Shifts in office and virtual primary care during the early COVID-19 pandemic in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Globally, primary care changed dramatically as a result of the coronavirus disease 2019 (COVID-19) pandemic. We aimed to understand the degree to which office and virtual primary care changed, and for which patients and physicians, during the initial months of the pandemic in Ontario, Canada. METHODS: This population-based study compared comprehensive, linked primary care physician billing data from Jan. 1 to July 28, 2020, with the same period in 2019. We identified Ontario residents with at least 1 office or virtual (telephone or video) visit during the study period. We compared trends in total physician visits, office visits and virtual visits before COVID-19 with trends after pandemic-related public health measures changed the delivery of care, according to various patient and physician characteristics. We used interrupted time series analysis to compare trends in the early and later halves of the COVID-19 period. RESULTS: Compared with 2019, total primary care visits between March and July 2020 decreased by 28.0%, from 7.66 to 5.51 per 1000 people/day. The smallest declines were among patients with the highest expected health care use (8.3%), those who could not be attributed to a primary care physician (10.2%), and older adults (19.1%). In contrast, total visits in rural areas increased by 6.4%. Office visits declined by 79.1% and virtual care increased 56-fold, comprising 71.1% of primary care physician visits. The lowest uptake of virtual care was among children (57.6%), rural residents (60.6%) and physicians with panels of ≥ 2500 patients (66.0%). INTERPRETATION: Primary care in Ontario saw large shifts from office to virtual care over the first 4 months of the COVID-19 pandemic. Total visits declined least among those with higher health care needs. The determinants and consequences of these major shifts in care require further study.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".