Virtual Care With Digital Technologies for Rural Canadians Living With Cardiovascular Disease
Bibliographic record
Abstract
Canada is a wealthy nation with a geographically diverse population, seeking health innovations to better serve patients in accordance with the Canada Health Act. In this country, population and geography converge with social determinants, policy, procurement regulations, and technological advances with the goal to achieve equity in the management and distribution of health care. Rural and remote patients are a vulnerable population; when managing chronic conditions like cardiovascular disease, there is currently inequity to accessing specialist physicians at the recommended frequency-increasing the likelihood of poor health outcomes. Ensuring equitable care for this population is an unrealized priority of several provincial and federal government mandates. Virtual care technology might provide practical, economical, and innovative solutions to remedy this discrepancy. We conducted a scoping review of the literature pertaining to the use of virtual care technologies to monitor patients living in rural areas of Canada with cardiovascular disease. A search strategy was developed to identify the literature specific to this context across 3 bibliographic databases. Two hundred thirty-two unique citations were ultimately assessed for eligibility, of which 37 met the inclusion criteria. In our assessment of these articles, we provide a summary of the interventions studied, their reported effectiveness in reducing adverse events and mortality, the challenges to implementation, and the receptivity of these technologies among patients, providers, and policy-makers. Furthermore, we glean insight into the barriers and opportunities to ensure equitable care for rural patients and conclude that there is an ongoing need for clinical trials on virtual care technologies in this context.
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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 | 0.008 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.014 | 0.022 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".