Validation of the French-Canadian Version of a Short Questionnaire to Assess Knowledge in Cardiac Patients (CADE-Q SV)
Bibliographic record
Abstract
BACKGROUND: The long-term success of cardiac rehabilitation programs rests in part on the patient's ability to maintain health behaviors, which is influence by the patient education received. Therefore, a short and reliable tool to assess patients' knowledge is warranted. The aim of this study was to translate, culturally-adapt and psychometrically validate the French-Canadian version of the Coronary Artery Disease Education Questionnaire Short Version (CADE-Q SV). METHODS: The French CADE-Q SV - translated and culturally-adapted - was reviewed by 3 bilingual experts in cardiovascular disease. This version was then psychometrically tested in 115 CR patients in two Canadian provinces (Québec and New Brunswick). The questionnaire was completed at patients' first CR session and in the end of their 6-month program to assess interpretability. The internal consistency was assessed using Kuder-Richardson-20 (KR-20) and Cronbach's alpha, factor structure using confirmatory factor analysis, and criterion validity regarding level of education and family income. RESULTS: KR-20 was 0.72. Factor analysis revealed 5 factors, all internally consistent. Criterion validity was supported by significant differences in total scores by educational level and family income (p < 0.05). Results showed that increases in knowledge can moderately increase mean steps per day and peakVO2, with an MCID of 3.00. The overall mean was 15.7 ± 2.0. The area with the highest knowledge was risk factors and the lowest was psychosocial risk. CONCLUSION: The French-Canadian CADE-SV was demonstrated to have good validity and reliability.
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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 | 0.010 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".