Oncologist-Reported Barriers and Facilitators to Offering Cancer Clinical Trials to Their Patients
Bibliographic record
Abstract
NCCN guidelines indicate that cancer clinical trials (CCTs) are the best management for patients with cancer. However, only 5% of patients enroll in them. We examined oncologists' perceived barriers and facilitators to discussing CCTs. This qualitative study was part of the ASCO-ACCC Initiative to Increase Racial and Ethnic Diversity in Clinical Trials. Barriers and facilitators at the system, trial, provider, and patient levels were examined. To achieve triangulation, patient encounters were reviewed using chart-stimulated recall (CSR) methods, thereby obtaining a valid assessment of physician performance. Ten oncology providers participated in this study. Nine were oncologists, and one was a clinical research coordinator; five were female; four were White; three were Asian; and three were Black. Barriers to offering CCTs were a lack of trial availability; ineligibility; a lack of knowledge; assumptions about patient interest, benefits, or harms; patient's disease factors; and negative attitudes. Facilitators of offering CCTs were a physical space to discuss trials; greater trial availability; a systematic approach to offering trials; patient factors; patients seeking trials; a lack of comorbidities; patients being younger in age; patients being aware of, asking about, or hearing of trials from their surgeon; and higher levels of altruism. Many of the cited barriers are addressable with the cited facilitators. A larger study is needed to generalize and validate these findings.
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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 | 0.024 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".