Emotion, cognitive load and learning outcomes during simulation training
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Notice bibliographique
Résumé
CONTEXT: Simulation training has emerged as an effective way to complement clinical training of medical students. Yet outcomes from simulation training must be considered suboptimal when 25-30% of students fail to recognise a cardiac murmur on which they were trained 1 hour previously. There are several possible explanations for failure to improve following simulation training, which include the impact of heightened emotions on learning and cognitive overload caused by interactivity with high-fidelity simulators. This study was conducted to assess emotion during simulation training and to explore the relationships between emotion and cognitive load, and diagnostic performance. METHODS: We trained 84 Year 1 medical students on a scenario of chest pain caused by symptomatic aortic stenosis. After training, students were asked to rate their emotional state and cognitive load. We then provided training on a dyspnoea scenario before asking participants to diagnose the murmur in which they had been trained (aortic stenosis) and a novel murmur (mitral regurgitation). We used factor analysis to identify the principal components of emotion, and then studied the associations between these components of emotion and cognitive load and diagnostic performance. RESULTS: We identified two principal components of emotion, which we felt represented invigoration and tranquillity. Both of these were associated with cognitive load with adjusted regression coefficients of 0.63 (95% confidence interval [CI] 0.28-0.99; p = 0.001) and - 0.44 (95% CI - 0.77 to - 0.10; p = 0.009), respectively. We found a significant negative association between cognitive load and the odds of subsequently identifying the trained murmur (odds ratio 0.27, 95% CI 0.11-0.67; p = 0.004). CONCLUSIONS: We found that increased invigoration and reduced tranquillity during simulation training were associated with increased cognitive load, and that the likelihood of correctly identifying a trained murmur declined with increasing cognitive load. Further studies are needed to evaluate the impact on performance of strategies to alter emotion and cognitive load during simulation training.
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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 | 0,001 | 0,007 |
| 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) | 0,001 | 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 découle