Changes in Primary Care Graduate Medical Education Are Not Correlated With Indicators of Need: Are States Missing an Opportunity to Strengthen Their Primary Care Workforce?
Bibliographic record
Abstract
PURPOSE: Federal and state graduate medical education (GME) funding exceeds $15 billion annually. It is critical to understand mechanisms to align undergraduate medical education (UME) and GME to meet workforce needs. This study aimed to determine whether states' primary care GME (PCGME) trainee growth correlates with indicators of need. METHOD: Data from the American Medical Association Physician Masterfile, the Association of American Medical Colleges, the American Association of the Colleges of Osteopathic Medicine, and the U.S. Census were analyzed to determine how changes between 2002 and 2012 in PCGME trainees-a net primary care physician (PCP) production estimate-correlated with state need using three indicators: (1) PCP-to-population ratio, (2) change in UME graduates, and (3) population growth. RESULTS: Nationally, PCGME trainees declined by 7.1% from the net loss of 679 trainees (combined loss of 54 postgraduate year 1 trainees in internal medicine, family medicine, and pediatrics and addition of 625 fellowship trainees in those specialties). The median state PCGME decline was 2.7%. There was no correlation between the percent change in states' PCGME trainees and PCP-to-population ratio (r = -0.06) or change in UME graduates (r = 0.17). Once adjusted for population growth, PCGME trainees declined by 15.3% nationally; the median state decline was 9.7%. CONCLUSIONS: There is little relationship between PCGME trainee growth and state need indicators. States should capitalize on opportunities to create explicit linkages between UME, GME, and population need; strategically allocate Medicaid GME funds; and monitor the impact of workforce policies and training institution outputs.
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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 | 0.016 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".