A qualitative study of barriers and facilitators to enrollment of adolescents and young adults onto cancer clinical trials at NCI community oncology research program (NCORP) sites.
Bibliographic record
Abstract
e19050 Background: Cancer clinical trials (CCTs) contribute to improving patient survival and quality of life; however, adolescents and young adults (AYAs, 15-39 years old), are underrepresented in CCTs, especially in the community setting. We aimed to identify barriers and facilitators to AYA CCT enrollment in the NCORP. Methods: We conducted 43 one-on-one semi-structured qualitative interviews with key stakeholders involved in the enrollment of AYAs across a diverse group of NCORP primary (n = 5) and affiliate (n = 10) sites. Interviews were conducted remotely by 3 trained interviewers using the Zoom platform. Stakeholders were recruited from high and low AYA enrolling sites (AYA/total site enrollments > 10% and < 3%, respectively). Stakeholders were overall NCORP Site PIs (n = 5), lead NCORP administrators (n = 4), clinical research associates (n = 11), medical and pediatric oncologists involved in the enrollment of AYAs (n = 7), regulatory research associates (n = 5), nurse navigators (n = 6), and patient advocates (n = 5). Interviews were audiotaped and transcribed. Thematic analysis was conducted to identify themes and relate them back to our primary research questions regarding barriers and facilitators to AYA CCT enrollment. Results: Stakeholder views on enrollment barriers centered on 5 main themes: (1) lack of site-level prioritization or discussion of AYA enrollment; (2) limited number of clinical trials for AYAs available nationally, with few trials opened locally; (3) insufficient resources and research staff; (4) concerns about the cost effectiveness of opening AYA trials due to low numbers of eligible patients; and (5) patient misconceptions about CCTs. Stakeholder views on enrollment facilitators centered on 3 main themes: (1) presence of an AYA program focused on increasing enrollment; (2) having a designated site AYA “champion”; and (3) having site leadership identify AYA enrollment as a priority. Stakeholders agreed that incentivizing AYA enrollments via increased reimbursement and/or study credits could potentially lead to increased enrollment. Conclusions: In addition to identifying multiple shared barriers to AYA CCT enrollment, our study also identified possible interventions for enrollment improvement, including designation of AYA “champions”, increased reimbursement for AYA enrollments, and improving AYA’s understanding of CCTs. Further studies are needed to assess the impact of interventions aimed at increasing AYA enrollment across the NCORP.
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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 | 0.017 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".