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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.056 | 0.598 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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; both teacher heads agree on what is shown here.
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".