INSAFEDARE Project: Innovative Applications of Assessment and Assurance of Data and Synthetic Data for Regulatory Decision Support.

Digital health solutions hold promise for enhancing healthcare delivery and patient outcomes, primarily driven by advancements such as machine learning, artificial intelligence, and data science, which enable the development of integrated care systems. Techniques for generating synthetic data from real datasets are highly advanced and continually evolving. This paper aims to present the INSAFEDARE project's ambition regarding medical devices' regulation and how real and synthetic data can be used to check if devices are safe and effective. The project will consist of three pillars: a) assurance of new state-of-the-art technologies and approaches (such as synthetic data), which will support the validation methods as part of regulatory decision-making; b) technical and scientific, focusing on data-based safety assurance, as well as discovery, integration and use of datasets, and use of machine learning approaches; and c) delivery to practice, through co-production involving relevant stakeholders, dissemination and sustainability of the project's outputs. Finally, INSAFEDARE will develop an open syllabus and training certification for health professionals focused on quality assurance.

AI4Work Project: Human-Centric Digital Twin Approaches to Trustworthy AI and Robotics for Improved Working Conditions in Healthcare and Education Sectors.

AI and robotics aim to transform workplace landscapes in a several sectors such as manufacturing, logistics, healthcare, construction, agriculture, and education. Central to this evolution is the innovative use of Digital Twin technology, which creates real-time updated virtual replicas of physical systems and entities. This technology is especially transformative in healthcare and education, promising customized and efficient experiences for all involved. This paper outlines the AI4Work project's approach to leveraging Digital Twin Technology to improve work environments in these sectors. The goal of AI4Work is to formulate a workplace where AI and robots seamlessly collaborate with humans, while explores how to best share tasks between humans and machines in six different domains. For healthcare, AI4Work will explore how Digital Twin technology can assist occupational doctors and psychologists in monitoring the physical and mental health of hospital personnel in order to predict burnout symptoms and to create a sustainable working environment. In education, AI4Work will investigate how to uphold the mental health of both educators and students while fostering a more supportive and enduring educational setting.

Emotional intelligence protects nurses against quiet quitting, turnover intention, and job burnout.

Background: Emotional intelligence can improve nurses' interpersonal and coping skills, job performance, and resilience. However, there is a dearth in the literature on whether emotional intelligence affects levels of quiet quitting, turnover intention, and job burnout in nurses. Objective: We examined the relationship between emotional intelligence, quiet quitting, turnover intention, and job burnout. Methods: We conducted a cross-sectional study in Greece with a convenience sample of 992 nurses. We used the following valid tools to measure our study variables: the Trait Emotional Intelligence Questionnaire-Short Form, the Quiet Quitting Scale, and the single item burnout measure. Results: The mean age of our nurses was 42.2 years. After controlling for gender, age, work experience, shift work, and understaffed department, the multivariable linear regression models indicated significant negative relationships between emotional intelligence and quiet quitting, turnover intention, and job burnout. Specifically, self-control reduced detachment, lack of motivation, job burnout, and turnover intention. Moreover, emotionality reduced detachment, lack of motivation, and lack of initiative. Sociability reduced lack of initiative and lack of motivation, while well-being reduced lack of motivation, job burnout, and turnover intention. Conclusion: Emotional intelligence reduced quiet quitting, turnover intention, and job burnout in nurses. Therefore, nurse managers and policy-makers should apply interventions to optimize the emotional intelligence profiles of nurses.

Social Support and Resilience Are Protective Factors against COVID-19 Pandemic Burnout and Job Burnout among Nurses in the Post-COVID-19 Era.

Nurses have experienced several psychological and work-related issues during the COVID-19 pandemic, including pandemic burnout and job burnout. The aim of this study was to examine the impact of social support and resilience on COVID-19 pandemic burnout and job burnout among nurses. We conducted a cross-sectional study in Greece. The study population included 963 nurses. We measured social support, resilience, COVID-19 pandemic burnout, and job burnout with the Multidimensional Scale of Perceived Social Support, Brief Resilience Scale, COVID-19 Burnout Scale, and Single-Item Burnout Measure, respectively. Nurses received high levels of social support, while their resilience was moderate. Additionally, nurses experienced moderate levels of COVID-19 burnout and job burnout. Increased social support and increased resilience were associated with reduced COVID-19 pandemic burnout. We found a negative relationship between social support and job burnout. A similar negative relationship was found between resilience and job burnout. Social support and resilience can act as protective factors against COVID-19 pandemic burnout and job burnout among nurses. Policy makers should develop and implement appropriate strategies to improve nurses' social support and resilience since they are the backbone of healthcare systems worldwide.

Impact of Workplace Bullying on Quiet Quitting in Nurses: The Mediating Effect of Coping Strategies.

Workplace bullying is common among nurses and negatively affects several work-related variables, such as job burnout and job satisfaction. However, no study until now has examined the impact of workplace bullying on quiet quitting among nurses. Thus, our aim was to examine the direct effect of workplace bullying on quiet quitting and to investigate the mediating effect of coping strategies on the relationship between workplace bullying and quiet quitting in nurses. We conducted a cross-sectional study with a convenience sample of 650 nurses in Greece. We collected our data in February 2024. We used the Negative Acts Questionnaire-Revised, the Quiet Quitting Scale, and the Brief COPE to measure workplace bullying, quiet quitting, and coping strategies, respectively. We found that workplace bullying and negative coping strategies were positive predictors of quiet quitting, while positive coping strategies were negative predictors of quiet quitting. Our mediation analysis showed that positive and negative coping strategies partially mediated the relationship between workplace bullying and quiet quitting. In particular, positive coping strategies caused competitive mediation, while negative coping strategies caused complimentary mediation. Nurses' managers and policy makers should improve working conditions by reducing workplace bullying and strengthening positive coping strategies among nurses.

Intensive Care Unit Mortality Trends during the First Two Years of the COVID-19 Pandemic in Greece: A Multi-Center Retrospective Study.

Data on COVID-19 mortality among patients in intensive care units (ICUs) from Eastern and/or Southern European countries, including Greece, are limited. The purpose of this study was to evaluate the ICU mortality trends among critically ill COVID-19 patients during the first two years of the pandemic in Greece and to further investigate if certain patients' clinical characteristics contributed to this outcome. We conducted a multi-center retrospective observational study among five large university hospitals in Greece, between February 2020 and January 2022. All adult critically ill patients with confirmed COVID-19 disease who required ICU admission for at least 24 h were eligible. In total, 1462 patients (66.35% males) were included in this study. The mean age of this cohort was 64.9 (±13.27) years old. The 28-day mortality rate was 35.99% (n = 528), while the overall in-hospital mortality was 50.96% (n = 745). Cox regression analysis demonstrated that older age (≥65 years old), a body mass index within the normal range, and a delay in ICU admission from symptom onset, as well as worse baseline clinical severity scores upon ICU admission, were associated with a greater risk of death. Mortality of critically ill COVID-19 patients was high during the first two years of the pandemic in Greece but comparable to other countries. Risk factors for death presented in this study are not different from those that have already been described for COVID-19 in other studies.

Nurses quietly quit their job more often than other healthcare workers: An alarming issue for healthcare services.

AIM: To assess the level of quiet quitting among healthcare workers (HCWs) and identify possible differences between nurses, physicians, and other HCWs. We investigated the impact of sociodemographic variables, job burnout, and job satisfaction on quiet quitting levels. BACKGROUND: The quiet-quitting phenomenon is not new but has been frequently discussed during the COVID-19 pandemic. Interestingly, the level of quiet quitting among HCWs has not been measured yet. METHODS: We conducted a cross-sectional study with a convenience sample. We measured sociodemographic variables, job burnout, job satisfaction, and quiet quitting. We adhered to STROBE guidelines for cross-sectional studies. FINDINGS: Among our sample, 67.4% of nurses were quiet quitters, while the prevalence of quiet quitting for physicians and other HCWs was 53.8% and 40.3%, respectively. Multivariable linear regression analysis identified that the levels of quiet quitting were higher among nurses than physicians and other HCWs. Moreover, greater job burnout contributed more to quiet quitting, while less satisfaction implied more quiet quitting. HCWs who work in shifts and those working in the private sector experienced higher levels of quiet quitting. DISCUSSION: More than half of our HCWs were described as quit quitters. Levels of quiet quitting were higher among nurses. Job burnout and job dissatisfaction were associated with higher levels of quiet quitting. IMPLICATIONS FOR NURSING PRACTICE AND NURSING POLICY: Measurement of quiet quitting and identification of risk factors are essential to prevent or reduce quiet quitting levels among HCWs. Our study provides information on this field helping managers and organizations to identify quiet quitters within HCWs. Policymakers and managers should develop and implement interventions both at an organizational level and at an individual level.

Resilience and Social Support Improve Mental Health and Quality of Life in Patients with Post-COVID-19 Syndrome.

Physical and mental health problems among post-COVID-19 patients are common, even a year after infection. As there is no prior study available, we investigated the impacts of resilience and social support on anxiety, depression, and quality of life among patients with post-COVID-19 syndrome. We conducted a cross-sectional study with a convenience sample. The measures included the demographic and clinical characteristics of patients, the Brief Resilience Scale, the Multidimensional Scale of Perceived Social Support, the Patient Health Questionnaire-4 (PHQ-4), and the EuroQol-5D-3L. The mean age of patients was 44.8 years. The total PHQ-4 score suggested that 32.8% of patients with post-COVID-19 syndrome experienced severe psychological distress, 32.8% experienced moderate distress, 23% experienced mild distress, and 11.5% had no distress. Moreover, 60.7% of patients had anxiety scores of ≥3 and 69.7% had depression scores of ≥3, indicating possible major anxiety or depression disorder. The mean EQ-5D-3L index value was 0.36, and the mean EQ-5D-3L VAS was 54.1. Multivariable analysis identified that resilience and social support reduced anxiety and depression among patients. Also, we found a significant positive relationship between resilience and social support, and quality of life. Our findings suggest that resilience and social support can be protective by reducing anxiety and depression and improving quality of life among patients with post-COVID-19 syndrome. Policymakers should develop and implement healthcare management programs to provide psychological support to these patients.

Post-COVID-19 Syndrome and Related Dysautonomia: Reduced Quality of Life, Increased Anxiety and Manifestation of Depressive Symptoms: Evidence from Greece.

Background: Post-COVID-19 syndrome affects a significant number of SARS-CoV-2 infected individuals, even in asymptomatic cases causing several neurological and neuropsychiatric symptoms and signs. Materials and Methods: An online cross-sectional study with a convenience sample was conducted in Greece from November 2022 to January 2023. We measured the demographic and clinical characteristics of patients with post-COVID-19 dysautonomia in terms of the quality of life with the EQ-5D-3L, and anxiety and depressive symptoms by employing Patient Health Questionnaire-4. Results: The study population included 122 patients with post-COVID-19 syndrome. One out of four patients (27.8%) manifested post-COVID-19 dysautonomia, while the mean duration of COVID-19 symptoms was 11.6 months. Anxiety and depressive symptoms were worse after the post-COVID-19 syndrome (p<0.001 in both cases). A statistically significant reduction in quality of life was observed among patients after the post-COVID-19 syndrome (p<0.001 for both EQ-5D-3L index value and EQ-5D-3L VAS). Post-COVID-19 dysautonomia increased depression symptoms after developing the post-COVID-19 syndrome (p=0.02). We found a negative relationship between the duration of COVID-19 symptoms and the quality of life (p<0.001). Moreover, our results showed that depressive symptoms were more common among females after the post-COVID-19 syndrome (p=0.01). Also, the quality of life was lower among females than males (p=0.004 for EQ-5D-3L index value, and p=0.007 for EQ-5D-3L VAS). Conclusions: Our results suggest that post-COVID-19 syndrome causes a tremendous impact on the patients' quality of life and mental health. In addition, we found that the groups most psychologically affected were patients with post-COVID-19 dysautonomia, females, and patients with a longer duration of symptoms. Policy makers should assign priority rights to vulnerable groups in future psychiatric planning. Policy measures should focus on the mental health of post-COVID-19 patients who seem to be particularly vulnerable.

The quiet quitting scale: Development and initial validation.

Introduction: COVID-19 pandemic causes drastic changes in workplaces that are likely to increase quite quitting among employees. Although quiet quitting is not a new phenomenon, there is no instrument to measure it. Objective: To develop and validate an instrument assessing quiet quitting among employees. Methods: We identified and generated items through an extensive literature review and interviews with employees. We carried out the content validity by content experts and we calculated the content validity ratio. We checked face validity by conducting cognitive interviews with employees and calculating the item-level face validity index. We conducted exploratory and confirmatory factor analysis to investigate the quiet quitting scale (QQS) factorial structure. We checked the concurrent validity of the QQS using four other scales, i.e., Copenhagen burnout inventory (CBI), single item burnout (SIB) measure, job satisfaction survey (JSS) and a single item to measure turnover intention. We estimated the reliability of the QQS measuring Cronbach's alpha, McDonald's omega, Cohen's kappa and intraclass correlation coefficient. Results: After expert panel review and item analysis, nine items with acceptable corrected item-total correlations, inter-item correlations, floor and ceiling effects, skewness and kurtosis were retained. Exploratory factor analysis extracted three factors, namely detachment, lack of initiative and lack of motivation, with a total of nine items. Confirmatory factor analysis confirmed this factorial structure for QQS. We found statistically significant correlations between QQS and CBI, SIB, JSS and turnover intention confirming that the concurrent validity of the QQS was great. Cronbach's alpha and McDonald's omega of the QQS were 0.803 and 0.806 respectively. Conclusion: QQS, a three-factor nine-item scale, has robust psychometric properties. QQS is an easy-to-administer, brief, reliable and valid tool to measure employees' quiet quitting. We recommend the use of the QQS in different societies and cultures to assess the validity of the instrument.