Motivated Down-Regulation of Emotion and Compassion Collapse Revisited

Hagman, William; Tinghög, Gustav; Dickert, Stephan; Slovic, Paul; Västfjäll, Daniel

doi:10.3389/fpsyg.2022.801150

Cited by 4 publications

(1 citation statement)

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“…Issues about the human cost of emotional labor ( Larson and Yao, 2005 ) are reflected in “compassionomics”: The study of the effects of compassionate healthcare for patients, healthcare systems, payers, and providers ( Trzeciak et al, 2017 ). This context also includes issues about the prevalence of workplace discrimination and violence in healthcare ( Greinacher et al, 2022 ), intention to leave ( Greinacher et al, 2022 ), COVID-19 related “compassion collapse” ( Hagman et al, 2022 ), as well as staff experiences of “compassion satisfaction” (enjoyment, reward, and passion for work) ( Okoli et al, 2020 ; Baqeas et al, 2021 ; Qu et al, 2022 ; Unjai et al, 2022 ). Other research has investigated “compassion inequalities,” which refers to differentials in patient treatment and care associated with stigmatized health conditions such as opioid use disorder ( Singh et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

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

et al. 2023

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Leveraging the Social Neuroscience of Prosocial Behavior to Advance Our Understanding of Pro-environmental Behavior

Lamm,

Wyss,

Brosch

et al. 2024

Environmental Neuroscience

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Revisiting the impact of singularity on the Identified Victim Effect: Replication and extension of Kogut and Ritov (2005a) Study 2

Majumder,

Tai,

Ziano

et al. 2024

Judgm. decis. mak.

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The identified victim effect is the phenomenon in which people tend to contribute more to identified than to unidentified victims. Kogut and Ritov (Journal of Behavioral Decision Making, 18(3), 157–167, 2005) found that the identified victim effect was limited to a single victim and driven by empathic emotions. In a pre-registered experiment with an online U.S. American MTurk sample on CloudResearch (N = 2003), we conducted a close replication and extension of Experiment 2 from Kogut and Ritov (Journal of Behavioral Decision Making, 18(3), 157–167, 2005). The replication findings failed to provide empirical support for the identified single victim effect hypothesis since we found no evidence of differences in willingness to contribute when comparing a single identified victim to a single unidentified victim (η2p = .00, 90% CI [0.00, 0.00]), and no indication for the target article’s interaction between singularity and identifiability (original: η2p = .062, 90% CI [0.01, 0.15]; replication: η2p = .00, 90% CI [0.00, 0.00]). Extending the replication to conduct a conceptual replication of Kogut and Ritov (Organizational Behavior and Human Decision Processes, 104(2), 150–157, 2007), we investigated a boundary condition of the effect—group belonging. We found support for an ingroup bias in helping behaviors and indications for empathic emotions and perceived responsibility contributing to this effect. We discuss differences between our study and the target article and implications for the literature on the identified victim effect.

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Motivated Down-Regulation of Emotion and Compassion Collapse Revisited

Cited by 4 publications

References 34 publications

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

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

Leveraging the Social Neuroscience of Prosocial Behavior to Advance Our Understanding of Pro-environmental Behavior

Revisiting the impact of singularity on the Identified Victim Effect: Replication and extension of Kogut and Ritov (2005a) Study 2

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