Evaluation of a digital patient education programme for Chinese immigrants after a heart attack
Bibliographic record
Abstract
AIMS: To evaluate a self-administered digital education resource for patients after a heart attack (adapted simplified Chinese version of Cardiac College™) on secondary prevention knowledge and health behaviour change outcomes. METHODS AND RESULTS: Chinese immigrants recovering from a heart attack were recruited from cardiac rehabilitation programmes at four metropolitan tertiary hospitals. Participants provided access to Cardiac College™ (adapted simplified Chinese version), a self-learning secondary prevention virtual education resource over 4 weeks. The web-based resources include 9 booklets and 10 pre-recorded video education sessions. Assessments included health literacy, secondary prevention knowledge, self-management behaviours, self-reported physical activity, and a heart-healthy diet. Satisfaction, acceptability, and engagement were also assessed.From 81 patients screened, 67 were recruited, and 64 (95.5%) completed the study. The participants' mean age was 67.2 ± 8.1 years old, 81.2% were males, and the majority had no English proficiency (65.6%). Following the intervention, significant improvements were observed for secondary prevention knowledge overall and in all subdomains, with the most improvement occurring in medical, exercise, and psychological domains (P < 0.001). Dietary and self-management behaviours also improved significantly (P < 0.05). According to participants, the educational materials were engaging (100%), and the content was adequate (68.8%); however, 26.6% found the information overwhelming. Overall, 46.9% were highly satisfied with the resources. CONCLUSION: A self-learning virtual patient-education package improved secondary prevention knowledge and self-care behaviour in Chinese immigrants after a heart attack. The culturally adapted version of Cardiac College™ offers an alternative education model where bilingual staff or translated resources are limited.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".