Contemplating Mindfulness at Work: An Integrative Review

Mindfulness research activity is surging within organizational science. Emerging evidence across multiple fields suggests that mindfulness is fundamentally connected to many aspects of workplace functioning, but this knowledge base has not been systematically integrated to date. This review coalesces the burgeoning body of mindfulness scholarship into a framework to guide mainstream management research investigating a broad range of constructs. The framework identifies how mindfulness influences attention, with downstream effects on functional domains of cognition, emotion, behavior, and physiology. Ultimately, these domains impact key workplace outcomes, including performance, relationships, and well-being. Consideration of the evidence on mindfulness at work stimulates important questions and challenges key assumptions within management science, generating an agenda for future research

Expanding the occupational health psychology methodology: an artificial neural network approach

Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Facultad de Psicología, Departamento de Psicología Social y Metodología. Fecha de Lectura: 22-01-202

Evaluation of service quality using SERVQUAL scale and machine learning algorithms: a case study in health care

Purpose This study aims to propose a service quality evaluation model for health care services. Design/methodology/approach In this study, a service quality evaluation model is proposed based on the service quality measurement (SERVQUAL) scale and machine learning algorithm. Primarily, items that affect the quality of service are determined based on the SERVQUAL scale. Subsequently, a service quality assessment model is generated to manage the resources that are allocated to improve the activities efficiently. Following this phase, a sample of classification model is conducted. Machine learning algorithms are used to establish the classification model. Findings The proposed evaluation model addresses the following questions: What are the potential impact levels of service quality dimensions on the quality of service practically? What should be prioritization among the service quality dimensions and Which dimensions of service quality should be improved primarily? A real-life case study in a public hospital is carried out to reveal how the proposed model works. The results that have been obtained from the case study show that the proposed model can be conducted easily in practice. It is also found that there is a remarkably high-service gap in the public hospital, in which the case study has been conducted, regarding the general physical conditions and food services. Originality/value The primary contribution of this study is threefold. The proposed evaluation model determines the impact levels of service quality dimensions on the service quality in practice. The proposed evaluation model prioritizes service quality dimensions in terms of their significance. The proposed evaluation model finds out the answer to the question of which service quality dimensions should be improved primarily

Intelligent Systems for Sustainable Person-Centered Healthcare

This open access book establishes a dialog among the medical and intelligent system domains for igniting transition toward a sustainable and cost-effective healthcare. The Person-Centered Care (PCC) positions a person in the center of a healthcare system, instead of defining a patient as a set of diagnoses and treatment episodes. The PCC-based conceptual background triggers enhanced application of Artificial Intelligence, as it dissolves the limits of processing traditional medical data records, clinical tests and surveys. Enhanced knowledge for diagnosing, treatment and rehabilitation is captured and utilized by inclusion of data sources characterizing personal lifestyle, and health literacy, and it involves insights derived from smart ambience and wearables data, community networks, and the caregivers’ feedback. The book discusses intelligent systems and their applications for healthcare data analysis, decision making and process design tasks. The measurement systems and efficiency evaluation models analyze ability of intelligent healthcare system to monitor person health and improving quality of life

Towards Consumer 4.0 Insights and Opportunities under the Marketing 4.0 Scenario

This Research Topic is a sequel to our previous Research Topic “From Consumer Experience to Affective Loyalty: Challenges and Prospects in the Psychology of Consumer Behavior 3.0”. This first article collection was devoted to analyze the changes that appeared in different industries and companies, fostered by factors mainly related to the development of technologies. The evolution from consumer 3.0 to consumer 4.0 represents an opportunity to feature the changes that have been occurring lately as well as to gain an insight into the future of consumer behavior. Nowadays, the markets are experiencing several transformations in consumer behavior. These changes have been fueled by several trends: processes of globalization that produced an extraordinary assortment of diverse products and brand alternatives, new business models based on the intensive use of technology advances in communication and mobile technologies that allow customers’ capacity to easily participating in co-creation processes with companies; and big data developments. In this scenario, customers acquired more power than ever before due to their availability of information required to choose among the better priced alternatives product-brand options, as well as the technological means to access to such alternatives. Thus, customers evolved from a position to simply receiving the offer proposed by companies, to a position of power where they had the last word in the decision process, that is, the position of consumer 3.0. These consumers were characterized by their ability to adopt and use new technologies to meet their individual needs. What is more, these types of consumers did not longer easily respond to traditional mass marketing techniques. Instead, this generation of consumers demanded a highly customized approach across all facets of businesses including new product development, communication and customer service, among others. Nevertheless, in the advent of Marketing 4.0, a new type of consumer is observed, namely the customer 4.0. The transition from consumer 3.0 to consumer 4.0 is becoming evident, not only in consumers’ behavior but also in companies’ behavior. Related to the first one, consumers 4.0 are hyper-connected through different technologies, including not only the well-known mobile or digital technologies, but also other type of technologies, such as IoT, nanotech or artificial intelligence. Hence, their behavior is characterized by the demand of technology that have integrated the facets of Marketing 4.0 such as geolocation, marketing virtual and augmented reality facets. Regarding the second one, companies should face a digital transformation affecting not only value areas, but also, the way business interact with the environment. In particular, companies need to incorporate systems and applications that allow them to collect and analyze information, while helping decision making, since in the long run these issues constitute the cornerstone on which to start building a successful marketing strategy 4.0. This Research Topic welcomes scientific papers that covers the following topics (but not limited exclusively): - Consumers’ 4.0. behavior in different countries, industries, products, brands, etc.; - Digital transformations of industries and companies due to new consumption patterns; - New devices launched by companies work to meet the demands of consumer 4.0 (e.g., IoT), as well as the use consumers make of such devices; - The latest technology trends in business areas that make easier the consumer-companies relationships (processing, communication or any other digital technologies)

Human experience in the natural and built environment : implications for research policy and practice

22nd IAPS conference. Edited book of abstracts. 427 pp. University of Strathclyde, Sheffield and West of Scotland Publication. ISBN: 978-0-94-764988-3

How are hospitals using artificial intelligence in strategic decision making? —a scoping review

Artificial intelligence (AI) is a useful tool for clinical decision-making in hospitals, and for strategic decision-making in other industries. This scoping review provides a comprehensive review of the potential for AI to improve strategic decision-making in hospitals by exploring current applications of AI in this area. Peer-reviewed publications and conference presentations associated with AI for strategic decision-making were identified in Health Administration, Computer Science and Business and Management databases to answer the research question; how are hospitals using AI in strategic decision-making? The review found 19 published AI applications for hospital strategic decision-making. The applications used a variety of knowledge-based, probabilistic reasoning and data-driven AI, that generally followed the course of AI maturity. They focused on specific decisions, with none providing a comprehensive framework for strategic decision-making drawing on existing enterprise- or system-wide data. There was little evidence of evaluation of the AI applications, with no cost-benefit evaluation. The scoping review suggests the need for substantial improvement in the understanding of AI and its application among hospital decision-makers leading to greater organisational maturity. This would suggest that journals and researchers require evaluative and economic research and that training to improve understanding of AI be provided for board members, managers and clinicians

Intelligent Systems for Sustainable Person-Centered Healthcare

This open access book establishes a dialog among the medical and intelligent system domains for igniting transition toward a sustainable and cost-effective healthcare. The Person-Centered Care (PCC) positions a person in the center of a healthcare system, instead of defining a patient as a set of diagnoses and treatment episodes. The PCC-based conceptual background triggers enhanced application of Artificial Intelligence, as it dissolves the limits of processing traditional medical data records, clinical tests and surveys. Enhanced knowledge for diagnosing, treatment and rehabilitation is captured and utilized by inclusion of data sources characterizing personal lifestyle, and health literacy, and it involves insights derived from smart ambience and wearables data, community networks, and the caregivers’ feedback. The book discusses intelligent systems and their applications for healthcare data analysis, decision making and process design tasks. The measurement systems and efficiency evaluation models analyze ability of intelligent healthcare system to monitor person health and improving quality of life

Compassionate mind training and its relationship with perceived stress, poor mental health, self-compassion and benevolence

Background: University students and employees are two populations where stress-related problems, anxiety, and depression are increasing. Interventions must be found to reverse this trajectory and to improve mental health. Research on self-compassion and compassion training as a strategy to handle high levels of perceived stress, depression, and anxiety has increased internationally during the last years. Accessibility to new effective health programs is important to spur the development within this field of psychology and mental health forward. Further, testing different delivery formats is important both in regard to accessibility as well as cost and time efficiency. Aims: The purpose of this thesis was to develop a compassionate mind training intervention and examine its effects on mental health and stress-related problems in two groups of adults (university students and employees). Furthermore, the prosocial concept of benevolence was measured to increase the understanding of the concept and its relation to self-compassion, anxiety, depression, perceived stress, and emotional exhaustion. Four studies were performed. The aim of the first study was to evaluate the effects of compassionate mind training on stress-related problems in university students compared with affect-focused training. Study II’s aim was to evaluate the effect of compassionate mind training provided by a digital mental health solution using a smartphone application on stress-related problems in university students compared with an active control group following a mindfulness program using a smartphone application and a passive waitlist control group, respectively. In study III, the aim was to evaluate the effect of compassionate mind training on stress-related problems among employees in two work organizations compared with physical exercise. Study IV investigated the associations between the psychological concepts of benevolence and stress, mental ill-health, and self-compassion among employees. Study I: Comparing the effects of compassionate mind training to an affect-focused training on university students with self-defined high levels of stress. Study II: Comparing the effects of digital compassionate mind training to an active control consisting of digital mindfulness training and a passive waitlist control on university students with stress-related problems. Study III: Comparing the effects of compassionate mind training to physical exercise on employees with self-defined high perceived stress in two organizations. Study IV: Investigating the associations between the psychological concept of benevolence and perceived stress, mental ill-health (e.g., emotional exhaustion, anxiety, and depression symptoms), satisfaction with life, and self-compassion among employees in two datasets. Methods: A compassionate mind training program was developed and evaluated in randomized controlled trials (studies I, II and III) and the data were analyzed by mixed effects models. Study IV used a cross-sectional design. An informed consent form was filled out by all participants in each study. Study I included 55 Swedish university students (mean age = 26) randomized to compassionate mind training (n = 28) and affect-focused training (n = 27). Assessment was done at pre- and posttraining evaluating participants’ self-reports on a self-compassion scale short form (SCS-SF), hospital anxiety and depression scale (HADS), and perceived stress scale (PSS-14). Mixed-effects regression models were used to analyze data. Study II included 57 Swedish university students (mean age = 25) who were randomized to digitally provided digital compassionate mind training (n = 23), digital mindfulness training (n = 19), and a waitlist (n = 15). The primary outcomes involved the perceived stress scale (PSS-10) and self-compassion scale short-form (SCS-SF), and secondary outcomes involved the Toronto alexithymia scale (TAS-20) and the clinical outcomes in routine evaluation-outcome measure (CORE-OM). Data were analyzed with multilevel growth models that provide advantages when analyzing repeated measures data from randomized between-group design. Study III included 49 employees from two work organizations who were randomized to compassionate mind training (n = 25) and physical exercise (n = 24). The participants filled in a self-report on the self-compassion scale (SCS), the perceived stress scale (PSS-14), the hospital anxiety and depression scale (HADS) and the satisfaction with life scale (SWLS). Mixed-effect growth models were applied to analyze the data. Study IV consisted of two cross sectional studies including 571 employees based on two dataset from five work organizations and examined the association between a new measure of the concept of a benevolence scale (BS) and self-report measures of a perceived stress scale (PSS-14) and emotional exhaustion (MBI-EE), symptom checklist, core depression subscale (SCL-CD6), The hospital anxiety and depression scale (HADS), the self- compassion scale (SCS), and the satisfaction with life scale (SWLS). Data were analyzed using bivariate Pearson r correlations. Results: The results of study I showed that compassionate mind training and the affect- focused training did differ significantly on the outcome measures of depression (p = 0.02) but not on the other measures of self-compassion, perceived stress, and anxiety. Study II found no significant effects between the mindfulness group and the compassionate mind training group. However, both digitally provided compassionate mind training and mindfulness training increased self‐compassion (p < 0.001) and decreased alexithymia (p = 0.01), respectively, compared to the waitlist. Only compassionate mind training significantly reduced stress (p = 0.027) compared to waitlist. No significant effect was found on global psychological distress (p = 0.227) in any of the groups. Results of study III showed that compassionate mind training and the physical exercise did differ significantly on the outcome measure of self-compassion (p = 0.03) but not on any of the other measures: perceived stress, anxiety, depression, and satisfaction with life. In study IV, results showed that benevolence was significantly and negatively correlated with perceived stress (r = −0.392), depression symptoms checklist (r = −0.190) depression correlated with self-compassion (0.401). However, benevolence was not significantly associated with either satisfaction with life (r = 0.148) or anxiety (r = −0.199). Conclusions: Compassionate mind training delivered both in a group setting and using a smartphone application showed weak results in the included studies. Reasons for this could depend on various factors such as low statistical power due to small group sizes, or that the compassionate mind training intervention is not an effective method compared to the active control groups: affect-focused training, mindfulness, or physical exercise. The results could also depend on low baseline values on the outcome measures which does not give room for improvements. It shows that compassionate mind training can be effective using a smartphone to train self-compassion and decrease perceived stress, anxiety and depression symptoms compared to a waitlist. It was observed that self-assessed benevolence was symptoms (r = −0.310) emotional exhaustion (r = - −0.295) and significantly and positively associated weakly with emotional exhaustion and depression, and moderate associated with perceived stress and self-compassion but the finding have low statistical value due to the cross-sectional design. The compassionate mind training studied in the current thesis showed minimal or no effects on mental health measured as perceived stress, anxiety, depression, and self-compassion on the populations of university students and employees. More robust studies need to be conducted with larger samples. Future studies should preregister the plan for statistical analysis and have a careful screening procedure of the participants as well as a strategy for adherence to prevent and avoid attrition. Also, long-term follow up and mixed-method studies are needed to further evaluate the impact of compassionate mind training, investigating when, how, and for whom compassionate mind training is beneficial, as well as the role of benevolence in stress, mental health, and self-compassion