Bridge to emergency medicine: A virtual medical student curriculum for flipped classroom learning during the COVID-19 pandemic
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,013 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».