Nursing students’ views of using virtual reality in healthcare: A qualitative study

Saab, Mohamad M.; Landers, Margaret; Murphy, David; O’Mahony, Billy; Cooke, Eoghan; O’Driscoll, Michelle; Hegarty, Josephine

doi:10.1111/jocn.15978

Cited by 53 publications

(48 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The literature suggests that AI technologies can support compassion by helping health professionals to understand and respond to human suffering. Specific examples include immersive VR training on psychological symptoms of dementia ( Jones et al, 2021 ); VR training using vignettes for Parkinson’s disease ( Hess et al, 2022 ), and VR training for testicular disease ( Saab et al, 2022 ). However, benefits of student engagement and perceived learning associated with immersive learning may not translate into better exam scores or clinical skills ( Jacobs and Maidwell-Smith, 2022 ) without sufficient preparation or teaching support ( Saab et al, 2022 ).…”

Section: Resultsmentioning

confidence: 99%

“…It is unclear how to maintain the initial benefits and permanence of behavior change produced by shortterm virtual health coaching interventions (Bevilacqua et al, 2020) and this needs further research to attain lasting clinical benefits. Findings in the literature suggests there is great potential for AI technologies to enhance underexplored elements of compassion by enriching education, learning and clinical practice (Sukhera and Poleksic, 2021;Saab et al, 2022). There appears to be an "engagement factor" (Navarrete et al, 2021) associated with immersive VR environments which could be further explored for student engagement and empathy awareness as well as other elements of compassion, such as making a judgment about the suffering (the need to 10.3389/fpsyg.2022.971044 act).…”

Section: Safety and Clinical Effectiveness Of Ai Technologies (4 Arti...mentioning

confidence: 99%

“…← Immersive VR training on symptoms of disease(Jones et al, 2021) e.g., vignettes for Parkinson's disease(Hess et al, 2022), VR training for testicular disease(Jacobs and Maidwell-Smith, 2022;Saab et al, 2022) …”

mentioning

confidence: 99%

See 2 more Smart Citations

Artificial intelligence technologies and compassion in healthcare: A systematic scoping review

et al. 2023

View full text Add to dashboard Cite

BackgroundAdvances in artificial intelligence (AI) technologies, together with the availability of big data in society, creates uncertainties about how these developments will affect healthcare systems worldwide. Compassion is essential for high-quality healthcare and research shows how prosocial caring behaviors benefit human health and societies. However, the possible association between AI technologies and compassion is under conceptualized and underexplored.ObjectivesThe aim of this scoping review is to provide a comprehensive depth and a balanced perspective of the emerging topic of AI technologies and compassion, to inform future research and practice. The review questions were: How is compassion discussed in relation to AI technologies in healthcare? How are AI technologies being used to enhance compassion in healthcare? What are the gaps in current knowledge and unexplored potential? What are the key areas where AI technologies could support compassion in healthcare?Materials and methodsA systematic scoping review following five steps of Joanna Briggs Institute methodology. Presentation of the scoping review conforms with PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews). Eligibility criteria were defined according to 3 concept constructs (AI technologies, compassion, healthcare) developed from the literature and informed by medical subject headings (MeSH) and key words for the electronic searches. Sources of evidence were Web of Science and PubMed databases, articles published in English language 2011–2022. Articles were screened by title/abstract using inclusion/exclusion criteria. Data extracted (author, date of publication, type of article, aim/context of healthcare, key relevant findings, country) was charted using data tables. Thematic analysis used an inductive-deductive approach to generate code categories from the review questions and the data. A multidisciplinary team assessed themes for resonance and relevance to research and practice.ResultsSearches identified 3,124 articles. A total of 197 were included after screening. The number of articles has increased over 10 years (2011, n = 1 to 2021, n = 47 and from Jan–Aug 2022 n = 35 articles). Overarching themes related to the review questions were: (1) Developments and debates (7 themes) Concerns about AI ethics, healthcare jobs, and loss of empathy; Human-centered design of AI technologies for healthcare; Optimistic speculation AI technologies will address care gaps; Interrogation of what it means to be human and to care; Recognition of future potential for patient monitoring, virtual proximity, and access to healthcare; Calls for curricula development and healthcare professional education; Implementation of AI applications to enhance health and wellbeing of the healthcare workforce. (2) How AI technologies enhance compassion (10 themes) Empathetic awareness; Empathetic response and relational behavior; Communication skills; Health coaching; Therapeutic interventions; Moral development learning; Clinical knowledge and clinical assessment; Healthcare quality assessment; Therapeutic bond and therapeutic alliance; Providing health information and advice. (3) Gaps in knowledge (4 themes) Educational effectiveness of AI-assisted learning; Patient diversity and AI technologies; Implementation of AI technologies in education and practice settings; Safety and clinical effectiveness of AI technologies. (4) Key areas for development (3 themes) Enriching education, learning and clinical practice; Extending healing spaces; Enhancing healing relationships.ConclusionThere is an association between AI technologies and compassion in healthcare and interest in this association has grown internationally over the last decade. In a range of healthcare contexts, AI technologies are being used to enhance empathetic awareness; empathetic response and relational behavior; communication skills; health coaching; therapeutic interventions; moral development learning; clinical knowledge and clinical assessment; healthcare quality assessment; therapeutic bond and therapeutic alliance; and to provide health information and advice. The findings inform a reconceptualization of compassion as a human-AI system of intelligent caring comprising six elements: (1) Awareness of suffering (e.g., pain, distress, risk, disadvantage); (2) Understanding the suffering (significance, context, rights, responsibilities etc.); (3) Connecting with the suffering (e.g., verbal, physical, signs and symbols); (4) Making a judgment about the suffering (the need to act); (5) Responding with an intention to alleviate the suffering; (6) Attention to the effect and outcomes of the response. These elements can operate at an individual (human or machine) and collective systems level (healthcare organizations or systems) as a cyclical system to alleviate different types of suffering. New and novel approaches to human-AI intelligent caring could enrich education, learning, and clinical practice; extend healing spaces; and enhance healing relationships.ImplicationsIn a complex adaptive system such as healthcare, human-AI intelligent caring will need to be implemented, not as an ideology, but through strategic choices, incentives, regulation, professional education, and training, as well as through joined up thinking about human-AI intelligent caring. Research funders can encourage research and development into the topic of AI technologies and compassion as a system of human-AI intelligent caring. Educators, technologists, and health professionals can inform themselves about the system of human-AI intelligent caring.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Safety and Clinical Effectiveness Of Ai Technologies (4 Arti...mentioning

confidence: 99%

See 1 more Smart Citation

Artificial intelligence technologies and compassion in healthcare: A systematic scoping review

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Studies of willingness to serve older adults [ 10 , 13 , 17 , 24 , 32 , 33 ] have reported that through learning to interact with older adults, nursing students can observe older adults’ verbal and nonverbal cues, with such interaction increasing their willingness to serve and care for older adults. This study used VR learning materials, and the results revealed that through the VR simulation scenario, the opportunities for students to interact with older adults were increased.…”

Section: Discussionmentioning

confidence: 99%

Exploring Key Factors Influencing Nursing Students’ Cognitive Load and Willingness to Serve Older Adults: Cross-sectional Descriptive Correlational Study

Hsieh¹,

Wang²,

Huang³

2023

JMIR Serious Games

View full text Add to dashboard Cite

Background Virtual learning environments (VLEs) use a virtual environment to support learning activities. VLEs are commonly used to overcome the temporal and spatial restrictions of learning activities held in conventional face-to-face classrooms. In VLEs, students can participate in learning activities using the internet, and teachers can provide assistive learning tools during the process. Objective The purpose of this study was to investigate the relationships among nursing students’ mental load, cognitive load, and affective learning outcomes in terms of their willingness to serve older adults in an interaction-based educational virtual reality (VR) learning environment. Methods This study used a cross-sectional method. A total of 130 students participated in interaction-based VR learning and completed related questionnaires. Descriptive and inferential statistics and stepwise regression for data analysis were used. Results The research results revealed that in the dimension of willingness to use VR learning materials, perceived usefulness received the highest score (mean 4.42, SD 0.45). In the dimension of nursing ability, students scored the highest in information management and application ability to care for case patients (mean 4.35, SD 0.54). Correlation analysis revealed that cognitive load during learning and willingness to serve older adults were negatively correlated, whereas willingness to use VR learning materials was positively correlated with nursing ability and willingness to serve older adults. Analyzing the regression coefficients of predictor variables revealed that willingness to use VR learning materials (β=.23; t2=2.89, P=.005) and cognitive load during learning (β=–.35; t2=–.4.30, P<.001) were predictive factors of nursing students’ willingness to serve older adults. Conclusions This study demonstrated that students’ willingness to use VR learning materials and their cognitive load during learning affected their willingness to care for older adults. Therefore, the components of mental or cognitive load generate inconsistent predictive effects on affective variables and willingness to serve older adults.

show abstract

“…in buyer-supplier relations; Boyd and Koles, 2019) or the training of employees (e.g. communication with patients; Saab et al. , 2022).…”

Section: Ar- and Vr-enhanced Service Communicationmentioning

confidence: 99%

Exploring the frontiers in reality-enhanced service communication: from augmented and virtual reality to neuro-enhanced reality

Hilken

Chylinski

Ruyter

et al. 2022

JOSM

View full text Add to dashboard Cite

PurposeThe authors explore neuro-enhanced reality (NeR) as a novel approach for enhancing service communication between customers, frontline employees, and service organizations that extends beyond current state-of-the-art approaches based on augmented reality (AR) and virtual reality (VR) technologies.Design/methodology/approachThe authors first take stock of research on reality-enhanced service communication with AR and VR, then complement these insights with emerging neuroscientific research to conceptualize how NeR enables innovative forms of service communication. On this basis, the authors develop a research agenda to guide the future study and managerial exploitation of NeR.FindingsAR and VR already offer unique affordances for digital-to-physical communication, but these can be extended with NeR. Specifically, NeR supports neuro-to-digital and digital-to-neuro communication based on neuroimaging (e.g. controlling digital content through thought) and neurostimulation (e.g. eliciting brain responses based on digital content). This provides a basis for outlining possible applications of NeR across service settings.Originality/valueThe authors advance knowledge on reality-enhanced service communication with AR and VR, whilst also demonstrating how neuroscientific research can be extended from understanding brain activity to generating novel service interactions.

show abstract

Nursing students’ views of using virtual reality in healthcare: A qualitative study

Abstract: This is an open access article under the terms of the Creat ive Commo ns Attri butio n-NonCo mmerc ial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

Cited by 53 publications

References 56 publications

Artificial intelligence technologies and compassion in healthcare: A systematic scoping review

Artificial intelligence technologies and compassion in healthcare: A systematic scoping review

Exploring Key Factors Influencing Nursing Students’ Cognitive Load and Willingness to Serve Older Adults: Cross-sectional Descriptive Correlational Study

Exploring the frontiers in reality-enhanced service communication: from augmented and virtual reality to neuro-enhanced reality

Contact Info

Product

Resources

About