Did the COVID-19 pandemic result in more family physicians stopping practice? Results from Ontario, Canada
Bibliographic record
Abstract
Abstract Purpose To understand changes in family physician practice patterns and whether more family physicians stopped working during the COVID-19 pandemic compared to previous years. Methods We analyzed administrative data from Ontario, Canada two ways: cross-sectional and longitudinal. First, we identified the percentage and characteristics of all family physicians who had a minimum of 50 billing days in 2019 but no billings during the first six months of the pandemic. Second, for each year from 2010 to 2020, we calculated the percentage of physicians who billed for services in the first quarter of the calendar year but submitted no bills between April and September of the given year. Results We found 3.1% of physicians working in 2019 (N=385/12,247) reported no billings in the first six months of the pandemic. Compared with other family physicians, a higher portion were age 75 or older (13.0% vs. 3.4%, p<0.001), had fee-for-service reimbursement (38% vs 25%, p<0.001), and had a panel size under 500 patients (40% vs 25%, p<0.001). Between 2010 and 2019, an average of 1.6% of physicians who practiced in the first quarter had no billings in each of the second and third quarters of the calendar year compared to 3.0% in 2020 (p<0.001). Conclusions Approximately twice as many family physicians stopped work in Ontario, Canada during COVID-19 compared to previous years, but the absolute number was small and those who did had smaller patient panels. More research is needed to understand the impact on primary care attachment and access to 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".