Using Immersive Virtual Reality to Impact Clinical Reasoning of New Graduate Nurses
Notice bibliographique
Résumé
New graduate nurses (NGNs) face challenging work environments due to experiential learning gaps, complex patients, and high rates of attrition (Tomblin Murphy et al., 2022). Clinical reasoning (CR) improves nursing quality by fostering confidence, autonomy, readiness for practice, and minimizing patient safety events such as failing to recognize a patient who is decompensating (Mohammadi‐Shahboulaghi et al., 2021; Powers et al., 2019). CR is a complex and iterative cognitive process whereby nurses apply knowledge and experience to a clinical situation (Benner, 1984; Kavanagh & Szweda, 2017; Levett-Jones et al., 2010). Virtual reality (VR) is a technology increasingly used to support CR in undergraduate nursing students (Sim et al., 2022). VR reduces the cost, space, and equipment required to develop CR while increasing access to diverse scenarios (R. P. Cant & Cooper, 2017). There is limited research examining the impact of VR on NGNs’ CR. Research questions: This work addresses the following: A. What are NGNs’ perspectives on integrating a VR experience into their transition to practice? B. How does an immersive VR experience impact NGNs’ CR skill development? Methods: A triangulation mixed methods with a single-group quasi-experimental design and an interpretive description approach was used to collect data from 12 NGNs in Halifax, Nova Scotia. Participants either had an active Registered Nurse or Licensed Practical Nurse license with the Nova Scotia College of Nurses, and started work within the last 12 months within an acute care nursing unit in the Central zone of Nova Scotia Health. The nurses’ CR cycle framework was used to design a VR experience using the Edify VR platform and the HP Reverb G2 head-mounted display (Levett-Jones et al., 2010). Data collection occurred pre-test, during the VR experience, post-test, and one-month distant post-test. The mixed methods analysis integrated qualitative interviews, surveys, and field notes, with quantitative measurements of cybersickness, using the Simulator Sickness Questionnaire (SSQ) and CR, using the Nurse’s Clinical Reasoning Scale (NCRS). Results: Participants shared five qualitative themes: VR as a contributor to a positive learning environment, VR hardware and navigation challenges, minimal cybersickness, improving CR with repeated practice, and participant VR recommendations. The themes of minimal cybersickness and improving CR with repeated practice were congruent with the quantitative findings. Participants’ median total SSQ scores were 3.78 (95% CI –34.26, -22.92, p < 0.05) and associated sub-scores were significantly lower than those who completed a similar gaming VR experience (M= 34.26) (Saredakis et al., 2020). Pre-test (M = 60.19, SD = 7.19) and post-test (M = 66.18, SD = 7.22) NCRS revealed a significant increase in CR (p = 0.0013). This increase was sustained with no significant difference in NCRS between the post-test and the distant post-test time points (p = 0.76). Conclusions: This is the first study to examine the impact of an immersive VR experience on NGNs’ CR skills. NGNs expressed enthusiasm for the VR experience noting its ability to promote a positive learning environment. While cybersickness was not a significant barrier to VR use, participants did note challenges with the VR equipment and the virtual environment. The findings support that VR is an impactful tool to promote NGNs’ CR. Additional research is needed to compare the efficacy of VR to other teaching modalities such as mannikin-based high-fidelity simulation.
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 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,000 | 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 ».