What Can Be Achieved With Motivation-Based Teaching of Medical Students? A Monocentric Retrospective Audit of Retention Among Highly Motivated Graduates Who Underwent the Learning-by-Doing Concept in Anesthesiology and Intensive Care Medicine
Notice bibliographique
Résumé
BACKGROUND: Medical education, in general, is undergoing a significant shift from traditional methods. It becomes very difficult to discover effective teaching methods within the limited possibilities in patient hands-on education, especially as seen in anesthesiology and intensive care medicine (AIM) teaching. Motivation-based teaching is very popular in all other aspects of education, but it has received scant attention in medical education literature, even though it can make a real difference for both students and physicians. OBJECTIVE: The primary aim of this retrospective audit was to find out if proper motivation-based teaching of students via the development of AKUTNE.CZ's serious games can help retain graduates of the Faculty of Medicine of Masaryk University (FMMU) for the AIM specialty. METHODS: Motivation-based teaching and the learning-by-doing concept were applied to a subject called Individual Project. Our topic, The Development of the Multimedia Educational Portal, AKUTNE.CZ, has been offered since 2010. The objective has been the development of supportive material in the form of interactive algorithms, serious games, and virtual patients for problem-based learning or team-based learning lectures aimed at acute medicine. We performed a retrospective questionnaire evaluation of all participants from the 2010-2017 period, focusing on their choice of medical specialty in 2017. The data were reported descriptively. RESULTS: We evaluated 142 students who passed Individual Project with topic The Development of the Multimedia Educational Portal, AKUTNE.CZ during 2010 to 2017. In this period, they developed up to 77 electronic serious games in the form of interactive multimedia algorithms. Out of 139 students in general medicine, 108 students (77.7%) had already graduated and 37 graduates (34.3%) worked in the AIM specialty. Furthermore, 57 graduates (52.8%) chose the same specialty after graduation, matching the topic of their algorithm, and 37 (65%) of these graduates decided to pursue AIM. CONCLUSIONS: Motivation-based teaching and the concept of learning-by-doing by the algorithm/serious game development led to the significant retention of FMMU graduates in the AIM specialty. This concept could be considered successful, and as the concept itself can also be well integrated into the teaching of other medical specialties, the potential of motivation-based teaching should be used more broadly within medical education.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».