ABSTRACT
Introduction: Healthcare workers, including physical therapists, have some of the most important roles in the healthcare system, as observed during the coronavirus disease 2019 pandemic. Physical therapists encounter emotionally and physically vulnerable patients, experience emotional labor, and are exposed to conditions that can lead to job stress and musculoskeletal disorders. We aimed to examine the relationships between physical therapists' emotional labor, its effect on perceived job stress, and the risk of developing musculoskeletal disorders. Methods: We conducted a 30-day survey among 230 physical therapists working in various settings from October 2 to November 1, 2019. Questionnaires, including questions on musculoskeletal symptoms, perceived job stress, and emotional labor, were administered to the participants. Results: The emotional labor sub-factors "overload and conflict in customer service" (β=0.201, p>0.001), "emotional inconsistency and impairment" (β=0.199, p>0.001), and "organizational support and protection system" (β=0.298, p>0.001) affected the job stress sub-factors "physical environment" (β=0.105, p>0.020), "insufficient compensation" (β=0.072, p<0.05), and "relational conflict" (β=-0.083, p>0.024). These job stress sub-factors affected musculoskeletal disorders. Conclusion: To prevent the long-term consequences of work-related strain, physical therapists should receive support for maintaining a healthy lifestyle and developing effective methods of communication with patients. Encouragement of activities for psychological rejuvenation and sharing emotional difficulties with colleagues is also desirable. Moreover, it is necessary to establish a direct line of grievance communication between physical therapists to hospitals. © 2023 The Author(s).
ABSTRACT
Improving load forecasting is becoming increasingly crucial for power system management and operational research. Disruptive influences can seriously impact both the supply and demand sides of power. This work examines the impact of the coronavirus on power usage in two US states from January 2020 to December 2020. A wide range of machine learning (ML) algorithms and ensemble learning are employed to conduct the analysis. The findings showed a surprising increase in monthly power use changes in Florida and Texas during the COVID-19 pandemic, in contrast to New York, where usage decreased over the same period. In Texas, the quantity of power usage rises from 2% to 6% practically every month, except for September, when it decreased by around 1%. For Florida, except for May, which showed a fall of roughly 2.5%, the growth varied from 2.5% to 7.5%. This indicates the need for more extensive research into such systems and the applicability of adopting groups of algorithms in learning the trends of electric power demand during uncertain events. Such learning will be helpful in forecasting future power demand changes due to especially public health-related scenarios. © 2023 Elsevier Ltd