Comparing Barriers and Facilitators to Adolescent and Young Adult Clinical Trial Enrollment Across High- and Low-Enrolling Community-Based Clinics
Bibliographic record
Abstract
BACKGROUND: Adolescent and young adult (AYA) patients with cancer are underrepresented on cancer clinical trials (CCTs), and most AYAs are treated in the community setting. Past research has focused on individual academic institutions, but factors impacting enrollment vary across institutions. Therefore, we examined the patterns of barriers and facilitators between high- and low-AYA enrolling community-based clinics to identify targets for intervention. MATERIALS AND METHODS: We conducted 34 semi-structured interviews with stakeholders employed used at National Cancer Institute Community Oncology Research Program (NCORP) affiliate sites ("clinics"). Stakeholders (eg, clinical research associates, patient advocates) were recruited from high- and low-AYA enrolling clinics. We conducted a content analysis and calculated the percentage of stakeholders from each clinic type that reported the barrier or facilitator. A 10% gap between high- and low-enrollers was considered the threshold for differences. RESULTS: Both high- and low-enrollers highlighted insufficient resources as a barrier and the presence of a patient eligibility screening process as a facilitator to AYA enrollment. High-enrolling clinics reported physician gatekeeping as a barrier and the improvement of departmental collaboration as a facilitator. Low-enrollers reported AYAs' uncertainty regarding the CCT process as a barrier and the need for increased physician endorsement of CCTs as a facilitator. CONCLUSIONS: High-enrolling clinics reported more barriers downstream in the enrollment process, such as physician gatekeeping. In contrast, low-enrolling clinics struggled with the earlier steps in the CCT enrollment process, such as identifying eligible trials. These findings highlight the need for multi-level, tailored interventions rather than a "one-size-fits-all" approach to improve AYA enrollment in the community setting.
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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 | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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; a candidate call from one teacher head, 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".