A Novel Scenario-Based, Mixed-Reality Platform for Training Nontechnical Skills of Battlefield First Aid: Prospective Interventional Study
Notice bibliographique
Résumé
BACKGROUND: Although battlefield first aid (BFA) training shares many common features with civilian training, such as the need to address technical skills and nontechnical skills (NTSs), it is more highly scenario-dependent. Studies into extended reality show clear benefits in medical training; however, the training effects of extended reality on NTSs, including teamwork and decision-making in BFA, have not been fully proven. OBJECTIVE: The current study aimed to create and test a scenario-based, mixed-reality platform suitable for training NTSs in BFA. METHODS: First, using next-generation modeling technology and an animation synchronization system, a 10-person offensive battle drill was established. Decision-making training software addressing basic principles of tactical combat casualty care was constructed and integrated into the scenarios with Unreal Engine 4 (Epic Games). Large-space teamwork and virtual interaction systems that made sense in the proposed platform were developed. Unreal Engine 4 and software engineering technology were used to combine modules to establish a mixed-reality BFA training platform. A total of 20 Grade 4 medical students were recruited to accept BFA training with the platform. Pretraining and posttraining tests were carried out in 2 forms to evaluate the training effectiveness: one was knowledge acquisition regarding the NTS and the other was a real-world, scenario-based test. In addition, the students were asked to rate their agreement with a series of survey items on a 5-point Likert scale. RESULTS: A battlefield geographic environment, tactical scenarios, scenario-based decision software, large-space teamwork, and virtual interaction system modules were successfully developed and combined to establish the mixed-reality training platform for BFA. The posttraining score of the students' knowledge acquisition was significantly higher than that of pretraining (t=-12.114; P≤.001). Furthermore, the NTS score and the total score that the students obtained in the real-world test were significantly higher than those before training (t=-17.756 and t=-21.354, respectively; P≤.001). However, there was no significant difference between the scores of technical skills that the students obtained before and after training. A posttraining survey revealed that the students found the platform helpful in improving NTSs for BFA, and they were confident in applying BFA skills after training. However, most trainees thought that the platform was not helpful for improving the technical skills of BFA, and 45% (9/20) of the trainees were not satisfied with the simulation effect. CONCLUSIONS: A scenario-based, mixed-reality platform was constructed in this study. In this platform, interaction of the movement of multiple players in a large space and the interaction of decision-making by the trainees between the real world and the virtual world were accomplished. The platform could improve the NTSs of BFA. Future works, including improvement of the simulation effects and development of a training platform that could effectively improve both the technical skills and NTSs of BFA, will be carried out.
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 | 0,003 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 ».