Medical Education Amid the COVID-19 Pandemic: New Perspectives for the Future
Notice bibliographique
Résumé
In response to the COVID-19 pandemic, many medical schools worldwide canceled clinical rotations, resulting in the loss of essential learning opportunities. Subsequently, many medical students reported significant anxiety and stress stemming from the uncertainty surrounding their education and the impact of these changes on their future careers. To address this problem, we recommend looking beyond missed clinical learning opportunities and reflecting on the true purpose of medical education—namely, to train well-rounded physicians. Across the United States, Canada, and the United Kingdom, medical students like ourselves mobilized to organize initiatives to support health care workers, community members, and public health efforts. Understanding the importance of public health promotion and education, medical students assisted in contact tracing and public health counseling. Advocating for vulnerable populations, they provided support to seniors through virtual companionship programs and grocery services. Noticing the struggle of frontline health care workers to find personal support, students collaborated to provide free childcare, pet care, and run errands. Observing a shortage of personal protective equipment, they collected these items to donate to various health care institutions. Recognizing the vast amount of information on COVID-19, students conducted knowledge translation to create accessible documents for the public. Understanding the urgent need for research, they applied their lab skills to contribute to research initiatives. Through this work, we and our fellow medical students learned that medical education does not solely rely on classes and rotations. Rather, it is lifelong learning in different environments and circumstances, and this is true among medical schools worldwide and is applicable beyond the current COVID-19 pandemic. In Canada, the CanMEDS framework describes 7 competencies that guide all learning objectives in medical education. Notably, 5 of the 7 CanMEDS roles focus not on the knowledge physicians possess but on the essential skills they exemplify: collaborator, leader, health advocate, professional, and communicator. Developing these roles is fundamental in the journey to becoming well-rounded future physicians, and we are, in effect, pursuing them in our contributions to the COVID-19 initiatives we described above. Despite the uncertainty of when formal clinical learning will resume, we recommend that students seek unorthodox ways to continuously advance their education outside the clinical sphere. Doing so will inspire them to continue to learn and to become more compassionate and well-rounded future physicians. The incorporation of community volunteering into medical education and its educational value are unexpected blessings from the COVID-19 pandemic and should be implemented into the traditional medical curriculum to enrich learning, even in the postpandemic world.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,011 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,007 | 0,008 |
| Communication savante | 0,010 | 0,022 |
| Science ouverte | 0,002 | 0,009 |
| Intégrité de la recherche | 0,017 | 0,025 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,028 | 0,003 |
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 ».