Addressing the Surgical Shortage: Revisiting Residency Training Requirements for International Medical Graduates
Bibliographic record
Abstract
The American Association of Medical Colleges projects a shortage of between 19,800 and 29,000 physicians in the surgical specialties by 2030. General surgery projects the greatest shortfall among surgical specialties, in part because of high rates of graduating residents pursuing fellowships. International medical graduates, many of whom trained in the US, have become a major part of the physician workforce in other specialties, but represent a small part of general surgeons in practice and trainees in general surgery residency programs. We review the evidence for a surgical workforce shortage in detail, discuss the role of International Medical Graduates (IMGs) in US health care, and propose a process by which foreign-trained surgeons can enter the US surgical workforce without having to repeat training in a US residency program. Such a program existed in the US the late 1960s and early 1970s and continues to exist in Canada, among other places; our proposal is modernized to reflect current US health care needs. IMGs offer ethnic, linguistic and cultural diversity that stands to benefit ethnic minorities and refugee communities while adding diversity to the surgical workforce and to surgical education. Once appropriately certified and credentialed, the IMG surgeon can work in a shortage area in exchange for a path toward permanent residency. We believe the surgical workforce will benefit in many important ways from expansion of skilled international medical graduates, just as it has in many other medical specialties.
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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 | 0.017 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".