementation of targeted interventions to increase AYA CCT enrollment.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".