ArticleBMJ open quality2024
SafeVRwards: Designing a complementary virtual reality module to the Safewards framework intended to relax and manage conflict in mental health wards.
Article in BMJ open quality, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Trial
- Exploring the Barriers to and Facilitators of Using Virtual Reality Relaxation for Patients With Psychiatric Problems: Qualitative Focus Group Study.Journal of medical Internet research · 2025Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAggression and negative activation in mental health inpatient units pose significant challenges for both patients and staff with severe physical and psychological ramifications. The Safewards model is an evidence-based conflict-containment framework including 10 strategies, such as 'Calm Down Methods'. As virtual reality (VR) scenarios have successfully enhanced anxiolytic and deactivating effects of therapeutic interventions, they are increasingly considered a means to enhance current models, like Safewards.
objectivesThe present participatory design investigates the feasibility and user experience of integrating VR therapy as an add-on strategy to the Safewards model, gathering preliminary data and qualitative feedback from bedside staff in an adult inpatient mental health unit.
methodsAn exploratory within-subjects design combining qualitative observations, self-report questionnaires and semistructured interviews is employed with four nurse champions from the mental health unit at Michael Garron Hospital (Toronto, Canada).
resultsA chronological overview of the design process, adaptations and description of the user experience is reported.
conclusion'SafeVRwards' introduces VR as a promising conflic-containment strategy complementary to the Safewards model, which can be optimised for deployment through user-oriented refinements and enhanced customisation capacity driven by clinical staff input.
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