Implementing Telehealth Technology for South Asian Seniors with Cardiovascular Disease
Bibliographic record
Abstract
The growing burden of chronic disease, including CVD, is escalating both health care spending and morbidity and mortality rates. 1 CVD is the leading cause of morbidity and mortality for men and women, causing 34% of all deaths in Canada. 2 By causing premature death, CVD has a significant impact on the quality of life of those afflicted by the disease, while resulting in negative economic and social consequences, particularly in SA seniors. 3 The risk and burden of heart disease in SA immigrants living in western societies is higher than in non-immigrants. 3 This is a concerning trend considering that SA immigrants are the fastest growing group of Canadian ethnic immigrants. 4 Given the demographic shift in Canada’s immigrant population, HCPs must respond to the increased prevalence of CVD in Canadian SA seniors in order to effectively meet the health care needs of this population. Historically, CVD prevention programs in Canada have focused on non-immigrants, with little research on ethnic differences in cardiovascular health and ethnically tailored prevention strategies. 3, 5 As a result, the primary role of HCPs working with SA seniors is to increase the senior’s awareness of CVD and associated risk factors in hopes that this may provide the incentive to make healthy lifestyle changes. 6 HCPs who assume active roles in reducing the risk of CVD in vulnerable populations can positively influence overall morbidity and mortality rates. 7 CVD is growing out of proportion in the SA seniors’ population due to a lack of knowledge and awareness in self-care management strategies, resulting in poor lifestyle choices. This results in a significant increase in modifiable risk factors, such as blood pressure and cholesterol. In order to better manage their disease and prevent rapid deterioration of their health, the SA senior population requires prompt, quality health information. The current health care delivery model is inefficient as seniors struggle with barriers such as accessibility and timely interventions. Hence, telehealth is a technology solution to reduce the risk of CVD among the seniors. It is the delivery of health care services to clients, such as the frail senior with mobility concerns, in the privacy of their home, to maintain or restore their health, improve their independence, and reduce disability or exacerbation of illness.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".