Mental health among healthcare workers during the prolonged COVID-19 pandemic: A cross-sectional survey in Jilin Province in China

Background The prolonged COVID-19 pandemic has seriously impacted the mental health of healthcare workers. This study aimed to explore the mental health status of healthcare workers, compare the differences in mental health between physicians and nurses, and verify the impact of risk perception on mental health in the long-term COVID-19 pandemic in Jilin Province, China. Methods A stratified random sample was used to conduct an on-site questionnaire survey in December 2020 to measure the mental health status, risk perceptions, and demographic characteristics of healthcare workers in Jilin Province, China. A total of 3,383 participants completed the questionnaire survey, of which 3,373 were valid questionnaires. Results A total of 23.6% (n = 795) of participants had symptoms of depression, 27.4% (n = 923) had symptoms of anxiety, and 16.3% (n = 551) had symptoms of stress. Physicians reported significantly higher rates of depression and anxiety than nurses (p = 0.023, p = 0.013, respectively). There was no significant difference in the proportion of participants with stress between physicians and nurses (p = 0.474). Multivariate logistic regression results showed that healthcare workers who had a high level of risk perception were more likely to have symptoms of depression (AOR = 4.12, p < 0.001), anxiety (AOR = 3.68, p < 0.001), and stress (AOR = 4.45, p < 0.001) after controlling for other variables. Conclusion At least one in six healthcare workers experienced mental health problems, and physicians were more likely than nurses to suffer from depression during the prolonged COVID-19 epidemic. Risk perception was highly predictive of depression, anxiety, and stress symptoms in medical staff. Public health interventions are needed to mitigate the long-term psychological impact of the COVID-19 pandemic.

Work Stress, Health Status and Presenteeism in Relation to Task Performance Among Chinese Medical Staff During COVID-19 Pandemic.

Objectives: This study aims to evaluate the direct effects of work stress, health status and presenteeism on task performance, and further explore the mediating effects of health status and presenteeism, hoping to provide theoretical basis for improving the performance of medical staff. Methods: A cross-sectional study was conducted among medical staff in Jilin Province, Northeast China. The Challenge and Hindrance-Related Self-Reported Stress scale, Short Form-8 Health Survey scale, Stanford Presenteeism Scale and Task Performance Scale were adopted to assess the work stress, health status, presenteeism and task performance of medical staff. Results: A total of 4,347 questionnaires were distributed among medical staff, and 4261 were valid, for an effective rate of 98.02%. The mean scores for work stress, health status, presenteeism and task performance were 2.05 ± 0.84, 4.18 ± 0.68, 2.15 ± 0.79 and 4.49 ± 0.64, respectively. The ANOVA results showed that there were significant differences in the task performance scores between different genders, ages, marital statuses, professional titles, departments and work years (P < 0.05). Work stress (ß = -0.136, P < 0.001) and presenteeism (ß = -0.171, P < 0.001) were negative predictors of task performance. Health status (ß = 0.10; P < 0.001) was positive predictor of task performance. Health status (ß = -0.070; P < -0.001) and presenteeism (ß = -0.064; P < 0.001) mediated the relationship between work stress and task performance (P < 0.001). Presenteeism mediated the relationship between health status and task performance (ß = 0.07; P < 0.001). Conclusion: Work stress and presenteeism had significant negative impact on the task performance of medical staff; health status had a significant positive effect on task performance. Meanwhile, health status and presenteeism played a mediating role in the relationship between work stress and task performance, and presenteeism played a mediating role in the relationship between health status and task performance. Reasonable assignment of tasks can reduce the work stress, but to improve the performance of medical staff, we should pay more attention on improving health, such as making health-related safeguard measures, raising awareness, building a platform, etc.

Single-cell transcriptomic profiling of lung endothelial cells identifies dynamic inflammatory and regenerative subpopulations.

Studies have demonstrated the phenotypic heterogeneity of vascular endothelial cells (ECs) within a vascular bed; however, little is known about how distinct endothelial subpopulations in a particular organ respond to an inflammatory stimulus. We performed single-cell RNA-Seq of 35,973 lung ECs obtained during baseline as well as postinjury time points after inflammatory lung injury induced by LPS. Seurat clustering and gene expression pathway analysis identified 2 major subpopulations in the lung microvascular endothelium, a subpopulation enriched for expression of immune response genes such as MHC genes (immuneEC) and another defined by increased expression of vascular development genes such as Sox17 (devEC). The presence of immuneEC and devEC subpopulations was also observed in nonhuman primate lungs infected with SARS-CoV-2 and murine lungs infected with H1N1 influenza virus. After the peak of inflammatory injury, we observed the emergence of a proliferative lung EC subpopulation. Overexpression of Sox17 prevented inflammatory activation in ECs. Thus, there appeared to be a "division of labor" within the lung microvascular endothelium in which some ECs showed propensity for inflammatory signaling and others for endothelial regeneration. These results provide underpinnings for the development of targeted therapies to limit inflammatory lung injury and promote regeneration.

Distance learning in higher education during COVID-19: The role of basic psychological needs and intrinsic motivation for persistence and procrastination-a multi-country study.

Due to the COVID-19 pandemic, higher educational institutions worldwide switched to emergency distance learning in early 2020. The less structured environment of distance learning forced students to regulate their learning and motivation more independently. According to self-determination theory (SDT), satisfaction of the three basic psychological needs for autonomy, competence and social relatedness affects intrinsic motivation, which in turn relates to more active or passive learning behavior. As the social context plays a major role for basic need satisfaction, distance learning may impair basic need satisfaction and thus intrinsic motivation and learning behavior. The aim of this study was to investigate the relationship between basic need satisfaction and procrastination and persistence in the context of emergency distance learning during the COVID-19 pandemic in a cross-sectional study. We also investigated the mediating role of intrinsic motivation in this relationship. Furthermore, to test the universal importance of SDT for intrinsic motivation and learning behavior under these circumstances in different countries, we collected data in Europe, Asia and North America. A total of N = 15,462 participants from Albania, Austria, China, Croatia, Estonia, Finland, Germany, Iceland, Japan, Kosovo, Lithuania, Poland, Malta, North Macedonia, Romania, Sweden, and the US answered questions regarding perceived competence, autonomy, social relatedness, intrinsic motivation, procrastination, persistence, and sociodemographic background. Our results support SDT's claim of universality regarding the relation between basic psychological need fulfilment, intrinsic motivation, procrastination, and persistence. However, whereas perceived competence had the highest direct effect on procrastination and persistence, social relatedness was mainly influential via intrinsic motivation.

JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation.

Recently, the coronavirus disease 2019 (COVID-19) has caused a pandemic disease in over 200 countries, influencing billions of humans. To control the infection, identifying and separating the infected people is the most crucial step. The main diagnostic tool is the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. Still, the sensitivity of the RT-PCR test is not high enough to effectively prevent the pandemic. The chest CT scan test provides a valuable complementary tool to the RT-PCR test, and it can identify the patients in the early-stage with high sensitivity. However, the chest CT scan test is usually time-consuming, requiring about 21.5 minutes per case. This paper develops a novel Joint Classification and Segmentation (JCS) system to perform real-time and explainable COVID- 19 chest CT diagnosis. To train our JCS system, we construct a large scale COVID- 19 Classification and Segmentation (COVID-CS) dataset, with 144,167 chest CT images of 400 COVID- 19 patients and 350 uninfected cases. 3,855 chest CT images of 200 patients are annotated with fine-grained pixel-level labels of opacifications, which are increased attenuation of the lung parenchyma. We also have annotated lesion counts, opacification areas, and locations and thus benefit various diagnosis aspects. Extensive experiments demonstrate that the proposed JCS diagnosis system is very efficient for COVID-19 classification and segmentation. It obtains an average sensitivity of 95.0% and a specificity of 93.0% on the classification test set, and 78.5% Dice score on the segmentation test set of our COVID-CS dataset. The COVID-CS dataset and code are available at https://github.com/yuhuan-wu/JCS.