Did the COVID-19 pandemic cause family physicians to stop practice? Results from Ontario, Canada
Bibliographic record
Abstract
Context: The COVID-19 pandemic forced family practices to change how they delivered care. Practices needed to implement and pay for personal protective equipment, enhanced infection control practices, and infrastructure to adopt virtual care. At the same time, fee-for-service practices experienced a decrease in revenue during periods when non-essential care was discouraged. Objective: The aim of this study was to understand changes in family physician practice patterns and whether more family physicians stopped working during the COVID-19 pandemic compared to previous years. Study Design: Cross-sectional study comparing 1) practice patterns during COVID-19 (March 11th to September 29th, 2020) with the same period in 2019 and 2) the proportion of physicians who stopped practice between March 11 and September 29 in each year from 2010 to 2020. Setting or Dataset: Linked administrative data from Ontario, Canada that includes physician billings, physician age, sex, rurality of practice, primary care practice model, and panel size. Outcome Measures: Office, virtual, home, and total visits for each physician during March – September 2020 compared to the same period in 2019. The proportion of family physicians who stopped work entirely between March and September in each year from 2010 to 2020. Population studied: Family physicians in Ontario with at least 50 days of billing activity in the year prior Results: We analyzed data for 12,247 physicians who were actively practicing in 2019. Mean total visits dropped from 2061 (standard deviation, SD: 1,795) in 2019 to 1703 (SD: 1,674) in 2020 with 66% of visits being virtual in 2020. Between March and September 2020, 3.0% of physicians (N = 385) stopping working entirely. Compared to all study physicians, a higher portion of physicians who stopped work in 2020 were age 75 or over (13.0% vs. 3.7%), practicing in an urban area (51% vs 49%), practicing fee-for-service (38% vs 25%), and have a panel size under 500 patients (40% vs 16%). Between 2010 and 2019, an average of 1.6% of physicians stopped working entirely between March and September in the given year 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 stopped working had smaller patient panels. Further research is needed to understand the impact of COVID-19 on primary care attachment.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".