Objective: The COVID-19 pandemic led to a deviation from classical face-to-face learning to distance learning. Few studies examined burnout among university students during the distance learning period due to the COVID-19 pandemic. This study that aims to investigate the prevalence of burnout among university students during distance learning and the factors associated with it.
Method: A cross-sectional study was conducted among undergraduate students at the University of Jordan. The modified version of the Maslach Burnout Inventory for students (MBI-SS) was used to assess burnout.
Results: The total number of participants was 587 and the mean total of MBI-SS score was 63.34 ± 8.85. Based on the MBI-SS definition, 6.6% of the study participants were found to have symptoms of burnout. Practicing hobbies, level of satisfaction with distance learning, and thoughts about quitting courses were significant predictors of burnout.
Conclusion: This study showed a relatively low prevalence of burnout among students during the distance learning period with several factors associated with it. As a result, identifying these factors will help both students and educational institutions to implement strategies that are needed for the primary and secondary prevention of burnout.
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http://dx.doi.org/10.1177/00912174221107780 | DOI Listing |
J Chem Theory Comput
January 2025
BIFOLD─Berlin Institute for the Foundations of Learning and Data, 10587 Berlin, Germany.
While machine learning (ML) models have been able to achieve unprecedented accuracies across various prediction tasks in quantum chemistry, it is now apparent that accuracy on a test set alone is not a guarantee for robust chemical modeling such as stable molecular dynamics (MD). To go beyond accuracy, we use explainable artificial intelligence (XAI) techniques to develop a general analysis framework for atomic interactions and apply it to the SchNet and PaiNN neural network models. We compare these interactions with a set of fundamental chemical principles to understand how well the models have learned the underlying physicochemical concepts from the data.
View Article and Find Full Text PDFJ Magn Reson Imaging
January 2025
Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Ålesund, Norway.
Background: Deep learning-based segmentation of brain metastases relies on large amounts of fully annotated data by domain experts. Semi-supervised learning offers potential efficient methods to improve model performance without excessive annotation burden.
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J Med Internet Res
January 2025
Learning and Capacity Development Unit, Health Emergencies Programme, World Health Organization, Geneva, Switzerland.
Background: The COVID-19 pandemic demonstrated the global need for accessible content to rapidly train health care workers during health emergencies. The massive open access online course (MOOC) format is a broadly embraced strategy for widespread dissemination of trainings. Yet, barriers associated with technology access, language, and cultural context limit the use of MOOCs, particularly in lower-resource communities.
View Article and Find Full Text PDFCurr Microbiol
January 2025
Coastar Therapeutics, San Diego, CA, 92126, USA.
Staphylococcus epidermidis (S. epidermidis) live in different human locations and natural environments. For ribotyping S.
View Article and Find Full Text PDFTransl Vis Sci Technol
January 2025
Wilmer Eye Institute, Johns Hopkins University, Baltimore, MD, USA.
Purpose: Uncorrected visual impairment (VI) significantly impacts life quality and exacerbates age-related health issues. Social determinants of health (SDOH) are associated with uncorrected VI, but quantitative evidence is limited. This study investigated the link between SDOH and uncorrected VI among aging adults to identify disparities and improve vision care.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!