Toward Graduate Medical Education (GME) Accountability
Bibliographic record
Abstract
PURPOSE: Graduate medical education (GME) plays a key role in the U.S. health care workforce, defining its overall size and specialty distribution and influencing physician practice locations. Medicare provides nearly $10 billion annually to support GME and faces growing policy maker interest in creating accountability measures. The purpose of this study was to develop and test candidate GME outcome measures related to physician workforce. METHOD: The authors performed a secondary analysis of data from the American Medical Association Physician Masterfile, National Provider Identifier file, Medicare claims, and National Health Service Corps, measuring the number and percentage of graduates from 2006 to 2008 practicing in high-need specialties and underserved areas aggregated by their U.S. GME program. RESULTS: Average overall primary care production rate was 25.2% for the study period, although this is an overestimate because hospitalists could not be excluded. Of 759 sponsoring institutions, 158 produced no primary care graduates, and 184 produced more than 80%. An average of 37.9% of internal medicine residents were retained in primary care, including hospitalists. Mean general surgery retention was 38.4%. Overall, 4.8% of graduates practiced in rural areas; 198 institutions produced no rural physicians, and 283 institutions produced no Federally Qualified Health Center or Rural Health Clinic physicians. CONCLUSIONS: GME outcomes are measurable for most institutions and training sites. Specialty and geographic locations vary significantly. These findings can inform educators and policy makers during a period of increased calls to align the GME system with national health needs.
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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 | 0.049 | 0.135 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".