Translation, cultural adaptation and psychometric validation of the Arabic short version of the coronary artery disease education questionnaire (CADE-Q SV) in Saudi Arabia
Bibliographic record
Abstract
Objective: To translate, culturally adapt, and psychometrically validate the Arabic Coronary Artery Disease Education Questionnaire Short Version (CADE-Q SV). Methods: The CADE-Q SV was translated to Arabic by two independent translators, followed by back-translation. Then, an expert panel of 10 healthcare providers and 10 patients reviewed the survey and provided input for content validity (CV) and clarity of items. For the psychometric analysis, 202 cardiac patients from Saudi Arabia completed the questionnaire, of which factor structure, internal consistency, construct, and criterion validity were assessed. Results: Items were translated, and CV was confirmed. Items were rated based on relevance and understandability. The scale was finalized after changes in 5 items. Confirmatory factor analysis revealed 5 factors, all internally consistent: medical condition, risk factors, exercise, nutrition, and psychosocial health. Overall alpha was 0.84. Construct validity was established by significant associations between scores and occupation, educational level, family income, having a diagnosis of acute coronary syndrome or valve disorders and with a history of valve repair or replacement a coronary artery bypass graft procedure. Scores were significantly higher for those that participated in cardiac rehabilitation, confirming criterion validity. Conclusions: Results from this study confirm the validity and reliability of the CADE-Q SV in Arabic-speaking patients. Innovation: The CADE-Q SV can be used as a knowledge measurement to support clinical work and development of education intervention for Arabic patients.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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; 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".