Mindful Self-Compassion (MSC) with Veterans: a Program Evaluation

Serpa, J. Greg; Bourey, Christine; Adjaoute, Ghislene N.; Pieczynski, Jessica

doi:10.1007/s12671-020-01508-1

Cited by 16 publications

(13 citation statements)

References 35 publications

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“…Compassion science mainly focuses on the bodily (psychological and neurobiological) and behavioral elements of compassion ( Kim et al, 2020 ; Goldberg, 2020 ) and the effects of oxytocin in the body ( Brown and Brown, 2015 ; Palgi et al, 2016 ; Seppälä et al, 2017 ). There is growing evidence about self-compassion and compassionate touch interventions ( Bond, 2002 ; Field, 2014 ; Serpa et al, 2021 ), self-care interventions ( Ehret et al, 2015 ; Friis et al, 2016 ; Brown et al, 2020 ), professionals’ self-care and self-compassion and compassion for others ( Mills et al, 2017 ); and resilience in caring roles ( Bleazard, 2020 ; Baqeas et al, 2021 ). Compassion is often used interchangeably with the notion of empathy ( Håkansson Eklund and Summer Meranius, 2021 ); previously defined as a person’s ability to sense another’s thoughts, feelings, and experiences, to share the other’s emotional experience, and to react to the observed experiences of another person ( Wieseke et al, 2012 ).…”

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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“…A meta-analysis which examined 27 randomized controlled trials of various self-compassion interventions found medium effect sizes for self-criticism, anxiety, depression, and stress (Ferrari et al, 2019). Mindful Self-Compassion , an 8-week program for adults, has shown post-intervention increases in happiness, life satisfaction, and decreases in anxiety, depression, fatigue, and stress (Friis et al, 2016; Neff & Germer, 2013; Serpa et al, 2021). Similarly, a study of the adaptation created for adolescents, Mindful Self-Compassion for Teens (MSC-T; formerly called Making Friends with Yourself) , reported decreases in negative affect, anxiety, depression, stress and increases in resilience post-intervention (Bluth & Eisenlohr-Moul, 2017; Bluth et al, 2016; Donovan et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Improving Mental Health Among Transgender Adolescents: Implementing Mindful Self-Compassion for Teens

Bluth

Lathren

Clepper-Faith

et al. 2021

Journal of Adolescent Research

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The purpose of this study was to investigate the feasibility, acceptability, and preliminary outcomes of an online self-compassion intervention for transgender adolescents, with the aim of improving mental health. Participants identified as transgender or gender expansive, were between the ages of 13 and 17, and lived in the U.S. or Canada. The empirically-based self-compassion program, Mindful Self-Compassion for Teens (formerly Making Friends with Yourself) was implemented in eight 1.5 hour sessions on the Zoom platform by two trained instructors. Surveys were administered pre-, post-intervention, and at 3 months follow-up, and qualitative data were collected through end-of-program interviews and open-ended questions on the post-survey. All protocols were approved by the university IRB. Quantitative data analysis included repeated measures ANOVAs, and qualitative data were analyzed via both inductive and deductive methods. Results indicated that all but one psychosocial measure significantly improved from pre- to post-intervention, which then significantly improved at 3-month follow-up; most other improvements were maintained at follow-up. Four themes emerged from the qualitative data: virtual safe space; connection to body; personal growth; and recommended course changes and are discussed. Results suggest that self-compassion interventions can be incorporated into therapy programs to support and improve mental health for transgender adolescents.

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“…Recent studies highlight the potential of self-compassionfocused interventions in the management of posttraumatic stress disorder (PTSD) (Serpa et al, 2021;Winders et al, 2020), which occurs following exposure to severe accidents, for example, actual or threatened death, domestic violence, war, or natural disaster (American Psychiatric Association, 2013). However, studies have demonstrated mixed effects and it is important to optimize the benefits of self-compassion-focused interventions on PTSD.…”

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confidence: 99%

Investigating the Influence of Self-Compassion-Focused Interventions on Posttraumatic Stress: A Systematic Review and Meta-Analysis

et al. 2021

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Objectives Self-compassion-focused interventions may be able to decrease posttraumatic stress symptoms. However, previous studies demonstrated mixed effects in which a series of confounders were not systematically quantified. In this study, a systematic review with meta-analysis was conducted to quantify the effects of self-compassion-focused therapies on posttraumatic stress disorder. Methods Twelve eligible studies were included after a systematic search of databases. Outcome measures were extracted for posttraumatic stress disorder. Results Our data indicated a medium protective effect on posttraumatic stress symptoms (SMD = − 0.65), with most of the studies (8/12) coming from clinical settings. More importantly, longer interventions were associated with better posttraumatic stress outcomes (p < 0.001). Baseline or changes in self-compassion scores were not associated with posttraumatic stress outcomes post-interventions. Conclusions Overall, findings from this meta-analysis quantified the complex influence of self-compassion-focused interventions on posttraumatic stress symptoms and may provide insights for optimizing intervention strategies.Systematic Review and Meta-analysis Registration: PROSPERO CRD42020208663.

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Mindful Self-Compassion (MSC) with Veterans: a Program Evaluation

Cited by 16 publications

References 35 publications

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

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

Improving Mental Health Among Transgender Adolescents: Implementing Mindful Self-Compassion for Teens

Investigating the Influence of Self-Compassion-Focused Interventions on Posttraumatic Stress: A Systematic Review and Meta-Analysis

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