Usability Evaluation of the Preoperative ISBAR (Identification, Situation, Background, Assessment, and Recommendation) Desktop Virtual Reality Application: Qualitative Observational Study
Notice bibliographique
Résumé
BACKGROUND: Systematic communication, such as the ISBAR (identification, situation, background, assessment, recommendation) approach, comprises a generic, transferable nontechnical skill. It can be used during the handover of patients set to undergo surgery and can be practiced in various ways, including virtual reality (VR). VR increasingly has been implemented and valued in nursing education as a positive contribution to teach students about pre- and postoperative nursing. A new nonimmersive 3D learning activity called the Preoperative ISBAR Desktop VR Application has been developed for undergraduate nursing students to learn preoperative handover using the ISBAR approach. However, the usability of this learning activity has not been studied. OBJECTIVE: This study aimed to investigate how second-year undergraduate nursing students evaluated the usability of the Preoperative ISBAR Desktop VR Application. METHODS: This was a qualitative study with observation and interviews. The inclusion criteria were undergraduate second-year nursing students of varying ages, gender, and anticipated technological competence. The System Usability Scale (SUS) questionnaire was used to get a score on overall usability. RESULTS: A total of 9 second-year nursing students aged 22-29 years participated in the study. The average score on the SUS was 83 (range 0-100), which equals a "B" on the graded scale and is excellent for an adjective-grade rating. The students expressed increased motivation to learn while working in self-instructed desktop VR. Still, a few technical difficulties occurred, and some students reported that they experienced some problems comprehending the instructions provided in the application. Long written instructions and a lack of self-pacing built into the application were considered limitations. CONCLUSIONS: The nursing students found the application to be usable overall, giving it an excellent usability score and noting that the application provided opportunities for active participation, which was motivational and facilitated their perceived learning outcomes. The next version of the application, to be used in a randomized controlled trial, will be upgraded to address technological and comprehension issues.
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Comment cette classification a été obtenuedéplier
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 | 0,004 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| 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 | 0,000 | 0,000 |
| 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 tête enseignante, 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 ».