Analyzing the impact of COVID-19 on the electricity demand in Austin, TX using an ensemble-model based counterfactual and 400,000 smart meters.

The COVID-19 pandemic caused lifestyle changes and has led to the new electricity demand patterns in the presence of non-pharmaceutical interventions such as work-from-home policy and lockdown. Quantifying the effect on electricity demand is critical for future electricity market planning yet challenging in the context of limited smart metered buildings, which leads to limited understanding of the temporal and spatial variations in building energy use. This study uses a large scale private smart meter electricity demand data from the City of Austin, combined with publicly available environmental data, and develops an ensemble regression model for long term daily electricity demand prediction. Using 15-min resolution data from over 400,000 smart meters from 2018 to 2020 aggregated by building type and zip code, our proposed model precisely formalizes the counterfactual universe in the without COVID-19 scenario. The model is used to understand building electricity demand changes during the pandemic and to identify relationships between such changes and socioeconomic patterns. Results indicate the increase in residential usage , demonstrating the spatial redistribution of energy consumption during the work-from-home period. Our experiments demonstrate the effectiveness of our proposed framework by assessing multiple socioeconomic impacts with the comparison between the counterfactual universe and observations.

A cross-sectional study on burnout and its individual and environmental correlates among hepatological surgery nurses in Hunan Province, China.

BACKGROUND: Burnout is a widespread occupational phenomenon among nurses with significant adverse outcomes for nurses, patients, and society. It is thus important and urgent to understand burnout and its risk factors to guide interventions. This study aimed to examine the level of burnout and explore its individual and environmental correlates. METHODS: This cross-sectional study was conducted in Hunan, China. A total of 623 hepatological surgery nurses completed an online survey (response rate: 72.78%). Burnout was measured using the standard Maslach Burnout Inventory (MBI). Information on individual factors and environmental factors was collected by self-designed questionnaires. RESULTS: The scores of emotional exhaustion, depersonalization, and personal achievement in nurse burnout were 30 (26-34), 11 (8-14), and 23 (20-26) respectively. The prevalence of high burnout ranged from 52.81% for emotional exhaustion to 90.37% for decreased personal achievement. The three dimensions of burnout shared common correlates such as self-rated physical health and working environment, while also having additional unique correlates such as overwork, satisfaction with income, and age. CONCLUSION: Hepatological surgery nurses in Hunan Province are suffering from high levels of burnout, which requires public attention and urgent interventions. Improvement of the physical health and working environment of nurses may be the most beneficial intervention measures to tackle various dimensions of burnout, while other targeted measures are also needed for each specific dimension.

Role Stress and Psychological Distress Among Chinese Nurses During the COVID-19 Pandemic: A Moderated Mediation Model of Social Support and Burnout.

Objective: Nurses are at high risk of psychological distress including stress, depression, and anxiety due to low personnel density and high work demand. Despite mounting evidence showing that role stress is a risk factor for nurses' psychological distress, the mediating and moderating mechanisms underlying this relationship are less known. This study tests the mediation effect of burnout in the association between role stress and psychological distress, and whether this mediation is moderated by social support. Methods: A sample of 623 Chinese nurses were recruited from all hepatological surgery departments in Hunan Province and filled out an online questionnaire to collect data on socio-demographics, role stress, burnout, psychological distress, and social support. Mediation and moderation analyses were carried out in SPSS macro-PROCESS. Results: Burnout partially mediated the positive association between role stress and psychological distress. Social support moderated the indirect effect of role stress on psychological distress via burnout, with the effect being stronger for nurses with low social support than those with high social support. Conclusions: These findings demonstrated how role stress contributed to nurses' psychological distress both directly and indirectly through burnout, and how this indirect effect was moderated by social support. The results provide important practical implications for future prevention and intervention programs to improve nurses' mental health from multiple aspects such as decreasing role stress and burnout while increasing social support.