Evaluation of a course to prepare international students for the United States Medical Licensing Examination step 2 clinical skills exam
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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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".