Family physician practice patterns during COVID-19 and future intentions
Bibliographic record
Abstract
<h3>Objective</h3> To determine the extent to which family physicians closed their doors altogether or for in-person visits during the pandemic, their future practice intentions, and related factors. <h3>Design</h3> Cross-sectional survey. <h3>Setting</h3> Six geographic areas in Toronto, Ont, aligned with Ontario Health Team regions. <h3>Participants</h3> Family doctors practising office-based, comprehensive family medicine. <h3>Main outcome measures</h3> Practice operations in January 2021, use of virtual care, and future plans. <h3>Results</h3> Of the 1016 (85.7%) individuals who responded to the survey, 99.7% (1001 of 1004) indicated their practices were open in January 2021, with 94.8% (928 of 979) seeing patients in person and 30.8% (264 of 856) providing in-person care to patients reporting COVID-19 symptoms. Respondents estimated spending 58.2% of clinical care time on telephone visits, 5.8% on video appointments, and 7.5% on e-mail or secure messaging. Among respondents, 17.5% (77 of 439) were planning to close their existing practices in the next 5 years. There were higher proportions of physicians who worked alone in clinics among those who did not see patients in person (27.6% no vs 12.4% yes, <i>P</i><.05), among those who did not see symptomatic patients (15.6% no vs 6.5% yes, <i>P</i><.001), and among those who planned to close their practices in the next 5 years (28.9% yes vs 13.9% no, <i>P</i><.01). <h3>Conclusion</h3> Most family physicians in Toronto were open to in-person care in January 2021, but almost one-fifth were considering closing their practices in the next 5 years. Policy makers need to prepare for a growing family physician shortage and better understand factors that support recruitment and retention.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".