Sources of Care Stress of Nursing Staff for Patients with Infectious Diseases during the Prevalence of COVID-19: A Case Study of Some Regional Teaching Hospitals in Southern Taiwan.

(1) Background: The COVID-19 epidemic had caused more than 100 million confirmed cases worldwide by the end of January 2021. The focus of this study was to explore which stress was felt the most by nursing staff in isolation wards in the face of dangerous infectious diseases. (2) Methods: Nursing staff in negative pressure isolation wards were taken as the research objects. The sources of stress were divided into 14 items in three categories, namely, patient care, infection protection, and support system, and the questionnaire results were ranked by a Gaussian curve. (3) Results: Even during the COVID-19 epidemic, nurses in isolation wards still consider that the clinical symptoms of patients in isolation wards cannot be closely tracked as the primary consideration. (4) Conclusions: During the epidemic period, the ability and confidence of nursing staff were strengthened through education and training, and their chances of infection were reduced through comprehensive vaccination and the improvement of protective equipment. In the face of the unstable mood of patients and their families due to isolation, more protective measures should be prepared for nursing staff. In order to relieve the stress, supervisors can adjust the nursing manpower timely according to the difficulty and risk of patient care to reduce the care stress.

COVID-19: Nothing is Normal in this Pandemic.

This manuscript brings attention to inaccurate epidemiological concepts that emerged during the COVID-19 pandemic. In social media and scientific journals, some wrong references were given to a "normal epidemic curve" and also to a "log-normal curve/distribution". For many years, textbooks and courses of reputable institutions and scientific journals have disseminated misleading concepts. For example, calling histogram to plots of epidemic curves or using epidemic data to introduce the concept of a Gaussian distribution, ignoring its temporal indexing. Although an epidemic curve may look like a Gaussian curve and be eventually modelled by a Gauss function, it is not a normal distribution or a log-normal, as some authors claim. A pandemic produces highly-complex data and to tackle it effectively statistical and mathematical modelling need to go beyond the "one-size-fits-all solution". Classical textbooks need to be updated since pandemics happen and epidemiology needs to provide reliable information to policy recommendations and actions.