Student Performances on Step 1 and Step 2 of the United States Medical Licensing Examination Following Implementation of a Problem-based Learning Curriculum
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: To examine students' performances on Step 1 and Step 2 of the United States Medical Licensing Examination (USMLE) following the implementation of a problem-based learning curriculum. METHOD: Performances on Step 1 of the USMLE for four classes at the University of Missouri-Columbia School of Medicine that completed a new problem-based learning curriculum (1997, 1998, 1999, and 2000) were compared with those of the last two classes to learn in the traditional curriculum (1995 and 1996). Performances on Step 2 of the USMLE for the classes of 1997, 1998, and 1999 were also compared with those of the classes of 1995 and 1996. The authors analyzed matriculation data (GPAs and MCAT scores) for all six classes. They compared all data with those of U.S. and Canadian first-time USMLE takers. RESULTS: The mean scores were higher on USMLE Step 1 for classes in the problem-based learning curriculum than for classes in the traditional curriculum. The mean scores for Step 2 were above the national mean for classes in the revised curriculum and below the national mean for classes in the traditional curriculum. The admission profiles of these classes were essentially the same before and after the change in curriculum. CONCLUSIONS: Major PBL revisions of the curriculum did not compromise the performances of medical students on the licensing examinations; in fact, they may have contributed to higher scores.
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.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it