How Augmenting Reality Changes the Reality of Simulation: Ethnographic Analysis
Notice bibliographique
Résumé
BACKGROUND: Simulation-based medical education (SBME) provides key medical training for providers to safely and ethically practice high-risk events. Augmented reality (AR)-enhanced simulation projects digital images of realistic examination findings into a participant's field of view, which allows nuanced physical examination findings such as respiratory distress and skin perfusion to be prominently displayed. It is unknown how AR compares to traditional mannequin (TM)-based simulation with regard to influencing participant attention and behavior. OBJECTIVE: The purpose of this study is to use video-based focused ethnography-a problem-focused, context-specific descriptive form of research whereby the research group collectively analyzes and interprets a subject of interest-to compare and categorize provider attention and behavior during TM and AR and provide suggestions for educators looking to delineate these 2 modalities. METHODS: Twenty recorded interprofessional simulations (10 TM, 10 AR) featuring a decompensating child were evaluated through video-based focused ethnography. A generative question was posed: "How do the attention and behavior of participants vary based on the simulation modality?" Iterative data collection, analysis, and pattern explanation were performed by a review team spanning critical care, simulation, and qualitative expertise. RESULTS: The attention and behavior of providers during TM and AR simulation clustered into three core themes: (1) focus and attention, (2) suspension of disbelief, and (3) communication. Participants focused on the mannequin during AR, especially when presented with changing physical examination findings, whereas in TM, participants focused disproportionately on the cardiorespiratory monitor. When participants could not trust what they were seeing or feeling in either modality, the illusion of realism was lost. In AR, this manifested as being unable to physically touch a digital mannequin, and in TM, participants were often unsure if they could trust their physical examination findings. Finally, communication differed, with calmer and clearer communication during TM, while AR communication was more chaotic. CONCLUSIONS: The primary differences clustered around focus and attention, suspension of disbelief, and communication. Our findings provide an alternative methodology to categorize simulation, shifting focus from simulation modality and fidelity to participant behavior and experience. This alternative categorization suggests that TM simulation may be superior for practical skill acquisition and the introduction of communication strategies for novice learners. Meanwhile, AR simulation offers the opportunity for advanced training in clinical assessment. Further, AR could be a more appropriate platform for assessing communication and leadership by more experienced clinicians due to the generated environment being more representative of decompensation events. Further research will explore the attention and behavior of providers in virtual reality-based simulations and real-life resuscitations. Ultimately, these profiles will inform the development of an evidence-based guide for educators looking to optimize simulation-based medical education by pairing learning objectives with the ideal simulation modality.
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 | 0,012 | 0,025 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,007 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,001 | 0,006 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 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 ».