Financial Disclosures: None reported. Support: None reported.
Bibliographic record
Abstract
accepted March 2, 2015. The US physician workforce includes allopathic physicians, osteopathic physi-cians, and international medical graduates (IMGs), which are grouped based on their medical education. International medical graduates are physicians who received their medical school education outside the United States or Canada.1 They comprise both US citizens (US IMGs) and citizens of foreign countries (non-US IMGs) who have trained abroad, and they are important segments of the physi-cian population. The number of physicians trained in US medical schools has been lagging behind demand for physicians over the past several decades, and a shortage of 125,000 physi-cians is predicted by 2025.2 The demand for physicians has resulted in many US health care institutions turning to international medical graduates (IMGs) to supplement their physician workforce. Today, 1 in 4 physicians3 practicing in the United States is trained at a foreign medical school; consequently, IMGs play a crucial role in our health care system. Until major steps are taken to expand the existing US medical education and training infrastructure, the United States ’ need for overseas medical schools to train physicians is likely to continue. The purpose of the present article is to summarize available data regarding IMGs in training and in practice. These data highlight the gap that IMGs fill in the US health care system.
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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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.375 | 0.080 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".