How Much Do Differences in Medical Schools Influence Student Performance? A Longitudinal Study Employing Hierarchical Linear Modeling
Bibliographic record
Abstract
BACKGROUND: Medical school curricula have undergone considerable change in the past half century. There is little evidence, however, for the impact of various curricula and educational policies on student learning once incoming performance and the nonrandom nature of students nested within schools has been accounted for. PURPOSE: To investigate effects of school variables on United States Medical Licensing Examination (USMLE) Step 1-3 scores over an 11-year period (1994-2004). METHODS: Using Association of American Medical Colleges and USMLE longitudinal data for 116 medical schools, hierarchical linear modeling was used to study the effects of school variables on Step 1-3. RESULTS: Mean unadjusted between school variance was 14.74%, 10.50%, and 11.25%, for USMLE Step 1-3. When student covariates were included, between-school variation was less than 5%. The variance accounted for in student performance by the student covariates ranged from 27.58% to 36.51% for Step 1,16.37% to 24.48% for Step 2 and 19.22% to 25.32% for Step 3.The proportion of the between-school variance that was accounted for by the student covariates ranged between 81.22% and 88.26% for Step 1, 48.44% and 79.77% for Step 2, and 68.41% and 80.78% for Step 3 [corrected]. School-level variables did not consistently predict for adjusted mean school Step performance. CONCLUSIONS: Individual student differences account for most of the variation in USMLE performance with small contributions from between-school variation and even smaller contribution from curriculum and educational policies.
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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 | 0.019 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".