Association between Clinical Simulation Design Features and Novice Healthcare Professionals’ Cognitive Load: A Systematic Review and Meta-Analysis
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Notice bibliographique
Résumé
Background Clinical simulations are complex educational interventions characterized by several design features, which have the potential to influence cognitive load, that is, the mental effort required to assimilate new information and learn. This systematic review and meta-analysis explored the associations between simulation design features and cognitive load in novice healthcare professionals. Methods Based on the Joanna Briggs Institute methodology, a search was performed in five databases for quantitative studies in which the cognitive load of novice healthcare professionals was measured during or after a simulation activity. Each clinical simulation was coded to describe its design features. Univariate and multivariate mixed model analyses were performed to explore the associations between simulation design features and cognitive load. Results From 962 unique records, 45 studies were included and 27 provided enough data on subjective cognitive load (i.e., Paas Scale and NASA-Task Load Index scores) to be meta-analyzed. In the multivariate analysis for the NASA-Task Load Index scores, each repetition of a simulation using the same scenario resulted in a linear decrease in cognitive load. In contrast, technology-based instruction before or during a simulation activity was associated with higher cognitive load. In the univariate analyses, other features such as feedback and instructor presence were also statistically associated with cognitive load. Regarding the univariate analyses of the Paas Scale scores, simulator type, briefing, debriefing, and repetitive practice were statistically associated with cognitive load. Conclusion This is the first meta-analysis exploring the relationship between clinical simulation design features and novice healthcare professionals’ cognitive load. Although the findings show that several design features can potentially increase or decrease cognitive load, several gaps and inconsistencies in the current literature make it difficult to appreciate how such reciprocity influences novice healthcare professionals’ learning. These limitations are discussed and avenues for educators and further research are suggested.
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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 | 0,007 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,009 | 0,002 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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