A national strategy to engage medical students in otolaryngology-head and neck surgery medical education: the LearnENT ambassador program
Bibliographic record
Abstract
BACKGROUND: In the realm of medical education, student-led ambassador programs represent an innovative approach to increase awareness about medical education resources. LearnENT is an internationally recognized otolaryngology-head and neck surgery (OHNS) smartphone app and website designed for medical trainees to learn about OHNS. However, upon the initial launch of the app, there was a lack of medical student awareness and engagement. APPROACH: In this article, we highlight the process and lessons learned from developing an ambassador program to increase the national presence and uptake of LearnENT. Medical students from across Canada were recruited and trained to promote the app at their respective institutions. EVALUATION: Ambassadors hosted events and spearheaded initiatives around the country with the goal of showcasing LearnENT. Furthermore, ambassadors were engaged in scholarly initiatives such as creating educational content for LearnENT and giving presentations at national conferences. REFLECTIONS: Critical factors in the success of a student-led ambassador program include ensuring widespread dissemination of the program, establishing clear expectations for ambassadors, equipping ambassadors with standardized promotional material, and promoting collaboration to collectively work towards addressing challenges. When creating a national student-led group such as an ambassador program, outreach to senior stakeholders can be an effective way to involve students at different institutions, provide mentorship opportunities for students and provide opportunities for educational scholarship. With new medical education innovations constantly surfacing, the LearnENT ambassador program model can be applied in other contexts to increase awareness of medical education resources.
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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 | 0.004 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 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".