Internet-Based Support for Cardiovascular Disease Management
Bibliographic record
Abstract
With significant declines in cardiovascular disease (CVD) mortality, attention has shifted to patient management. Programs designed to manage CVD require the involvement of health professionals for comanagement and patients' self-management. However, these programs are commonly limited to large urban centers, resulting in limited access for rural patients. The use of telehealth potentially overcomes geographical barriers and can improve access to care for patients. The current research explores how an Internet-based platform might facilitate collaboration among healthcare providers comanaging patients and enhance behavioural change in patients. Forty-eight participants were interviewed including: (a) patients (n = 12), (b) physicians (n = 11), (c) nurses (n = 13), and (d) allied health professionals (n = 10). The results were organized and analyzed in three central themes: (1) role of technology for CVD management, (2) challenges to technology adoption, and (3) incentives for technology adoption. Health care providers and patients supported future implementation of Internet-based technology support for CVD management.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".