Development of a mobile phone-based intervention to improve adherence to secondary prevention of coronary heart disease in China
Bibliographic record
Abstract
Coronary heart disease (CHD) is a major disease burden globally and in China, but secondary prevention among CHD patients remains insufficient. Mobile health (mHealth) technology holds promise for improving secondary prevention but few previous studies included both provider-facing and patient-directed measures. We conducted a physician needs assessment survey (n = 59), physician interviews (n = 6), one focus group and a short cellphone message validation survey (n = 14) in Shanghai and Hainan, China. Based on these results, we developed a multifaceted mHealth intervention that includes: (1) a provider-facing bilingual mobile app guiding prescription of evidence-based medications for secondary prevention and (2) a patient-directed short messaging system automatically sending reminders to patients regarding medication adherence and lifestyle changes (4-5 messages per week for 12 weeks). This combined intervention has the potential to improve secondary prevention of CHD and to be adapted to other countries and healthcare conditions.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".