Nurses exposed to coronavirus disease 2019 (COVID‐19) are psychologically stressed. This study examines the characteristics and distribution of negative emotions among Chinese nurses during the COVID‐19 pandemic and explores regulatory emotional self‐efficacy (RESE) as the underlying mechanism in the relationship between nurses' personalities and negative emotions. A cross‐sectional design with convenience sampling was utilized. Three comprehensive tertiary hospitals located in China were selected. Nurses (n = 339) who cared for COVID‐19 patients were enrolled. Recruitment was conducted between 14 February 2020 and 1 March 2020. Self‐reported questionnaires about personality, RESE, and epidemic‐related negative emotions were completed online. A correlation analysis, structural equation modelling, and the bootstrapping method were used to analyse the data. This study identified a 24.9% incidence of negative emotions in nurses. RESE was a significant mediator explaining the effect of personality on epidemic‐related negative emotions. RESE mediated the effect of introversion–extroversion on depression (β = −0.151, P = 0.015), neuroticism (β = −0.182, P = 0.007), fear (β = −0.142, P = 0.006), anxiety (β = −0.189, P = 0.015), and hypochondria (β = −0.118, P = 0.010); it also mediated the effect of neuroticism on depression (β = 0.313, P = 0.002), neuroticism (β = 0.394, P = 0.003), fear (β = 0.345, P = 0.005), anxiety (β = 0.384, P = 0.003), and hypochondria (β = 0.259, P = 0.004). Nurses caring for COVID‐19 patients displayed negative emotions, particularly emotionally unstable and introverted nurses with a low RESE level. RESE is often essential for interventions because it significantly influences the relationship between personality and negative emotions. In the event of a major outbreak, tailored psychological well‐being education, which includes emotional self‐efficacy strategies, should be provided by organizations to help nurses manage stress related to the outbreak.

Risk predictive models for delirium in the intensive care unit: a systematic review and meta-analysis Xiangping Chen, Yuewen Lao, Yi Zhang, Lijie Qiao, Yiyu Zhuang

Purpose: To develop a dynamic nomogram of subsyndromal delirium (SSD) in intensive care unit (ICU) patients and internally validate its efficacy in predicting SSD. Patients and Methods: Patients who met the inclusion and exclusion criteria in the ICU of a tertiary hospital in Zhejiang from September 2021 to June 2022 were selected as the research objects. The patient data were randomly divided into the training set and validation set according to the ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression were used to screen the predictors of SSD, and R software was used to construct a dynamic nomogram. Receiver operating characteristic (ROC) curve, calibration band and decision curve were used to evaluate the discrimination, calibration and clinical effectiveness of the model. Results: A total of 1000 eligible patients were included, including 700 in the training set and 300 in the validation set. Age, drinking history, C reactive protein level, APACHE II, indwelling urinary catheter, mechanical ventilation, cerebrovascular disease, respiratory failure, constraint, dexmedetomidine, and propofol were predictors of SSD in ICU patients. The ROC curve values of the training set was 0.902 (95% confidence interval: 0.879– 0.925), the best cutoff value was 0.264, the specificity was 78.4%, and the sensitivity was 88.0%. The ROC curve values of the validation set was 0.888 (95% confidence interval: 0.850– 0.930), the best cutoff value was 0.543, the specificity was 94.9%, and the sensitivity was 70.9%. The calibration band showed good calibration in the training and validation set. Decision curve analysis showed that the net benefit in the model was significantly high. Conclusion: The dynamic nomogram has good predictive performance, so it is a precise and effective tool for medical staff to predict and manage SSD in the early stage.