From 0-50 in Pandemic, and Then Back? A Case Study of Virtual Care in Ontario Pre–COVID-19, During, and Post–COVID-19
Bibliographic record
Abstract
We review the evolution of virtual care (VC) in Ontario. Pre-COVID-19, the primary focus was on patients in remote and underserved areas who went to host sites for care. Ontario's vision pre-pandemic was for a gradual increase in VC by physicians registered with the Ontario Telemedicine Network (OTN), using OTN-approved video technologies; some accommodated patients and doctors wherever they were. Less than 1% of care was virtual pre-pandemic. We discuss how policies that altered access to in-person care (pandemic lockdowns and guidelines to seek and provide care virtually), compensation policy changes (allowing any Ontario physician to be compensated for VC), and policies allowing common technologies not previously allowed (including, importantly, the telephone), drove and enabled a rapid shift to >50% of care being virtual at the start of the pandemic, leveling off to ∼30% over time. We review policy changes in late 2022 and predict these will result in a drop in VC compared with the policies during the pandemic, particularly for walk-in clinic patients, in a province where 2.2-4.6 million people do not have a primary care doctor and presumably use walk-in clinics. This is because, going forward, physicians will be compensated less for telephone care than for in-person or video care for rostered patients, and because compensation will be less still for telephone or video care provided to walk-in patients. Through this case study we develop a visual model of how these key policy and technology factors influence the provision of VC.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".