Exploring the Acceptability, Feasibility, and Perceived Effects of Immersive Virtual Reality in Comparison to Standardized Patient Simulations in Nursing Education: A Mixed-Methods Pilot Study
Notice bibliographique
Résumé
Purpose: Simulation using immersive virtual reality (IVR) is gaining in popularity in nursing pedagogy. Considering its innovative character, it is essential to tailor the integration of IVR simulation based on the acceptability and feasibility reported by nursing students. Moreover, little is known about its effects compared to other simulation types, such as standardized patient simulation (SPS). This study aimed to compare the acceptability, feasibility, and perceived effects of IVR and SPS activities among undergraduate nursing students. Method: A pilot mixed-methods randomized crossover-controlled trial over two campuses in the province of Quebec was completed. The sample included undergraduate nursing students (n = 14). Participants were randomly assigned to begin with IVR or SPS, followed by the other modality. Data collection included post-assessments regarding acceptability, cognitive load, engagement, situational motivation, and satisfaction after each simulation type. We performed Wilcoxon tests using SPSS. We conducted individual or dyad interviews using a semi-structured interview guide addressing acceptability and feasibility. Three team members analyzed verbatim transcripts. Inductive coding was used to explore emerging ideas, followed by deductive coding to categorize initial codes within the predefined dimensions of acceptability and feasibility from Sidani and Braden’s (2021) framework. Summary tables were produced to condense data. Results: Acceptability was conceptualized in five dimensions: appropriateness, convenience, effectiveness, adherence, and risks. Feasibility was separated into five subthemes: quality of trainers, preparation of participants, material resources, context, and fidelity of the scenario. Participants appreciated the various possibilities and immersive aspects of IVR, such as practising in a safe environment and the innovative, fun experience. A qualitative improvement in patient assessment structure, fluidity, priority establishment, clinical reasoning, and autonomy was also reported. The fidelity of the scenario was deemed higher for IVR than for SPS, according to participants who discussed the use of IVR for evaluation. However, some nuances in implementing IVR, such as targeted competencies, technical problems, equipment comfort, familiarization, and risks of cybersickness should be considered before IVR implementation in nursing education. Furthermore, quantitative results indicated comparable results between IVR and SPS simulations across all variables. No statistically significant difference was found between the two modalities. Conclusion: The implementation of IVR appears acceptable and feasible for undergraduate nursing students, with particular attention to certain factors to ensure optimal outcomes. Quantitative results suggest comparable outcomes, highlighting points of convergence of the two simulation approaches in nursing education. These findings underline the importance of seeking the opinions of primary users when introducing innovative pedagogical interventions. Despite the study’s limitations, this pilot research provides insights into using IVR and SPS activities with nursing students. Future research should focus on testing IVR for evaluation purposes in nursing curricula.
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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 | 0,016 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 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) | 0,003 | 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 ».