Do faster‑trained physicians fill the gaps? Geographic concentration of emergency medicine physicians with different postgraduate training in Ontario Canada
Bibliographic record
Abstract
BACKGROUND: Emergency departments in underserved areas face chronic staffing challenges. One possible solution is to use physicians who are quicker to train and more pervasive in lieu of more extensively trained physicians. Canada allows for emergency medicine specialization via a 3-year pathway (CCFP(EM)) and a 5-year pathway (FRCPC) which means different geographic areas are exposed to EM physicians with different training lengths. METHODS: We examine Ontario, Canada which has both widespread geographic diversity and emergency providers with these two lengths of postgraduate training. We scrape the College of Physicians and Surgeons of Ontario public registry in 2015 and 2024. We map the geographic distribution of physician types and estimate spatial autocorrelation measures using global and local Morans I to determine whether these physicians became more geographically concentrated. RESULTS: Between 2015 and 2024, the number of CCFP(EM) and FRCPC physicians increased in overall numbers but their unique locations remained stable. Mapping of these locations suggests clustering into urban or suburban areas in the province. CCFP(EM) physicians have become more concentrated over time (Morans I of 0.234 and 0.308 in 2015 and 2024) relative to FRCPC physicians (Morans I of 0.096 and 0.103). CONCLUSION: We find that, from 2015 to 2024, emergency physicians have become more concentrated in the province of Ontario due to CCFP(EM) physicians concentrating around urban areas with academic medical centres. Policies relying on less extensively trained providers to plug staffing gaps may not necessarily be effective in improving equitable access to physicians.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".