Assessing the Transition of Training in Health Systems Science From Undergraduate to Graduate Medical Education
Bibliographic record
Abstract
BACKGROUND: The American Medical Association Accelerating Change in Medical Education (AMA-ACE) consortium proposes that medical schools include a new 3-pillar model incorporating health systems science (HSS) and basic and clinical sciences. One of the goals of AMA-ACE was to support HSS curricular innovation to improve residency preparation. OBJECTIVE: This study evaluates the effectiveness of HSS curricula by using a large dataset to link medical school graduates to internship Milestones through collaboration with the Accreditation Council for Graduate Medical Education (ACGME). METHODS: ACGME subcompetencies related to the schools' HSS curricula were identified for internal medicine, emergency medicine, family medicine, obstetrics and gynecology (OB/GYN), pediatrics, and surgery. Analysis compared Milestone ratings of ACE school graduates to non-ACE graduates at 6 and 12 months using generalized estimating equation models. RESULTS: At 6 months both groups demonstrated similar HSS-related levels of Milestone performance on the selected ACGME competencies. At 1 year, ACE graduates in OB/GYN scored minimally higher on 2 systems-based practice (SBP) subcompetencies compared to non-ACE school graduates: SBP01 (1.96 vs 1.82, 95% CI 0.03-0.24) and SBP02 (1.87 vs 1.79, 95% CI 0.01-0.16). In internal medicine, ACE graduates scored minimally higher on 3 HSS-related subcompetencies: SBP01 (2.19 vs 2.05, 95% CI 0.04-0.26), PBLI01 (2.13 vs 2.01; 95% CI 0.01-0.24), and PBLI04 (2.05 vs 1.93; 95% CI 0.03-0.21). For the other specialties examined, there were no significant differences between groups. CONCLUSIONS: Graduates from schools with training in HSS had similar Milestone ratings for most subcompetencies and very small differences in Milestone ratings for only 5 subcompetencies across 6 specialties at 1 year, compared to graduates from non-ACE schools. These differences are likely not educationally meaningful.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".