Bridge to emergency medicine: A virtual medical student curriculum for flipped classroom learning during the COVID-19 pandemic
Bibliographic record
Abstract
Intro/Background: Medical students who are matching in emergency medicine (EM) should be well prepared to start intern year with an understanding of the workup of common chief complaints. EM education opportunities vary among different medical schools. Students' educational experiences largely depended on didactics received or patients seen during their rotations, both of which have been limited by the COVID-19 pandemic. Purpose/Objective: We sought to create a free, open access, flipped classroom curriculum targeting EM-bound fourth-year medical students to prepare them with essential knowledge and practical management skills needed for intern year. We included vetted asynchronous resources for self-study paired with a robust, case-based, virtual EM elective that could be used by programs to offset limited clinical exposure imposed by COVID-19. Methods: Using the EM Model as a guide, a team of experienced EM educators identified essential learning topics to create an 8-week, self-paced, free open access asynchronous curriculum called Bridge to EM. Self-study content was paired with facilitated case-based virtual classroom experiences provided by Foundations of Emergency Medicine (FoEM). 1 The curriculum was published on Academic Life in EM,2 the FoEM website, and listed on the AAMC iCollaborative.3 Outcomes (if available): The Bridge curriculum was viewed 72,928 times from May-Dec 2020. Viewers were from 5650 cities in 127 countries. Chicago, New York City, Toronto, and Melbourne were the most common cities to access content. During the same time period, 44 discrete learning sites encompassing over 3,400 learners registered to use the formal virtual curriculum, which included the Bridge asynchronous content and the FoEM cases. Most of these sites were US based medical schools. Summary: We have created a flexible, online, freely available curriculum that can be used individually by medical students to prepare for intern year, or systematically by programs or medical schools to provide a virtual, case-based EM curriculum. Prior to development of the Bridge to EM, there was no existing online curriculum for students to use to prepare for the start of their intern year. The COVID-19 crisis created an urgent need for online and virtual learning materials while students were prohibited from EM rotations in most US medical schools. The Bridge curriculum was published during the early pandemic timeframe to meet the needs of both students and programs. Its release was met with enthusiasm from students and educators, and it was accessed around the world. The curriculum can be used to teach basic EM concepts as a supplement to a traditional EM clinical elective, or to replace it when in-person rotations are not possible. The curriculum uses principles of effective learning such as spaced repetition, application of content in a case-based context, flipped classroom learning, and interactive discussions. The Bridge platform can serve as a prototype or model for online curricula for other disciplines or for different target content areas or audiences.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".