V-CarE—A Conceptual Design Model for Providing COVID-19 Pandemic Awareness: Proposal for a Virtual Reality Design Approach to Facilitate People With Persistent Postural-Perceptual Dizziness
Notice bibliographique
Résumé
BACKGROUND: Virtual reality (VR) technology has been solidifying its ground since its existence, where engagement and a sense of presence are key. The contemporary field of development has captured the attention of researchers due to its flexibility and compatibility attributes. During the COVID-19 pandemic, several research outputs have shown promising prospects of continuing research in the field of VR design and development-in health sciences including learning and training. OBJECTIVE: In this paper, we aim to propose a conceptual development model named V-CarE (Virtual Care Experience) that can facilitate the understanding of pandemics when it comes to a crisis, taking precautionary measures where needed, and getting used to certain actions for preventing pandemic spread through habituation. Moreover, this conceptual model is useful to expand the development strategy to incorporate different types of users and technological aid as per need and requirement. METHODS: For a detailed understanding of the proposed model, we have developed a novel design strategy to bring awareness to the user about the current COVID-19 pandemic. VR research in health sciences has shown that with appropriate management and development, VR technology can efficiently support people with health issues and special needs, which motivated our attempts to explore the possibility of employing our proposed model to treat Persistent Postural-Perceptual Dizziness (PPPD)-a persistent nonvertiginous dizziness that could last for 3 months or more. The purpose of including patients with PPPD is to get them engaged in the learning experience and to make them comfortable with VR. We believe this confidence and habituation would help them get engaged with VR for treatment (dizziness alleviation) while practicing the preventive measures during the pandemic in an interactive environment without actually facing any pandemic directly. Subsequently, for advanced development using the V-CarE model, we have briefly discussed that even contemporary technology like internet of things (IoT) for handling devices, can be incorporated without disrupting the complete 3D-immersive experience. RESULTS: In our discussion, we have shown that the proposed model represents a significant step toward the accessibility of VR technology by creating a pathway toward awareness of pandemics and, also, an effective care strategy for PPPD people. Moreover, by introducing advanced technology, we will only further enhance the development for wider accessibility of VR technology while keeping the core purpose of the development intact. CONCLUSIONS: V-CarE-based developed VR projects are designed with all the core elements of health sciences, technology, and training making it accessible and engaging for the users and improving their lifestyle by safely experiencing the unknown. We suggest that with further design-based research, the proposed V-CarE model has the potential to be a valuable tool connecting different fields to wider communities.
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 | 0,005 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,004 |
| Communication savante | 0,006 | 0,006 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 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 ».