The Chinese version of the Coronary Artery Disease Education Questionnaire-II (CADEQ-II): translation and validation
Bibliographic record
Abstract
OBJECTIVES: It is necessary to assess disease-related knowledge in patients with coronary artery disease (CAD) for tailored patient education; however there is a lack of a well-validated measurement in China. The objective of this study was to translate and validate a Chinese version of the Coronary Artery Disease Education Questionnaire-II (CADEQ-II). METHODS: The Chinese version of CADEQ-II was translated and culturally adapted. Then, it was tested for psychometric properties through a convenient sampling. Content validity was examined based on a panel of five experts. The item difficulty index and item discrimination index were calculated to assess the item difficulty and item discrimination. Internal consistency reliability was measured with the Cronbach's alpha coefficient. Criterion-related validity was established through comparing scores in patients with different education levels. Construct validity was assessed through confirmatory factor analysis (CFA). RESULTS: The Chinese version of the CADEQ-II was finalized after deleting three items and modifying two items from the original version. Three hundred and sixteen participants completed the whole questionnaire. Content validity index of the whole questionnaire was 0.87. The Cronbach's alpha coefficient of the overall questionnaire was 0.907. The significant difference of the knowledge scores among patients with different education levels supported criterion-related validity. CFA confirmed the proposed four-factorial structure of the questionnaire. CONCLUSION: The Chinese version of CADEQ-II had an acceptable reliability and validity among Chinese patients with CAD. It could be used to develop individualized health education for Chinese patients with CAD. Also, it could serve as a suitable outcome measurement to evaluate the effectiveness of education interventions related to CAD.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".