Medical school curricula: do curricular approaches affect competence in medicine?
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: US medical school curricula continually undergo reform. The effect of formal curricular approaches (course organization and pedagogical techniques) on competence in medicine as measured by the United States Medical Licensing Examinations (USMLE) Step 1, 2, and 3 is not fully understood. The purpose of this study was to investigate the effects of formal curricular approaches in a latent variable path analysis model of achievement-aptitude-competence in medicine. METHODS: Using Association of American Medical Colleges (AAMC) and USMLE longitudinal data (1994-2004) for 116 medical schools, structural equation modeling was used to study latent variable path models assessing the impact of curriculum on competence in medicine (n=9,332). RESULTS: A latent variable path model consisting of three latent variables measured by undergraduate grade point average (general achievement), Medical College Admission Test subscores (aptitude for medicine), and USMLE Step 1-3 (competence in medicine) was used to assess the impact of curriculum on competence in medicine. Two models were tested; one resulted in a Comparative Fit Index=.931 with a path coefficient of 0.04 from curriculum to competence in medicine. While there was a good fit of the data to the final model, the type of school curriculum did not significantly influence competence in medicine since it accounted for less than 1% of the variation in student performance on the USMLE. CONCLUSIONS: Various formal curricular approaches have little differential effect on students' performance on the USMLE.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".