Training and Assessing Teamwork in Interprofessional Virtual Reality–Based Simulation Using the TeamSTEPPS Framework: Protocol for Randomized Pre-Post Intervention Study
Notice bibliographique
Résumé
BACKGROUND: Interprofessional teamwork is essential for patient outcomes in emergency medicine; yet, effective training in this area is scarce. Virtual reality (VR) provides a promising, resource-efficient solution for simulating emergency scenarios and facilitating interprofessional collaboration. While VR-based training has shown benefits for medical skill and knowledge acquisition, assessing teamwork within such environments remains a challenge due to the lack of validated measurement tools. Existing teamwork assessment instruments, developed for physical simulations, may not fully apply to VR due to differences in communication modalities, interaction mechanics, and observer perspectives. OBJECTIVE: This study aims to adapt and validate the TeamSTEPPS framework to assess teamwork in VR-based training. Subsequently, these adapted instruments will enable the investigation of whether interprofessional teamwork can be successfully trained in VR scenarios. METHODS: Prior to the study, measurement instruments for subjective (Teamwork Perceptions Questionnaire) and objective teamwork quality (Team Performance Observation Tool, TPOT) will be adapted and validated for use in VR scenarios. Validation of the adapted version of the Team Performance Observation Tool includes expert consensus via a modified Delphi method as well as validity and reliability testing using recorded VR teamwork sessions. The study itself is designed as a prospective pre-post study with a planned enrollment of 65 nursing and 65 medical students working in randomly assigned interprofessional teams. On 3 timepoints (day 1, day 8, and day 15), participants engage in a VR scenario simulating 1 out of 3 different emergency medical conditions (esophageal variceal bleeding, exacerbated chronic obstructive pulmonary disease, and atrial fibrillation due to urinary tract infection). As an intervention, a structured training video on successful teamwork according to the TeamSTEPPS concept is shown on day 8 immediately before the second VR scenario. Teamwork is assessed objectively with the adapted version of the Team Performance Observation Tool and subjectively with the adapted Teamwork Perceptions Questionnaire. Medical performance will be recorded automatically by the VR software based on the medical measures conducted by the team. RESULTS: As of May 2024, a total of 28 interprofessional teams have been enrolled. Data analysis will begin in late 2025. CONCLUSIONS: This study addresses the challenge of adapting teamwork assessment tools to VR environments and may provide insights into the potential of VR-based training for improving interprofessional collaboration in medical education. Future research could include a control group to measure the effects of team training more rigorously or use more enhanced technologies (eg, natural language processing) to capture the full range of teamwork behavior. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/68705.
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 | 0,028 | 0,022 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,003 |
| Méta-épidémiologie (sens large) | 0,009 | 0,004 |
| Bibliométrie | 0,004 | 0,004 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,006 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,049 | 0,012 |
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 ».