Virtual care use before and during the COVID-19 pandemic: a repeated cross-sectional study
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic is thought to have increased use of virtual care, but population-based studies are lacking. We aimed to assess the uptake of virtual care during the COVID-19 pandemic using comprehensive population-based data from Ontario. METHODS: This was a repeated cross-sectional study design. We used administrative data to evaluate changes in in-person and virtual visits among all residents of Ontario before (2012-2019) and during (January-August 2020) the COVID-19 pandemic. We included all patients who had an ambulatory care visit in Ontario. We excluded claims for patients who were not Ontario residents or had an invalid or missing health card number. We compared monthly or quarterly virtual care use across age groups, neighbourhood income quintiles and chronic disease subgroups. We also examined physician characteristics that may have been associated with virtual care use. RESULTS: Among all residents of Ontario (population 14.6 million), virtual care increased from 1.6% of total ambulatory visits in the second quarter of 2019 to 70.6% in the second quarter of 2020. The proportion of physicians who provided 1 or more virtual visits per year increased from 7.0% in the second quarter of 2019 to 85.9% in the second quarter of 2020. The proportion of Ontarians who had a virtual visit increased from 1.3% in 2019 to 29.2% in 2020. Older patients were the highest users of virtual care. The proportion of total virtual visits that were provided to patients residing in rural areas (v. urban areas) declined significantly between 2012 and 2020, reflecting a shift in virtual care to a service increasingly used in urban centres. The rates of virtual care use increased similarly across all conditions and across all income quintiles. INTERPRETATION: Our findings show that Ontario's approach to virtual care led to broad adoption across all provider groups, patient age, types of chronic diseases and neighborhood income. These findings have policy implications, including use of virtual care billing codes, for the ongoing use of virtual care during the second wave of the pandemic and beyond.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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