Evidence map›Paper›PMID 41733241›Full record

ArticleJournal of advanced nursing2026

Spherical Video-Based Virtual Reality for Nurses' Workplace Violence Management: A Convergent Mixed-Methods Study.

Yen-Yin Lin, Ting-Ti Lin, Yin-Ling Hung, Hsin-Pei Feng, Chun-Yu Liang, Wen-Chii Tzeng

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Article in Journal of advanced nursing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Yen-Yin LinTri-Service General Hospital, Taipei City, Taiwan.
Ting-Ti LinNational Defense Medical University, Taipei City, Taiwan.ORCID https://orcid.org/0000-0003-2682-5273
Yin-Ling HungTri-Service General Hospital, Taipei City, Taiwan.
Hsin-Pei FengNational Defense Medical University, Taipei City, Taiwan.ORCID https://orcid.org/0000-0002-2248-9308
Chun-Yu LiangNational Defense Medical University, Taipei City, Taiwan.ORCID https://orcid.org/0000-0002-2007-9583
Wen-Chii TzengNational Defense Medical University, Taipei City, Taiwan.ORCID https://orcid.org/0000-0002-4205-896X

Funding

National Science and Technology Council NSTC 113-2314-B-016-019-MY3
6 · The paper itself

Abstract

aimTo evaluate the feasibility, effectiveness, and acceptability of a spherical video-based virtual reality training programme aimed at helping nurses manage workplace violence.

designA convergent mixed-methods study.

methodsThis study included nurses from a tertiary medical centre in Taiwan. The training programme involved four interactive 360° scenarios focused on recognising, de-escalating, and responding to workplace violence. Quantitative measures included risk perception, confidence in coping with aggression, and technology acceptance. Qualitative measures included the participants' learning experiences. Quantitative and qualitative findings were integrated through joint displays.

resultsThe programme was feasible, with all participants completing the training. Nurses reported high levels of perceived usefulness and ease of use. Quantitative data revealed considerable improvements in risk awareness and confidence in responding to incidents of violence. Qualitative data revealed that immersion and emotional resonance enhanced engagement, fostered self-reflection, and reinforced learning. Technical challenges included subtitle placement and speech recognition accuracy.

conclusionSpherical video-based virtual reality is a feasible, acceptable, and effective training approach that improves nurses' preparedness for managing workplace violence by enhancing situational awareness and confidence in addressing high-risk situations. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: Integrating spherical video-based virtual reality into continual education may strengthen nurses' workplace safety competencies, prevent harm from incidents of violence, and improve patient care in stressful environments. IMPACT: Workplace violence undermines nurse safety and patient care. Current training modules often lack contextual realism. Our programme improved nurses' awareness, confidence, and reflective learning and was feasible and well accepted. The findings are relevant to nursing educators, hospital administrators, and policymakers seeking sustainable strategies for addressing workplace violence. REPORTING

methodThis study adhered to the Revised Standards for Quality Improvement Reporting Excellence. PATIENT OR PUBLIC CONTRIBUTION: Patients or the public were not involved in the design, conduct, or reporting of this study.

Indexed as

Nursing Staff, HospitalVirtual RealityWorkplace ViolenceAdultFeasibility StudiesFemaleHumansMaleMiddle AgedTaiwanVideo Recordingimmersive learningmixed‐methods researchnursenursing educationvirtual realityworkplace violence

Identifiers

PMID41733241
PMCPMC13576876

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.