Bibliographic record
Abstract
PURPOSE: There is a projected shortage of primary care physicians in the United States, and providers other than U.S medical graduates may be needed to fill the gap. The authors conducted this study to quantify the contribution that Caribbean-educated physicians make to the U.S. primary care workforce. METHOD: Using May 2011 American Medical Association Physician Masterfile and Educational Commission for Foreign Medical Graduates data, the authors identified physicians whose Masterfile records indicated that they provided direct patient care. They classified these physicians according to the type of medical school from which they graduated: graduates of Caribbean medical schools (C-IMGs), graduates of other international medical schools (non-C-IMGs), graduates of U.S. MD-granting medical schools (USMGs), and graduates of U.S. DO-granting medical schools (DOs). They then calculated the frequencies and percentages of self-designated primary care specialties for each physician classification. RESULTS: There were 684,469 physicians in direct patient care categories for whom data were available concerning medical school and self-designated specialty. About one-quarter of these physicians were graduates of international medical schools (C-IMGs: 3.0%, n = 20,333; non-C-IMGs: 20.4%, n = 139,415), and approximately three-quarters were U.S. medical school graduates (USMGs: 70.3%, n = 481,061; DOs: 6.4%, n = 43,660). Overall, C-IMGs had the highest proportion of physicians practicing in primary care specialties (56.7%) compared with non-C-IMGs (42.3%), USMGs (32.9%), and DOs (54.0%). CONCLUSIONS: More than half of Caribbean-educated physicians involved in direct patient care are practicing in primary care specialties, thereby making an important contribution to the U.S. primary care workforce.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".