Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: To assist in workforce planning by updating trends in the characteristics of near-retirement comprehensive family physicians (FPs) and their patients since the COVID-19 pandemic. DESIGN: Population-level serial cross-sectional analysis using linked health administrative datasets. SETTING: Ontario. PARTICIPANTS: The Ontario population as of March 31, 2022 (15,023,570), and the comprehensive FPs to whom they are attached (9375). We compared these populations to pre-pandemic analyses (2008, 2013, and 2019). MAIN OUTCOME MEASURES: Temporal trends in the number, proportion, and characteristics of comprehensive FPs; comprehensive FPs nearing retirement; and patients attached to comprehensive FPs, focusing on FPs nearing retirement. RESULTS: After 2019, growth in the overall comprehensive FP workforce stagnated (2019: 9377; 2022: 9375). For the first time during the study period, in 2022 there was a decline in the number and proportion of early-career physicians (age <35 years) and female physicians comprised the majority (51.5%) of the workforce. An increasing proportion of the workforce is age 65 and older (2008: 10.0%; 2013: 14.4%; 2019: 13.9%; 2022: 15.2%), and correspondingly, an increasing number and proportion of patients are attached to near-retirement FPs. The oldest FP cohort (age ≥70) also increased in number and proportion in 2022. Patients attached to near-retirement FPs were older and had higher levels of chronic conditions compared with patients across the overall FP workforce. Mean roster sizes remained relatively stable and female FPs consistently cared for smaller rosters than male FPs. An increasing proportion of patients had the highest level of complexity, and practices of all FP age groups comprised increasing proportions of those with the highest resource needs. CONCLUSION: Changes to the comprehensive FP workforce since the COVID-19 pandemic, together with increasing patient complexity, raise concerns about the workforce's capacity to absorb patients whose FPs are poised to retire.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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