12 Tips for taking a Student Led Medical Education Program from Concept to Curriculum
Bibliographic record
Abstract
This article was migrated. The article was marked as recommended. The choice of a future career specialty has always been a stressful decision for medical students. To mitigate this stress and assist students in making more informed career decisions we developed the Pre-clerkship Residency Exploration Program (PREP), a two-week summer elective program that provides students with the opportunity to gain exposure to specialities that traditionally do not receive a lot of attention in medical school. To initiate this student led program we faced many obstacles, suffered many failures, learned a tremendous amount and eventually found success. In this article, we offer 12 tips on how to create a medical education program that is sustainable, effective and receives strong buy-in from faculty and administration. Our tips come from the perspective of students starting their own program but are translatable to anyone interested in taking an innovative idea and seeing it through to fruition.
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How this classification was reachedexpand
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 | 0.001 | 0.007 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".