Background: During COVID-19 pandemic, healthcare workers (HCWs) have had high workload and have been exposed to multiple psychosocial stressors. The aim of this study was to evaluate HCWs in terms of the relative contributions of socio-demographic and mental health variables on three burnout dimensions: personal, work-related, and client-related burnout. Methods: A cross-sectional study was performed using an online questionnaire spread via social networks. A snowball technique supported by health care institutions and professional organizations was applied. Results: A total of 2008 subjects completed the survey. Gender, parental status, marriage status, and salary reduction were found to be significant factors for personal burnout. Health problems and direct contact with infected people were significantly associated with more susceptibility to high personal and work-related burnout. Frontline working positions were associated with all three dimensions. Higher levels of stress and depression in HCWs were significantly associated with increased levels of all burnout dimensions. Higher levels of satisfaction with life and resilience were significantly associated with lower levels of all burnout dimensions. Conclusions: All three burnout dimensions were associated with a specific set of covariates. Consideration of these three dimensions is important when designing future burnout prevention programs for HCWs.

The heart-rate dynamics are one of the most analyzed physiological interactions. Many mathematical methods were proposed to evaluate heart-rate variability. These methods have been successfully applied in research to expand knowledge concerning the cardiovascular dynamics in healthy as well as in pathological conditions. Notwithstanding, they are still far from clinical practice. In this paper, we aim to review the nonlinear methods most used to assess heart-rate dynamics. We focused on methods based on concepts of chaos, fractality, and complexity: Poincaré plot, recurrence plot analysis, fractal dimension (and the correlation dimension), detrended fluctuation analysis, Hurst exponent, Lyapunov exponent entropies (Shannon, conditional, approximate, sample entropy, and multiscale entropy), and symbolic dynamics. We present the description of the methods along with their most notable applications.

During the COVID-19 pandemic, healthcare workers (HCW) have been exposed to multiple psychosocial stressors. Resilience might protect employees from the negative consequences of chronic stress. The aim of this study was to explore the mediating role of resilience in the relationship between depression and burnout (personal, work-related, and client-related). A cross-sectional study was performed using an online questionnaire distributed via social networks. A survey was conducted comprising standardized measures of resilience (Resilience Scale-25 items), depression (subscale of Depression Anxiety Stress Scales-21 items), and burnout (Copenhagen Burnout Inventory Scale-19 items). A total of 2008 subjects completed the survey, and a hierarchical regression model was estimated for each burnout dimension. The results revealed that depression had not only a directed effect on personal, work- and client-related burnout, but also an indirect small effect on it through resilience. Psychological resilience played a partial mediating role between depression and all burnout dimensions. This partial mediation suggests that there may be other possible variables (e.g., social connection, self-compassion, gratitude, sense of purpose) that further explain the associations.

Mental health effects secondary to the COVID-19 pandemic were till recently considered less important or were neglected. Portugal and Brazil are facing the pandemic in quite different ways. This study aimed to describe the mental health status of the general adult population in Portugal and Brazil during the COVID-19 pandemic and analyze the differences between the two countries. A cross-sectional quantitative study was based on an online questionnaire. Socio-demographic data were collected in addition to four validated scales: CAGE (acronym cut-annoyed-guilty-eye) Questionnaire, Satisfaction with Life Scale, Generalized Anxiety Disorder-7 and Patient Health Questionnaire-2. For each outcome, a multiple linear regression was performed. Five hundred and fifty people answered the questionnaire (435 women). The median age was 38 (Q1, Q3: 30, 47) years, 52.5% resided in Brazil and 47.5% in Portugal. The prevalence of anxiety was 71.3% (mild anxiety was present in 43.1%), the prevalence of depression was 24.7% and 23.8% of the sample had both depression and anxiety. Isolation was a significant factor for depression but not for anxiety. Well-being was below average. Mental illness was considerably higher than pre-COVID-19 levels. Portugal and Brazil will have to be prepared for future consequences of poor mental health and contribute immediate psychological support to their adult populations.

About 160 years ago, the concept of entropy was introduced in thermodynamics by Rudolf Clausius. Since then, it has been continually extended, interpreted, and applied by researchers in many scientific fields, such as general physics, information theory, chaos theory, data mining, and mathematical linguistics. This paper presents The Entropy Universe, which aims to review the many variants of entropies applied to time-series. The purpose is to answer research questions such as: How did each entropy emerge? What is the mathematical definition of each variant of entropy? How are entropies related to each other? What are the most applied scientific fields for each entropy? We describe in-depth the relationship between the most applied entropies in time-series for different scientific fields, establishing bases for researchers to properly choose the variant of entropy most suitable for their data. The number of citations over the past sixteen years of each paper proposing a new entropy was also accessed. The Shannon/differential, the Tsallis, the sample, the permutation, and the approximate entropies were the most cited ones. Based on the ten research areas with the most significant number of records obtained in the Web of Science and Scopus, the areas in which the entropies are more applied are computer science, physics, mathematics, and engineering. The universe of entropies is growing each day, either due to the introducing new variants either due to novel applications. Knowing each entropy’s strengths and of limitations is essential to ensure the proper improvement of this research field.

The COVID-19 pandemic has negatively affected the mental health of the general population, and for healthcare workers (HCWs) it has been no different. Religiosity and spirituality are known coping strategies for mental illnesses, especially in stressful times. This study aimed to describe the role of spiritual-religious coping regarding fear and anxiety in relation to COVID-19 in HCWs in Portugal. A cross-sectional quantitative online survey was performed. Socio-demographic and health data were collected as well as the Duke University Religion Index, Spirituality Scale, Fear of COVID-19 Scale, and Coronavirus Anxiety Scale. Two hundred and twenty-two HCWs participated in the study, 74.3% were female and 81.1% were physicians. The median age was 37 years (Q1, Q3: 31, 51.3). Religiosity was neither a significant factor for coronavirus-related anxiety nor it was for fear of COVID-19. Participants with higher levels in the hope/optimism dimension of the Spirituality Scale showed less coronavirus-related anxiety. Female HCWs, non-physicians, and the ones with a previous history of anxiety presented higher levels of fear and/or anxiety related to COVID-19. HCWs’ levels of distress should be identified and reduced, so their work is not impaired.