Evaluation of a course to prepare international students for the United States Medical Licensing Examination step 2 clinical skills exam
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: United States (US) and Canadian citizens attending medical school abroad often desire to return to the US for residency, and therefore must pass US licensing exams. We describe a 2-day United States Medical Licensing Examination (USMLE) step 2 clinical skills (CS) preparation course for students in the Technion American Medical School program (Haifa, Israel) between 2012 and 2016. METHODS: Students completed pre- and post-course questionnaires. The paired t-test was used to measure students' perceptions of knowledge, preparation, confidence, and competence in CS pre- and post-course. To test for differences by gender or country of birth, analysis of variance was used. We compared USMLE step 2 CS pass rates between the 5 years prior to the course and the 5 years during which the course was offered. RESULTS: Ninety students took the course between 2012 and 2016. Course evaluations began in 2013. Seventy-three students agreed to participate in the evaluation, and 64 completed the pre- and post-course surveys. Of the 64 students, 58% were US-born and 53% were male. Students reported statistically significant improvements in confidence and competence in all areas. No differences were found by gender or country of origin. The average pass rate for the 5 years prior to the course was 82%, and the average pass rate for the 5 years of the course was 89%. CONCLUSION: A CS course delivered at an international medical school may help to close the gap between the pass rates of US and international medical graduates on a high-stakes licensing exam. More experience is needed to determine if this model is replicable.
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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 | 0.092 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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