Virtual Care With Digital Technologies for Rural Canadians Living With Cardiovascular Disease
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| 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 it