Prevalence of International Medical Graduates in Integrated Plastic Surgery Programs
Bibliographic record
Abstract
International medical graduates (IMGs) are physicians who did not attend medical school in the USA or Canada. IMGs comprise nearly one-quarter of the physician workforce and play a vital role in health care. Here, we aimed to identify the prevalence of IMGs in integrated programs and evaluate factors that influence their success in the residency match. Methods: The annual match reports from 2010 to 2020 were retrieved and summarized. Electronic surveys for program directors and program coordinators were distributed to US integrated plastic surgery programs. Each program's website was appraised for information regarding the eligibility of IMGs. Websites were also used to identify the number of IMG residents. Results: The number of applicants who matched into integrated programs ranged from 69 to 180 per year, of which US applicants comprised 61-165. US IMGs filled one to three positions per year, whereas non-US IMGs filled two to seven. Although 48% of programs have matched non-citizen IMGs and 79% have not encountered difficulties during the visa process, 67% of coordinators reported that the onboarding process is more challenging for IMGs. There are no IMGs in 52% of programs, and most institutions offer information on their website regarding visa sponsorship. Conclusion: IMGs make up less than 10% of filled positions per cycle. Although most programs accept IMGs, a small number matriculate. This may be explained by the competitiveness of integrated programs and the volume of IMG applications. Further research is needed to identify contributing factors of low IMG representation in plastic surgery programs.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".