Development and validation of an English version of the Coronary Artery Disease Education Questionnaire (CADE‐Q)
Bibliographic record
Abstract
BACKGROUND: The Coronary Artery Disease Education Questionnaire (CADE-Q) is a validated specific tool used to assess the knowledge and educate coronary patients in cardiac rehabilitation on aspects related to coronary artery disease (CAD). The aim of this study is to translate, cross-culturally adapt and validate from Portuguese to English the Coronary Artery Disease Education Questionnaire (CADE-Q). METHODS: Two independent translations were performed by qualified translators. After back-translation, both versions were reviewed by a committee of experts. A final English version was tested in a pilot study. For the psychometric validation, the tool was administered to 200 Canadian coronary patients enrolled in cardiac rehabilitation (CR). The internal consistency was assessed using Cronbach's alpha, the test-retest reliability using intraclass correlation coefficient (ICC), and the construct validity through factor analysis. Criterion validity of CADE-Q was assessed with regard to patients' characteristics. RESULTS: Eleven of 19 questions were modified and culturally adapted in the English version. Cronbach's alpha was 0.809 and ICC was 0.846. Factor analysis revealed five factors, all internally consistent and well defined by the items. Criterion validity was supported by significant differences in mean scores by family income (p = 0.02) and educational level (p < 0.001). CONCLUSION: The English version of the CADE-Q was demonstrated to have adequate reliability and validity, supporting its use in further studies.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".