Preparing future health care workers for interactions with people with dementia: A mixed methods study

Torrence, Caitlin; Bhanu, Aasish; Bertrand, Jacques-Charles; Dye, Cheryl; Truong, Khoa; Madathil, Kapil Chalil

doi:10.1080/02701960.2022.2042805

Cited by 3 publications

(14 citation statements)

References 37 publications

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“…Multiple evaluation studies into the effects of immersive simulation for dementia suggest that an VR experience can simulate a range of aspects of dementia so that students can develop empathetic understanding ( Ball et al, 2015 ; Slater et al, 2019 ; Hirt and Beer, 2020 ; Sung et al, 2022 ). However, research in the US indicates that both VR and physical delivery formats of a dementia tour can be effective, but university students on healthcare courses ( n = 41) reported poorer attitudes about living with advanced dementia and feeling less prepared for caregiving in both approaches ( Torrence et al, 2022 ). Interesting research in Romania ( Groza et al, 2017 ) and Germany ( Schmidt et al, 2022 ) into the use of age simulation suits shows that “instant ageing” can generate more negative expectations regarding older age and reenforced stereotypes.…”

Section: Resultsmentioning

confidence: 99%

“…Multiple evaluation studies into the effects of immersive simulation for dementia suggest that an VR experience can simulate a range of aspects of dementia so that students can develop empathetic understanding (Ball et al, 2015;Slater et al, 2019;Hirt and Beer, 2020;Sung et al, 2022). However, research in the US indicates that both VR and physical delivery formats of a dementia tour can be effective, but university students on healthcare courses (n = 41) reported poorer attitudes about living with advanced dementia and feeling less prepared for caregiving in both approaches (Torrence et al, 2022). Interesting research in Romania (Groza et al, 2017) and Germany (Schmidt et al, 2022) ← Learning about anatomy and physiology of disease awareness using digital anatomy (Osis, 2021) ← Automated student skills assessment in pain assessment skills development (Moosaei et al, 2017) ← Automated patient health status and mood assessment (Yokoo et al, 2020) ← Automated assessment of Parkinson's disease (Sabo et al, 2022) (3) Connecting with the suffering (e.g., verbal, physical, signs, and symbols)…”

Section: Empathetic Awareness (15 Articles)mentioning

confidence: 99%

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

et al. 2023

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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.

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Section: Resultsmentioning

confidence: 99%

“…Multiple evaluation studies into the effects of immersive simulation for dementia suggest that an VR experience can simulate a range of aspects of dementia so that students can develop empathetic understanding (Ball et al, 2015;Slater et al, 2019;Hirt and Beer, 2020;Sung et al, 2022). However, research in the US indicates that both VR and physical delivery formats of a dementia tour can be effective, but university students on healthcare courses (n = 41) reported poorer attitudes about living with advanced dementia and feeling less prepared for caregiving in both approaches (Torrence et al, 2022). Interesting research in Romania (Groza et al, 2017) and Germany (Schmidt et al, 2022) ← Learning about anatomy and physiology of disease awareness using digital anatomy (Osis, 2021) ← Automated student skills assessment in pain assessment skills development (Moosaei et al, 2017) ← Automated patient health status and mood assessment (Yokoo et al, 2020) ← Automated assessment of Parkinson's disease (Sabo et al, 2022) (3) Connecting with the suffering (e.g., verbal, physical, signs, and symbols)…”

Section: Empathetic Awareness (15 Articles)mentioning

confidence: 99%

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

et al. 2023

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“…Fourteen studies (32%; [ 44 – 57 ]) were conducted in the USA, eleven (25%; [ 58 – 68 ]) in the UK, five in the Netherlands (11%; [ 42 , 69 – 72 ]), four in South Korea (9%; [ 73 – 76 ]), three in Australia (7%; [ 77 – 79 ]), two in Canada (5%; [ 41 , 43 ]), and one report (2%) in Ireland [ 80 ], Norway [ 81 ], Lithuania [ 82 ], China [ 83 ], and Taiwan [ 84 ], respectively.…”

Section: Resultsmentioning

confidence: 99%

“…The ‘Virtual Dementia Tour ® ’ (VDT ® ) was noted as another intervention programme in nine reports [ 46 , 48 , 52 , 56 , 57 , 66 – 68 , 80 ]. One report [ 57 ] referred to the VDT ® but outlined an adapted version tailored to national contexts. Trained facilitators were the most common interventionists [ 46 , 48 , 52 ], with some having prior experience in running the self-experience programme [ 56 ].…”

Section: Resultsmentioning

confidence: 99%

“…The practices can be delivered through various channels, including Virtual Reality goggles, screens, audio overlay, and other systems. Five reports [ 45 , 57 , 58 , 61 , 79 ] describe five different Virtual Reality programmes. Three of them [ 45 , 61 , 79 ] utilised smartphone apps (in combination with VR goggles) to create an immersive first-person simulation that allows participants to directly experience situations people with dementia may encounter.…”

Section: Resultsmentioning

confidence: 99%

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Exploring self-experience practices in dementia care: A scoping review

Wittmann,

Bieber,

Carroll

et al. 2024

PLoS ONE

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Background Recognised as essential for high-quality dementia service, person-centred care aims to understand and respect the unique needs of each individual. Self-experience practices may offer caregivers an opportunity to acquire knowledge, empathy, and skills related to person-centred care, especially through recreating experiences similar to dementia. Given the need to enhance the understanding of self-experience practices in dementia care, a more comprehensive investigation of these training interventions for (future) caregivers is needed. Methods We conducted a scoping review to map the evidence on the use of self-experience practices in dementia training. We systematically searched Cochrane Library, MEDLINE via PubMed, CINAHL, and Web of Science. We also searched for grey literature, as well as registry entries, and conducted backward citation tracking of included reviews. We analysed data on intervention characteristics, factors influencing the implementation, and learning outcomes based on Kirkpatrick’s model. Results We included 44 reports across 30 intervention programmes. The majority of reports (91%) were published from 2016 onwards, with 32% originating from the USA and 25% from the UK. We identified passive, interactive, immersive, and multicomponent self-experience interventions in dementia education and training. Learning outcomes based on Kirkpatrick’s model were fairly distributed across all identified modalities. Both consumers and providers emphasised aspects related to the development and implementation of practices, particularly organisational-related considerations such as temporal and spatial planning of trainings. Conclusions Our review highlights diverse interventions incorporating self-experience practices, with an increasing role for technological tools. While self-experience interventions engage participants, the impact on individuals with dementia and organisational levels remain largely unreported. Our overview, informed by current literature, underscores unique considerations and challenges associated with dementia-related self-experience practices. Implementing and evaluating complex training interventions using self-experience practices should consider ethical aspects. Trial registration Registry: Registered within the Open Science Framework (available at https://osf.io/fycxa/).

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Evaluating a virtual reality dementia training experience using psychophysiological methods: A randomised controlled study

Stafford,

Bender,

Parsons

et al. 2024

Australas J Ageing

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ObjectivesVirtual reality (VR) is increasingly used for training the dementia care workforce. It is unknown whether VR is superior to traditional training techniques in improving dementia care amongst practicing nurses. This study compared the impact of a VR application on nurses' knowledge and attitudes towards people living with dementia, to video‐based, non‐immersive training.MethodsTwenty‐two registered and enrolled nurses were randomised to either interactive VR experience or video footage captured from within the app. Participants completed surveys pre‐ and post‐training to assess their knowledge of dementia, attitudes towards dementia and person‐centredness. Engagement with training was assessed objectively using facial electromyography, and subjectively with self‐reported scales.ResultsVirtual reality evoked objectively significant greater positive and negative emotional responses than video (positive emotion fEMG: VR mean .012 mV vs. video .005 mV, F[1, 20] = 8.70, p = .01; negative emotion fEMG: VR mean .018 mV vs. video .008 mV, F[1, 20] = 18.40, p < .001). Self‐ratings of engagement and emotional state were similar. There was little change in the VR group's knowledge of, and attitudes towards, dementia; the video group's dementia knowledge improved (total DKAS mean differences: VR .1 t = .07, df = 9, p = .95 vs. video −2.3 t = −2.265, df = 11, p = .045).ConclusionsVirtual reality is more engaging than traditional training in highly experienced dementia care practitioners. Despite this, VR may not be superior to traditional training techniques to improve knowledge and attitude for many learners. A focus of future research in the area should be on how to capitalise on VR's greater emotional engagement so that Australia's nursing workforce is better equipped to care for the increasing number of people living with dementia.

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Preparing future health care workers for interactions with people with dementia: A mixed methods study

Cited by 3 publications

References 37 publications

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

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

Exploring self-experience practices in dementia care: A scoping review

Evaluating a virtual reality dementia training experience using psychophysiological methods: A randomised controlled study

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