Characteristics and Distribution of Graduate Medical Education Training Sites: Are We Missing Opportunities to Meet U.S. Health Workforce Needs?
Bibliographic record
Abstract
PURPOSE: Shortages of generalist physicians in primary care and surgery have been projected. Residency programs that expose trainees to community-based health clinics and rural settings have a greater likelihood of producing physicians who later practice in these environments. The objective of this study was to characterize the distribution of residency training sites in different settings for three high-need specialties-family medicine, internal medicine, and general surgery. METHOD: The authors merged 2012 data from the Accreditation Council for Graduate Medical Education Accreditation Data System and 2010 data from the Centers for Medicare and Medicaid Services hospital cost report to match training sites with descriptive data about those locations. They used chi-square tests to compare the characteristics and distribution of residency programs and training sites in family medicine, internal medicine, and general surgery. RESULTS: The authors identified 1,095 residency programs and 3,373 training sites. The majority of training occurred in private, not-for-profit hospitals. Only 48 (of 1,390; 4%) family medicine training sites and 43 (of 936; 5%) internal medicine training sites were community-based health clinics. Seventy-eight (6%) family medicine sites, 8 (1%) internal medicine sites, and 16 (2%) general surgery sites were located in rural settings. One hundred thirty (14%) internal medicine sites were Department of Veterans Affairs medical facilities compared with 78 (6%) family medicine sites and 94 (9%) general surgery sites (P < .001). CONCLUSIONS: Relatively little training occurs in rural or community-based settings. Expanding training opportunities in these low-access areas could improve physician supply there.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".