Measurement of factors influencing the participation of patients with prostate cancer in clinical trials: a Canadian perspective
Bibliographic record
Abstract
OBJECTIVE: To identify factors that patients with prostate cancer believe to be important determinants in their decisions about future enrolment in clinical trials. PATIENTS AND METHODS: In all, 122 patients (within 5 years of a diagnosis of prostate cancer) who had never been asked to participate in a clinical trial were asked to complete a 30-item measure of 'Factors Influencing Participation' in clinical trials. RESULTS: Factor analysis showed that variables influencing participation can be grouped into three areas: acceptability (e.g. recruitment process and altruistic beliefs); awareness (e.g. impact on quality of life, survivorship and randomization process); and accessibility (e.g. costs to patient, influence of family, age, time and need for extra tests). Awareness items were rated significantly more important by patients with T1 or T2 disease (P = 0.002). Patients who had not made a treatment decision also rated awareness (P = 0.05) and acceptability (P = 0.04) items higher. Patients with less than a university education identified access items as more important (P = 0.03). Helping future patients with prostate cancer, the impact of the study protocol on survival, being fully informed about the study, relationship with specialists, and impact of study on quality of life were identified as the five variables having the most influence on future enrolment. CONCLUSIONS: Men rated items related to awareness and acceptability as being the most important determinants to future enrolment in a clinical trial. Knowledge about what these men believe is important for their future participation in a clinical trial will help researchers to design protocols that address the needs of targeted patient groups.
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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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".