Virtual care and the influence of a pandemic: Necessary policy shifts to drive digital innovation in healthcare
Bibliographic record
Abstract
The potential for virtual healthcare to improve access to primary care services in Canada has long been a topic of discussion; however, implementation has been slow despite growing interest among the public. Non-essential service lockdowns implemented in 2020 in response to the COVID-19 pandemic catalyzed rapid and widespread uptake of virtual healthcare delivery. It is important to consider how to maintain equitable access to virtual care following the pandemic. We conducted a narrative scoping review to understand barriers related to the sustained adoption of virtual primary care delivery in Canada. Barriers at the system, healthcare provider, and patient levels were related to digital health infrastructure, and the regulatory environment governing virtual care provision and remuneration for healthcare professionals. The article identifies areas where policy shifts by health system leaders could sustain the longer-term availability of Canadian virtual care services.
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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 | 0.019 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.009 |
| 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".