Characterizing Canadian rural general surgeons: trends over time and 10-year replacement needs
Bibliographic record
Abstract
Background: Recruiting residents to practise rurally begins with an accurate characterization of rural surgeons. We sought to identify and analyze demographic trends among rural surgeons in Canada and to predict the rural workforce requirements for the next decade. Methods: In this retrospective observational study, we assessed the demographic and practice characteristics of rural general surgeons in Canada, defined as surgeons working in cities with a population of 100 000 or less. Surgeons were identified using the websites of provincial colleges of physicians and surgeons. Demographic characteristics included year and country of medical degree achievement, fellowship status and primary practice location. We developed a model predicting future rural workforce requirements based on the following assumptions: that the current ratio of rural surgeons to rural patients is adequate, that the rural population will increase by 1.1% annually, that a rural surgeon’s career length is 36 years, and that 85 graduates will enter the workforce annually. Results: Our study sample included 760 rural general surgeons. The majority graduated after 1989 (75%), were Canadian medical graduates (73%) and did not complete a fellowship (82%). There was a significant shift toward rural surgeons being trained in Canada, from 37% of surgeons graduating before 1969 to 91% of those graduating after 2009 (p < 0.001). Modelling predicts 282 rural general surgeons will retire by 2031, with 88 new surgeons needed to account for the population growth. Therefore, we predict a demand for 370 rural surgeons over the next decade, meaning 43% of general surgery graduates will need to enter rural practice. Conclusion: Rural general surgeons in Canada vary widely in their background demographic characteristics. Future opportunities in rural general surgery are projected to increase. Recruitment and training of general surgery graduates to serve Canada’s rural communities remains essential.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".