The role of workplace social capital on the relationship between perceived stress and professional identity among clinical nurses during the COVID-19 outbreak.

AIM: Workplace social capital refers to relationship networks formed by individuals in an organization through long-term mutually beneficial interactions and cooperation with members. These relationship networks can create value and resources for organizations and individuals. This current study aimed to explore the potential impact of workplace social capital on the association between perceived stress and professional identity in clinical nurses during the COVID-19 outbreak. METHODS: In this cross-sectional study, 308 Chinese clinical nurses filled out the Chinese Workplace Social Capital Scale, the Chinese Perceived Stress Scale, and the Chinese Nurse's Professional Identity Scale. Descriptive analysis, independent samples t test, analysis of variance, Pearson correlation analyses, and bootstrap method were performed to analyze the data. RESULTS: Perceived stress was negatively correlated with professional identity (r = -0.455, p < .001). Workplace social capital was not found to moderate the relationship between perceived stress and professional identity (95% CI -0.03 to- 0.06, p = .47 > .05). Instead, it mediated that relationship (95% CI -0.61 to -0.19, p < .05), and its mediating effect was -0.37. CONCLUSIONS: In the early stages of the COVID-19 outbreak, workplace social capital among the investigated clinical nurses failed to buffer the negative impact of perceived stress on professional identity, but it did play a part in mediating perceived stress and professional identity. A healthy workplace should be provided to clinical nurses to improve their professional identity, while lowering perceived stress.

Work stress among Chinese nurses to support Wuhan in fighting against COVID-19 epidemic.

AIMS: To investigate the work stress among Chinese nurses who are supporting Wuhan in fighting against Coronavirus Disease 2019 (COVID-19) infection and to explore the relevant influencing factors. BACKGROUND: The COVID-19 epidemic has posed a major threat to public health. Nurses have always played an important role in infection prevention, infection control, isolation, containment and public health. However, available data on the work stress among these nurses are limited. METHODS: A cross-sectional survey. An online questionnaire was completed by 180 anti-epidemic nurses from Guangxi. Data collection tools, including the Chinese version of the Stress Overload Scale (SOS) and the Self-rating Anxiety Scale (SAS), were used. Descriptive single factor correlation and multiple regression analyses were used in exploring the related influencing factors. RESULTS: The SOS (39.91 ± 12.92) and SAS (32.19 ± 7.56) scores of this nurse group were positively correlated (r = 0.676, p < .05). Multiple regression analysis showed that only children, working hours per week and anxiety were the main factors affecting nurse stress (p = .000, .048, .000, respectively). CONCLUSIONS: Nurses who fight against COVID-19 were generally under pressure. IMPLICATIONS FOR NURSING MANAGEMENT: Nurse leaders should pay attention to the work stress and the influencing factors of the nurses who are fighting against COVID-19 infection, and offer solutions to retain mental health among these nurses.

Application of the ARIMA model on the COVID-2019 epidemic dataset.

Coronavirus disease 2019 (COVID-2019) has been recognized as a global threat, and several studies are being conducted using various mathematical models to predict the probable evolution of this epidemic. These mathematical models based on various factors and analyses are subject to potential bias. Here, we propose a simple econometric model that could be useful to predict the spread of COVID-2019. We performed Auto Regressive Integrated Moving Average (ARIMA) model prediction on the Johns Hopkins epidemiological data to predict the epidemiological trend of the prevalence and incidence of COVID-2019. For further comparison or for future perspective, case definition and data collection have to be maintained in real time.