Knowledge, Attitudes, and Self-efficacy as Predictors of Preparedness for Oncology Clinical Trials
Bibliographic record
Abstract
OBJECTIVE: This study used the Ottawa Decision Support Framework to evaluate a model examining associations between clinical trial knowledge, attitudinal barriers to participating in clinical trials, clinical trial self-efficacy, and clinical trial preparedness among 1256 cancer patients seen for their first outpatient consultation at a cancer center. As an exploratory aim, moderator effects for gender, race/ethnicity, education, and metastatic status on associations in the model were evaluated. METHODS: . Patients completed measures of cancer clinical trial knowledge, attitudinal barriers, self-efficacy, and preparedness. Structural equation modeling (SEM) was conducted to evaluate whether self-efficacy mediated the association between knowledge and barriers with preparedness. RESULTS: . The SEM explained 26% of the variance in cancer clinical trial preparedness. Self-efficacy mediated the associations between attitudinal barriers and preparedness, but self-efficacy did not mediate the knowledge-preparedness relationship. CONCLUSIONS: . Findings partially support the Ottawa Decision Support Framework and suggest that assessing patients' level of self-efficacy may be just as important as evaluating their knowledge and attitudes about cancer 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 | 0.013 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".