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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".