Implementation of Virtual Reality in Health Professions Education: Scoping Review
Notice bibliographique
Résumé
BACKGROUND: Virtual reality has been gaining ground in health professions education and may offer students a platform to experience and master situations without endangering patients or themselves. When implemented effectively, virtual reality technologies may enable highly engaging learning activities and interactive simulations. However, implementation processes present challenges, and the key to successful implementation is identifying barriers and facilitators as well as finding strategies to address them. OBJECTIVE: This scoping review aimed to identify the literature on virtual reality implementation in health professions education, identify barriers to and facilitators of implementation, and highlight gaps in the literature in this area. METHODS: The scoping review was conducted based on the Joanna Briggs Institute Evidence Synthesis methodologies. Electronic searches were conducted in the Academic Search Elite, Education Source, and CINAHL databases on January 5, 2022, in Google Scholar on February 2 and November 18, 2022, and in PubMed database on November 18, 2022. We conducted hand searches of key items, reference tracking, and citation tracking and searches on government webpages on February 2, 2022. At least 2 reviewers screened the identified literature. Eligible studies were considered based on predefined inclusion criteria. The results of the identified items were analyzed and synthesized using qualitative content analysis. RESULTS: We included 7 papers and identified 7 categories related to facilitators of and barriers to implementation-collaborative participation, availability, expenses, guidelines, technology, careful design and evaluation, and training-and developed a model that links the categories to the 4 constructs from Carl May's general theory of implementation. All the included reports provided recommendations for implementation, including recommendations for careful design and evaluation, training of faculty and students, and faculty presence during use. CONCLUSIONS: Virtual reality implementation in health professions education appears to be a new and underexplored research field. This scoping review has several limitations, including definitions and search words, language, and that we did not assess the included papers' quality. Important implications from our findings are that ensuring faculty's and students' competence in using virtual reality technology is necessary for the implementation processes. Collaborative participation by including end users in the development process is another factor that may ensure successful implementation in higher education contexts. To ensure stakeholders' motivation and potential to use virtual reality, faculty and students could be invited to participate in the development process to ensure that the educational content is valued. Moreover, technological challenges and usability issues should be resolved before implementation to ensure that pedagogical content is the focus. This accentuates the importance of piloting, sufficient time resources, basic testing, and sharing of experiences before implementation. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/37222.
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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 | 0,048 | 0,196 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,005 | 0,006 |
| Bibliométrie | 0,020 | 0,023 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,008 | 0,007 |
| Science ouverte | 0,004 | 0,005 |
| Intégrité de la recherche | 0,006 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».