Family physician practice patterns during COVID-19 and future intentions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it