The Pivotal Role of the International Medical Graduate
Bibliographic record
Abstract
* Abbreviations: AAP — : American Academy of Pediatrics ECFMG — : Educational Commission for Foreign Medical Graduates IMG — : international medical graduate The American Medical Association Physician Masterfile reveals 26 000 physicians who attended medical school outside the United States and Canada and who are not currently in residency, declaring pediatrics as their specialty. According to the Educational Commission for Foreign Medical Graduates (ECFMG), in 2015, 3 countries (India, Canada, and Pakistan) contributed the highest numbers of non–US-born international medical graduates (IMGs) receiving ECFMG certification. However, there were also a sizable number of certificate holders from Latin America, the Middle East, and Africa (Nigeria).1 Non–US-born pediatricians inherently offer a broad cultural, linguistic, and ethnic diversity; as such, they may contribute to the goal in our pediatric specialty to improve workforce diversity and culturally effective health care.2,3 IMGs (including US-born IMGs) comprise >26% of the entire physician workforce in the United States. Forty-one percent of practicing IMGs are in primary care disciplines as defined by the Association of American Medical Colleges.4 They play a vital role in the care of vulnerable populations in both rural and urban underserved areas.2,5 Non–US-born IMGs also constitute a disproportionate number of subspecialists … Address correspondence to Rana Chakraborty, MD, MSc, FRCPCH, FAAP, FPIDS, PhD, Department of Pediatrics and Adolescent Medicine, Mayo Clinic, 200 1st St SW, Rochester, MN 55905. E-mail: chakraborty.rana{at}mayo.edu
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".