Construction and Validation of a Questionnaire about Heart Failure Patients' Knowledge of Their Disease
Bibliographic record
Abstract
BACKGROUND: The lack of tools to measure heart failure patients' knowledge about their syndrome when participating in rehabilitation programs demonstrates the need for specific recommendations regarding the amount or content of information required. OBJECTIVES: To develop and validate a questionnaire to assess heart failure patients' knowledge about their syndrome when participating in cardiac rehabilitation programs. METHODS: The tool was developed based on the Coronary Artery Disease Education Questionnaire and applied to 96 patients with heart failure, with a mean age of 60.22 ± 11.6 years, 64% being men. Reproducibility was obtained via the intraclass correlation coefficient, using the test-retest method. Internal consistency was assessed by use of Cronbach's alpha, and construct validity, by use of exploratory factor analysis. RESULTS: The final version of the tool had 19 questions arranged in ten areas of importance for patient education. The proposed questionnaire had a clarity index of 8.94 ± 0.83. The intraclass correlation coefficient was 0.856, and Cronbach's alpha, 0.749. Factor analysis revealed five factors associated with the knowledge areas. Comparing the final scores with the characteristics of the population evidenced that low educational level and low income are significantly associated with low levels of knowledge. CONCLUSION: The instrument has satisfactory clarity and validity indices, and can be used to assess the heart failure patients' knowledge about their syndrome when participating in cardiac rehabilitation programs.
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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 | 0.016 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".