Assessment of Patient Knowledge of Cardiac Rehabilitation: Brazil vs Canada
Bibliographic record
Abstract
BACKGROUND: Much of the relationship between health status and knowledge about health and disease can be attributed to the combined effects of disparate health-related behavior, environmental conditions, and socioeconomic structures as well as contact with and delivery of health care. OBJECTIVE: The aim of this study was to describe and compare knowledge of patients with coronary artery disease (CAD) enrolled in cardiac rehabilitation (CR) programs in Brazil and Canada about CAD-related factors. METHODS: Two samples of 300 Brazilian and 300 Canadian patients enrolled in CR were compared cross-sectionally. Brazilian patients were recruited from 2 CR centers in Southern Brazil, whereas Canadian patients were recruited from 1 CR center in Ontario. Knowledge was assessed using the Coronary Artery Disease Education Questionnaire (CADE-Q), psychometrically validated in Portuguese and English. The data were processed through descriptive statistics, post-hoc and the Student's t-tests. RESULTS: The mean total knowledge score for the whole sample was 41.42 ± 9.3. Canadian respondents had significantly greater mean total knowledge scores than Brazilian respondents. The most highly knowledgeably domain in both samples was physical exercise. In 13 of 19 questions, Canadian respondents reported significantly greater knowledge scores than Brazilian respondents. CONCLUSIONS: Canadian outpatients reported significantly greater knowledge than their Brazilian counterparts. The results also suggest that having a structured educational curriculum in CR programs may contribute to increased patient knowledge, which may ultimately facilitate behavioral changes.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".