Optimism and Its Impact on Mental and Physical Well-Being

(1) Background: The present study aims to assess the level of professional burnout and secondary traumatic stress (STS), and to identify potential risk or protective factors among health care workers (HCWs) during the coronavirus disease 2019 (COVID-19) outbreak.; (2) Methods: This cross-sectional study, based on an online survey, collected demographic data and mental distress outcomes from 184 HCWs from 1 May 2020, to 15 June 2020, from 45 different countries. The degree of STS, perceived stress and burnout was assessed using the Secondary Traumatic Stress Scale (STSS), the Perceived Stress Scale (PSS) and Maslach Burnout Inventory Human Service Survey (MBI-HSS) respectively. Stepwise multiple regression analysis was performed to identify potential risk and protective factors for STS; (3) Results: 184 HCWs (M = 90; Age mean: 46.45; SD: 11.02) completed the survey. A considerable proportion of HCWs had symptoms of STS (41.3%), emotional exhaustion (56.0%), and depersonalization (48.9%). The prevalence of STS was 47.5% in frontline HCWs while in HCWs working in other units it was 30.3% (p < 0.023); 67.1% for the HCWs exposed to patients’ death and 32.9% for those HCWs which were not exposed to the same condition (p < 0.001). In stepwise multiple regression analysis, perceived stress, emotional exhaustion, and exposure to patients’ death remained as significant predictors in the final model for STS (adjusted R2 = 0.537, p < 0.001); (4) Conclusions: During the current COVID-19 pandemic, HCWs facing patients’ physical pain, psychological suffering, and death are more likely to develop STS.

Recent controversies about the level of replicability of behavioral research analyzed using statistical inference have cast interest in developing more efficient techniques for analyzing the results of psychological experiments. Here we claim that complementing the analytical workflow of psychological experiments with Machine Learning-based analysis will both maximize accuracy and minimize replicability issues. As compared to statistical inference, ML analysis of experimental data is model agnostic and primarily focused on prediction rather than inference. We also highlight some potential pitfalls resulting from adoption of Machine Learning based experiment analysis. If not properly used it can lead to over-optimistic accuracy estimates similarly observed using statistical inference. Remedies to such pitfalls are also presented such and building model based on cross validation and the use of ensemble models. ML models are typically regarded as black boxes and we will discuss strategies aimed at rendering more transparent the predictions.

The experience of working on the frontlines of the COVID-19 healthcare crisis has presented a cumulative traumatic experience that affects healthcare professionals’ well-being. Psychological resources such as resilience and adaptive defense mechanisms are essential in protecting individuals from severe stress and burnout. During September 2020, 233 healthcare workers responded to an online survey to test the impact of demographic variables, COVID-19 exposure, and psychological resources in determining stress and burnout during the COVID-19 emergency. Frontline workers reported higher scores for stress, emotional exhaustion, and depersonalization (p < 0.001) as compared to colleagues working in units not directly serving patients with COVID-19. Mature defensive functioning was associated with resilience and personal accomplishment (r = 0.320; p < 0.001), while neurotic and immature defenses were related to perceived stress and burnout. Stress and burnout were predicted by lower age, female gender, greater exposure to COVID-19, lower resilience, and immature defensive functioning among healthcare professionals (R² = 463; p < 0.001). Working on the frontlines of the COVID-19 pandemic appears to provoke greater stress and burnout. On the other hand, resilience and adaptive defense mechanisms predicted better adjustment. Future reaction plans should promote effective programs offering support for healthcare workers who provide direct care to patients with COVID-19.

Background: Hypertension is among the most important risk factors for cardiovascular diseases, which are considered high mortality risk medical conditions. To date, several studies have reported positive effects of mindfulness-based stress reduction (MBSR) interventions on physical and psychological well-being in other medical conditions, but no meta-analysis on MBSR programs for hypertension has been conducted. Objectives: The objective of this study was to determine the effectiveness of MBSR programs for hypertension. Methods: A systematic review and meta-analysis of randomized controlled trials examining the effects of MBSR on systolic and diastolic blood pressure (BP), anxiety, depression, and perceived stress in people with hypertension or pre-hypertension was conducted. The PubMed/MEDLINE and PsycINFO databases were searched in November 2020 to identify relevant studies. Results: Six studies were included. The comparison of MBSR versus control conditions on diastolic BP was associated with a statistically significant mean effect size favoring MBSR over control conditions (MD = −2.029; 95% confidence interval (CI): −3.676 to −0.383, p = 0.016, k = 6; 22 effect sizes overall), without evidence of heterogeneity (I² = 0.000%). The comparison of MBSR versus control conditions on systolic BP was associated with a mean effect size which was statistically significant only at a marginal level (MD = −3.894; 95% CI: −7.736–0.053, p = 0.047, k = 6; 22 effect sizes overall), without evidence of high heterogeneity (I² = 20.772%). The higher the proportion of participants on antihypertensive medications was, the larger the effects of MBSR were on systolic BP (B = −0.750, z = −2.73, p = 0.003). Conclusions: MBSR seems to be a promising intervention, particularly effective on the reduction of diastolic BP. More well-conducted trials are required.