The impact of nurses' sense of security on turnover intention during the normalization of COVID-19 epidemic: The mediating role of work engagement.

Background: COVID-19 pandemic has entered a normal stage in China. During this phase, nurses have an increased workload and mental health issues that threaten the sense of security. Poor sense of security may have a considerable impact on turnover intention through low work engagement. It was challenging to maintain the nurse workforce. Fewer studies have been conducted on the effect of nurses' sense of security on their turnover intention in that phase. This study aimed to investigate the interrelationship between nurses' sense of security, work engagement, and turnover intention during the normalization phase of the epidemic in China and to explore the impact of sense of security on turnover intention. Methods: A cross-sectional survey was conducted from September 2020 to May 2021 in Guangdong Province, China. Data were collected online using Sense of Security Scale for Medical Staff (SSS-MS), Utrecht Work Engagement Scale (UWES), and Turnover Intention Scale. Pearson's correlation analysis was used to assess the correlation between sense of security, work engagement, and turnover intention. The hypothesis model used multiple linear regression models and the bootstrapping procedure to analyze the relationship between these variables. Results: Data were collected from 2,480 nurses who met the inclusion criteria. Over half(64.5%) of nurses had a high and very high turnover intention. After controlling the demographic and working variables, sense of security (ß = 0.291, P < 0.001) had a direct positive effect on work engagement. Sense of security (ß = -0.447, P < 0.001) and work engagement (ß = -0.484, P < 0.001) had a direct negative effect on turnover intention. Sense of security and all of its components were associated with turnover intention through the partially mediating effects of work engagement. Conclusions: Nurses' turnover intention was at a high level during the normalization phase of the epidemic. Sense of security and its components act as positive resources to reduce turnover intention by improving work engagement. Policy makers and managers may pay attention to the needs of nurses' sense of security, which may be a new perspective to help managers reduce their turnover intention and stabilize the nurse team.

Application of a prediction model with laboratory indexes in the risk stratification of patients with COVID-19

In the present study, a prediction model with combined laboratory indexes in risk stratification of patients with COVID-19 was established and tested. The data of 170 patients with COVID-19 who were divided into an asymptomatic-moderate group (141 cases) and severe or above group (29 cases) were retrospectively analyzed. The clinical characteristics and laboratory indexes of the two groups were compared. Multivariate logistic regression analysis was performed to construct the prediction model based on laboratory indexes. A receiver operating characteristic (ROC) curve analysis was used to compare the diagnostic efficacy of different indexes. Decision curve analysis (DCA) was performed to quantify and compare the clinical validity of the prediction models. There were significant differences in blood cell count, high-sensitivity C-reactive protein (hsCRP) and procalcitonin (PCT) levels between the severe or above group and the asymptomatic-moderate group (all P<0.05). Among all individual indexes, hsCRP had the highest diagnostic efficacy (area under the curve=0.870), with a sensitivity and specificity of 0.828 and 0.802, respectively. The red blood cell count, hsCRP and PCT were used to construct the prediction model. The AUC of the prediction model was higher than that of hsCRP (0.912 vs. 0.870) but the difference was not significant (P=0.307). DCA suggested that the net benefit of the prediction model was higher than that of hsCRP in most cases and significantly higher than that of PCT, lymphocytes and monocytes. The prediction model with combined laboratory indexes was able to more effectively predict the clinical classification of patients with COVID-19 and may be used as a tool for risk stratification of patients. [ABSTRACT FROM AUTHOR] Copyright of Experimental & Therapeutic Medicine is the property of Spandidos Publications UK Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)