Association between virtual primary care and emergency department use during the first year of the COVID-19 pandemic in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Uptake of virtual care increased substantially during the first year of the COVID-19 pandemic. The aim of this study was to evaluate whether a shift from in-person to virtual visits by primary care physicians was associated with increased use of emergency departments among their enrolled patients. METHODS: We conducted an observational study of monthly virtual visits and emergency department visits from Apr. 1, 2020, to Mar. 31, 2021, using administrative data from Ontario, Canada. We used multivariable regression analysis to estimate the association between the proportion of a physician's visits that were delivered virtually and the number of emergency department visits among their enrolled patients. RESULTS: The proportion of virtual visits was higher among female, younger and urban physicians, and the number of emergency department visits was lower among patients of female and urban physicians. In an unadjusted analysis, a 1% increase in a physician's proportion of virtual visits was found to be associated with 11.0 (95% confidence interval [CI] 10.1-11.8) fewer emergency department visits per 1000 rostered patients. After controlling for covariates, we observed no statistically significant change in emergency department visits per 1% increase in the proportion of virtual visits (0.2, 95% CI -0.5 to 0.9). INTERPRETATION: We did not find evidence that patients substituted emergency department visits in the context of decreased availability of in-person care with their family physician during the first year of the COVID-19 pandemic. Future research should focus on the long-term impact of virtual care on access and quality of patient care.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".