Factors that influence the recruitment of patients to Phase III studies in oncology
Bibliographic record
Abstract
BACKGROUND: The multiple determinants of a patient's decision to enter into a clinical trial have been explored largely from the perspectives of patients and their physicians. Little research has involved clinical research associates (CRAs) formally, despite their central role in the process of recruitment. The current study was initiated to explore the factors that influence the decision of patients with cancer regarding clinical trial entry, specifically from the perspective of the CRA. METHODS: Two focus groups of CRAs from the Hamilton Regional Cancer Center were organized. A skilled facilitator guided both groups through exploratory and subsequent confirmatory phases of discussions, which were audiotaped for review and coding using a process of consensus employing intercoder triangulation. RESULTS: The two groups identified a number of factors that they believed influenced the recruitment process. Numerous physician and patient factors were reaffirmed, such as the impression of the scientific merit of a study or the sense of personal benefit, respectively. More uniquely, CRAs identified information transfer within the informed consent process as a major aspect of their specialized role. It was believed that full disclosure of information, in terms of both the content and the techniques and styles of delivery, was an important predictor of recruitment success. The groups quickly reached consensus on which factors they believed were the most important overall with respect to influencing study recruitment. CONCLUSIONS: CRAs appear to have a unique role in the process of recruiting patients to active clinical trials. They believe that they have an important influence on recruitment success. Further research to validate this impression is required, because, ultimately, a greater understanding of the relative roles of physician and patient factors and, potentially, CRA factors will be important in the development of ethical and supportive strategies to optimize the recruitment of patients with cancer into randomized clinical trials.
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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 | 0.099 | 0.337 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".