Examination of a Nurse-led Community-based Education and Coaching Intervention for Coronary Heart Disease High-risk Individuals in China
Bibliographic record
Abstract
PURPOSE: Early detection and management of coronary heart disease (CHD) are embedded into many community health service and primary care practices in western countries. The Framingham CHD risk score has been used to predict CHD and mortality for nearly 20 years, and it has predicted CHD event risk accurately in multiethnic populations. The aim of this study was to access the effect of a 6-month community-based intervention on CHD risk in individuals at high risk. METHODS: A randomized controlled trial of individuals with a high 10-year CHD risk were recruited from two communities in China. Individuals in the intervention group (n = 53) received a 3-month group education and a 3-month coaching session. Physical examination and self-report questionnaires were used to collect both pre- and postintervention data on blood pressure, glucose, cholesterol, body mass index, smoking, depression, and health-related quality of life (HRQoL). RESULTS: A total of 102 participants (85.0%) completed the 6-month study. Compared with the usual care group, the intervention group had a 5 mmHg greater reduction in systolic blood pressure (t = 2.01, p = .047), larger declines in glucose (t = -2.49, p = .015), cholesterol (t = -2.44, p = .017), body mass index (t = -2.58, p = .011), and depression (t = -2.05, p = .043), and better reports of HRQoL (t = 3.36, p = .001). No significant group differences in smoking behaviors were reported. CONCLUSION: A 6-month community-based intervention in a CHD high-risk population improved disease-related risk factors, depression, and HRQoL. Results provide preliminary evidence for primary prevention of cardiovascular disease risk in a community high-risk population.
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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 | 0.001 |
| 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".