Deliver Cardiac Virtual Care: A Primer for Cardiovascular Professionals in Canada
Bibliographic record
Abstract
The COVID-19 pandemic, with its need for distancing, has necessitated the use of virtual care in never-before-seen volumes. This review article aims to provide a primer on virtual care for cardiovascular professionals in Canada. The technology to facilitate remote patient interactions is already available, but barriers exist. Adequate and effective cardiac virtual care must be further developed given the need for rapid evaluation and close ongoing follow-up of patients, as seen in the areas of management of heart failure, cardiac rehabilitation, electrophysiology, and hypertension. Many Canadian organizations have published resources to assist health care providers and patients navigate the unfamiliar virtual care landscape. Although there are concerns surrounding issues such as patient privacy, access to technology, language discrepancies, and billing, these deficits provide opportunities for growth by health care organizations and technology companies. The integration of virtual care, home-based devices, and disruptive technologies emphasize the trend toward virtualization of health care, with the potential for greater personalization of health care interactions and continuity of care. Funding models were rapidly developed at the beginning of the COVID-19 pandemic, and although some provinces have deemed these changes as permanent, the status from other provinces remains unknown. The foundations to support virtual care as a key modality for health care delivery in Canada have been built, and further developments may strengthen its viability as a long-term option.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".