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This qualitative study describes the psychological experience of patients hospitalized with COVID-19. These patients went through 3 psychological stages: extremely uncertainties during the initial diagnostic stage, complicated feelings of negativity during the treatment stage, and positive growth in the recovery stage. It is important for nurses to provide holistic care.

Background Gestational diabetes mellitus (GDM) is one of the critical causes of adverse perinatal outcomes. A reliable estimate of GDM in early pregnancy would facilitate intervention plans for maternal and infant health care to prevent the risk of adverse perinatal outcomes. This study aims to build an early model to predict GDM in the first trimester for the primary health care centre. Methods Characteristics of pregnant women in the first trimester were collected from eastern China from 2017 to 2019. The univariate analysis was performed using SPSS 23.0 statistical software. Characteristics comparison was applied with Mann-Whitney U test for continuous variables and chi-square test for categorical variables. All analyses were two-sided with p < 0.05 indicating statistical significance. The train_test_split function in Python was used to split the data set into 70% for training and 30% for test. The Random Forest model and Logistic Regression model in Python were applied to model the training data set. The 10-fold cross-validation was used to assess the model’s performance by the areas under the ROC Curve, diagnostic accuracy, sensitivity, and specificity. Results A total of 1,139 pregnant women (186 with GDM) were included in the final data analysis. Significant differences were observed in age (Z=−2.693, p=0.007), pre-pregnancy BMI (Z=−5.502, p<0.001), abdomen circumference in the first trimester (Z=−6.069, p<0.001), gravidity (Z=−3.210, p=0.001), PCOS (χ2=101.024, p<0.001), irregular menstruation (χ2=6.578, p=0.010), and family history of diabetes (χ2=15.266, p<0.001) between participants with GDM or without GDM. The Random Forest model achieved a higher AUC than the Logistic Regression model (0.777±0.034 vs 0.755±0.032), and had a better discrimination ability of GDM from Non-GDMs (Sensitivity: 0.651±0.087 vs 0.683±0.084, Specificity: 0.813±0.075 vs 0.736±0.087). Conclusions This research developed a simple model to predict the risk of GDM using machine learning algorithm based on pre-pregnancy BMI, abdomen circumference in the first trimester, age, PCOS, gravidity, irregular menstruation, and family history of diabetes. The model was easy in operation, and all predictors were easily obtained in the first trimester in primary health care centres.

Objective To investigate the current situation of work stress in nurses returning to work in Chinese tertiary A general hospitals after giving birth to their second child and to analyze influencing factors. Methods From January to April 2021, 448 nurses returning to work after the birth of their second child, working in 23 general hospitals in China, were investigated and completed the postpartum work stress scale and self-rating depression scale. Results The total work stress score of returning nurses after giving birth to their second child was 90.40 ± 18.29, and the dimension with the highest score was the role commitment of the mother. Multiple linear regression analysis showed that family monthly income, turnover intention, time since returning to work, age of the first child, and depressive symptoms were the influencing factors on work stress. Conclusion It is important to reduce the work stress of the nurses returning to work after the birth of their second child. Nursing managers should pay attention to this group of postpartum nurses and formulate targeted measures to alleviate their work stress.