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 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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".