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Research on student engagement has recently gained popularity as it can address problems such as early dropout and poor achievement. The growing interest in investigating student engagement during the Covid-19 pandemic is reflected in increased publications addressing this topic. However, no review provided research evidence and an overview of existing literature on student engagement during emergency remote teaching (ERT). We reviewed how student engagement studies were undertaken during ERT from three perspectives: (1) the landscape of studies, (2) methodologies issues, and (3) the strategies used to facilitate student engagement. 42 articles were analysed from an initial pool of 436 search results. The findings illustrate that current studies were predominately undertaken in the United States (36%) and China (22%) with focusing on STEM subjects as a dominant discipline. The literature was largely inconsistent in defining and measuring student engagement. In addition, the majority of studies (57%) investigated students' engagement from the perspective of students, unlike other stakeholders. The most prominent finding is that ERT promoted several important engagement strategies, including motivational factors, teachers' facilitation, a hybrid learning model, and using learning technologies to boost students' engagement. [ABSTRACT FROM AUTHOR]

In a very short time, higher education transitioned to online and blended learning, in response to the global COVID-19 pandemic. Although research literature is replete with rationale for instructors to develop digital competence during the Great Online Transition, research on the correlates of digital competence and effort expectancy in relation to their work engagement has remained insufficient. Thus, the objective of this study is to investigate the effects of digital competence and effort expectancy and how they predict teachers' work engagement. A sample of 321 in-service teachers selected from universities in China took part in this study. The results of structural equation modeling indicated that teachers' digital competence positively and significantly correlated with their work engagement and their effort expectancy. In addition, effort expectancy significantly influenced teachers' work engagement. Lastly, effort expectancy, as the mediator variable, was found to mediate the relationship between teachers' digital competence and their work engagement. Implications were suggested for improving teachers' work engagement to support digital shifts. [ABSTRACT FROM AUTHOR]

Objective: To assess the quality of the doctor-patient relationship (DPR) in China and possible influencing factors during the COVID-19 period from the patient's perspective. Methods: An online survey was carried out nationwide from March 12, 2020 to March 30, 2020 in China via a convenience sampling strategy. Patients who met the inclusion criteria were invited to complete a questionnaire regarding the quality of DPR, including sociodemographic information, the Patient-Doctor Relationship Questionnaire (PDRQ-9), and influencing factors for DPR during the pandemic. Results: A total of 1903 patients were included. Our result showed that participants had a higher PDRQ-9 score during the COVID-19 pandemic (4.18 ± 0.51) than that before the COVID-19 pandemic (3.86 ± 0.67). Importance-performance analysis (IPA) revealed that doctor-patient communication, patient satisfaction, consultation time, doctor's attitude, and medical knowledge were specific aspects that needed to be prioritized to improve the DPR. Multiple linear regression analysis suggested that positive media reports, telemedicine, and national policies had a significantly positive effect on the DPR during the pandemic (P < 0.05). Conclusion: In general, the DPR had been improved during the COVID-19 pandemic. Our research found the key points that needed to be prioritized to improve the DPR during the pandemic, which may provide effective suggestions for building a harmonious DPR in the future. [ABSTRACT FROM AUTHOR]

Background: Doctor-patient relationship (DPR) is very important for patient outcomes, especially during a public health emergency like the COVID-19 pandemic. However, few studies have evaluated DPR and related sentiments from medical professionals' perspectives. Thus, the aim of the study is to provide a better understanding of DPR from medical professionals' perspectives during the COVID-19 pandemic in China. Methods: A total of 979 medical professionals, including doctors, nurses, technicians, and other workers have completed a series of questionnaires to evaluate their attitudes toward DPR, trust, violence against doctors, factors that affected and improved DPR, and the importance of these factors on DPR. Analyses of variances (ANOVA) and linear regressions were used to analyze the effects of the pandemic, demographic variables, and various elements on DPR. Results: One-way ANOVA revealed a significant effect of education on recent DPR [ F (2, 976) = 6.17, p < 0.001 and trust at F (2, 976) = 9.54, p < 0.001], indicating that individuals with higher level of education (bachelor's degree, Master's degree and above) showed poorer recent DPR and lower level of trust. The level of hospital also showed a significant effect on trust [ F (5, 973) = 3.79, p = 0.0021]. Cochran's Q test revealed a significant difference in factors that affected [Q(11) = 3,997.83, p < 0.001] and improved [Q(8) = 3,304.53, p < 0.001] DPR. Backward stepwise linear regressions revealed predictors for changes during [ F (9, 969) = 21.17, p < 0.001, R 2 = 0.16], shortly after [ F (7, 971) = 54.98, p < 0.001, R 2 = 0.28], and long after [ F (10, 968) = 37.83, p < 0.001, R 2 = 0.29] the pandemic. Conclusions: Medical professionals' perceptions of DPR is important as they provide basis for the improvement in working environment of medical professionals and hospital visiting experience of patients, as well as healthcare policy making and preparation for future public health emergencies. [ABSTRACT FROM AUTHOR]

• First work to investigate solutions for collaborative medical AI model for COVID-19. • A novel federated semi-supervised learning technique is proposed to train a network. • Method fully utilizes available data from different sources, w/ or w/o annotations. • Multinational dataset of 1704 scans from 3 countries (China, Italy, and Japan). • Studies fully evaluate and understand different configurations of the method. [Display omitted] The recent outbreak of Coronavirus Disease 2019 (COVID-19) has led to urgent needs for reliable diagnosis and management of SARS-CoV-2 infection. The current guideline is using RT-PCR for testing. As a complimentary tool with diagnostic imaging, chest Computed Tomography (CT) has been shown to be able to reveal visual patterns characteristic for COVID-19, which has definite value at several stages during the disease course. To facilitate CT analysis, recent efforts have focused on computer-aided characterization and diagnosis with chest CT scan, which has shown promising results. However, domain shift of data across clinical data centers poses a serious challenge when deploying learning-based models. A common way to alleviate this issue is to fine-tune the model locally with the target domains local data and annotations. Unfortunately, the availability and quality of local annotations usually varies due to heterogeneity in equipment and distribution of medical resources across the globe. This impact may be pronounced in the detection of COVID-19, since the relevant patterns vary in size, shape, and texture. In this work, we attempt to find a solution for this challenge via federated and semi-supervised learning. A multi-national database consisting of 1704 scans from three countries is adopted to study the performance gap, when training a model with one dataset and applying it to another. Expert radiologists manually delineated 945 scans for COVID-19 findings. In handling the variability in both the data and annotations, a novel federated semi-supervised learning technique is proposed to fully utilize all available data (with or without annotations). Federated learning avoids the need for sensitive data-sharing, which makes it favorable for institutions and nations with strict regulatory policy on data privacy. Moreover, semi-supervision potentially reduces the annotation burden under a distributed setting. The proposed framework is shown to be effective compared to fully supervised scenarios with conventional data sharing instead of model weight sharing. [ABSTRACT FROM AUTHOR]

• The psychological trauma caused by COVID-19 was a negative predictor of mental health among Wuhan's high school students. • Resilience and positive emotion regulation interrupted the direct impact of psychological trauma on mental health. • Resilience and positive emotion regulation are mediators. This study explored the protective mechanisms of resilience and positive emotion regulation against psychological trauma among Wuhan's high school students during the COVID-19 outbreak. We obtained 286 valid samples from four high schools in Wuhan, China. To construct a measurement model and a structural model, structural equation models using four measurement scales with valid items were adopted, including a scale of psychological trauma, a scale of resilience, a scale of positive emotional regulation, and a scale of mental health. Participants reported their answers to items on a 5-point Likert-like scale. The results revealed that psychological trauma was a negative predictor of mental health, and resilience and positive emotion regulation mediated the relationship between psychological trauma and mental health. Accordingly, we concluded that resilience and positive emotion regulation interrupted the direct impact of psychological trauma on mental health, thereby greatly protecting the mental health of Wuhan's high school students during the COVID-19 outbreak. [ABSTRACT FROM AUTHOR]