Barriers and facilitators to enrollment in cancer clinical trials
Bibliographic record
Abstract
BACKGROUND: The literature continues to report low rates of accrual to cancer clinical trials. Previous studies have examined principally physician-related or patient-related barriers. Clinical research associates (CRAs) have a unique perspective on enrollment that has been explored very little. This study sought the views of CRAs on barriers and facilitators to accrual. METHODS: Focus groups were held at six of eight tertiary cancer centers in Ontario, Canada. Audiotapes of sessions were transcribed and subjected to content analysis by two of the authors. Emergent themes were identified. These themes are illustrated by representative quotes taken from the transcripts. RESULTS: Factors that acted as barriers or facilitators were classified into physician-related, patient-related, or system-related factors. CRAs identified physician attitudes regarding patient participation as the principal physician-related barrier. Barriers, facilitators, and modifying factors that were related to patient involvement were discussed by CRAs. Patients seemed more knowledgeable about trials than in the past and were willing to participate. System factors were considered to have the greatest impact on the ability to accrue. CRAs identified increasing trial and pharmaceutical demands coupled with tight trial time lines. Time was seen as a diminishing resource. Greater demands not only affect specific clinical trial accrual but also affect general support for trials in the cancer center and hospital. CONCLUSIONS: The impact of greater demands in a climate of decreasing health care resources is perceived by CRAs as having a negative affect on accrual. Consequently, the important process of translating potentially beneficial basic research findings into clinical practice is slowed.
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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 | 0.082 | 0.203 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".