U.S. Citizens Who Graduated from Medical Schools Outside the United States and Canada and Received Certification from the Educational Commission for Foreign Medical Graduates, 1983???2002
Bibliographic record
Abstract
PURPOSE: To provide a descriptive overview of international medical school graduates (IMGs), U.S. and non-U.S. citizens, who obtained their medical degrees outside of the United States and Canada, with a focus on where U.S. citizens received their medical education and how this choice has changed over time. METHOD: The study group included all IMGs (n = 143,926) certified by the Educational Commission for Foreign Medical Graduates (ECFMG) from 1983-2002. Descriptive statistics were calculated, including historical certification rates for non-U.S. citizen and U.S. citizen IMGs. For IMGs who were U.S. citizens (n = 18,762), the data were summarized by medical school and country of medical school. RESULTS: U.S. citizens who attended medical schools abroad were more likely to attend schools in Central America and the Caribbean than in any other geographic region. There was a steady decrease in the number of U.S. citizens graduating from European medical schools. Conversely, the number graduating from medical schools in India and Israel rose. Over the period studied, the regions of Africa, Oceania, and South America graduated relatively few U.S. citizens. CONCLUSIONS: From 1983-2002, U.S. citizens graduated from medical schools in Central America and the Caribbean more than any other geographic region. Studying the characteristics of medical schools in this region and their similarities to U.S. medical schools, such as a four-year curriculum, may explain why U.S. citizens are attracted to this region in large numbers. Additional studies focusing on the characteristics of medical schools that train IMGs, the performance of the graduates, and their posttraining practice patterns are warranted.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".