Telehealth Education And Counselling program to optimise secondary prevention of Heart disease for patients with cardiovascular Disease living in Rural Australia: TEACH-RA study
Bibliographic record
Abstract
Abstract Funding Acknowledgements Type of funding sources: Other. Main funding source(s): SOLVE CHD Collaboration grant Introduction The burden of cardiovascular disease (CVD) is rapidly increasing, however, access to secondary prevention in rural areas is limited. Moreover, rural and remote CVD patients have limited access to primary care and other health services to receive ongoing support and post-discharge follow-up, and this may predispose them to insufficient secondary prevention in terms of a high risk of uncontrolled blood pressure and consequent life-threatening complications including coronary heart disease. Therefore, reform of secondary prevention in rural and remote areas is required to improve reach and access for patients living in such areas, and consequently improve clinical and other health outcomes. Objective To evaluate the feasibility of an innovative, tailored telehealth education and counselling program to optimise secondary prevention of CVD in patients living in rural areas in South Australia. Methods A quasi experimental study will be conducted by recruiting patients from existing database; country access to cardiac health (CATCH) program. Patients with CVD, aged ≥ 18, enrolled into CATCH program and living in rural and remote South Australia. Sample size is calculated using an online tool and is based on the primary endpoint to detect reduced readmission rate in patients with CVD and cardiac events. Patients with acute coronary syndrome/myocardial infarction, revascularisation procedure, stable and unstable angina, post coronary artery bypass grafting, post cardiac valve replacement surgery, and heart failure will be included. The sample size is calculated using online tool with appropriate statistical assumptions, which gives 113. The intervention of this study consists of two components: 1) delivery of a tailored telehealth counselling and education through telephone by a nurse and 2) use of an iPad application for patient education. Primary outcome is readmission for CVD and cardiac events. Secondary outcomes are knowledge of cardiovascular risk factors, lifestyle modification (e.g. smoking, alcohol, diet and physical exercise), medication adherence, health related quality of life and clinical outcomes such as lipid profile and blood pressure. Conclusion TEACH-RA study will test the feasibility of a tailored telehealth education and counselling program in patients with cardiac conditions living in rural and remote areas.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".