Innovating medical education—Bringing the clinical environment online
Notice bibliographique
Résumé
As a medical student, transitioning from university- to hospital-based learning is a highly anticipated rite of passage. It's a unique experience that challenges students to think critically and practically apply the knowledge they have gained thus far. Unfortunately, the COVID-19 pandemic has changed all this. Not having that first in-person patient interaction can significantly impact a student's ability to consolidate knowledge and engage in experiential and peer assisted learning. As final-year students who had the unique experience of pre-COVID clinical experience and online education, it was our goal to reflect on our experiences and simulate the clinical environment for new students. Using a process called design thinking, our first step was to empathise with the students and understand how their experiences were different to ours. Using this insight and facilitated by academic staff, we synthesised a range of near-peer-assisted clinical learning aids in the form of a clinical guidebook, podcasts and a clinical exam skills teaching video. The guidebook and podcasts covered high-yield clinical scenarios and sample history vignettes with accompanying guidance on how to present the history. We also included tips on approaching patients and taking histories that we had gained during our clinical experience. The neonatal examination video consisted of a head-to-toe examination of a newborn baby supplemented by a voiceover explaining the steps and the relevance of each section. We will be actively looking for student feedback to further refine these learning aids. To design appropriate content, we reflected on our own experiences and what tools were most beneficial to our learning, including learning opportunities that are unique to the clinical environment. We realised the importance of experiential learning in how much you can gain from observation and interacting with patients. These personal interactions are crucial in solidifying concepts learned in the classroom and grounding knowledge in practice. Additionally, we realised that virtual learning means that students miss out on the passive learning obtained from face-to-face interactions with peers, in addition to the social support provided from classmates. Peer-assisted learning is a hidden asset of medical education and through discussion and collaboration further insight and knowledge can be obtained. This was our first experience as educators rather than students. As a physician, dedication to life-long learning is an essential component of one's career, and through our participation in this project, we were able to take that first step into teaching. If anything, COVID-19 has forced medical schools and clinical educators to be creative and innovative in designing and delivering medical curriculum. Although many new avenues of teaching were designed during the pandemic, their application is likely to still be beneficial when clinical placement practice returns to normal. As medical students who have recently engaged in similar clinical modules, we have the unique insight and recent memory of our first hospital experience, and thus, our ability to engage these students in peer assisted learning can provide additional benefits to the standard curriculum.
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 | 0,023 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,005 | 0,008 |
| Communication savante | 0,014 | 0,010 |
| Science ouverte | 0,002 | 0,020 |
| Intégrité de la recherche | 0,003 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,013 | 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 ».