Social network analysis can draw upon surveys and discussions to generate quantitative and qualitative data. We describe network data generated via a social network survey and discussion activity with high school science teachers in a teacher leadership development program. Data include social network maps related to seeking expertise in teaching content and/or pedagogy, disaggregated by contacts at the school, district, state, nation, and international spheres of influence. Data also include transcripts of the activity and teacher discussions of networks in their own educational settings. This data article is related to the research article, "The use of visual network scales in teacher leader development" Polizzi et al., 2019, where data interpretation can be found.
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http://dx.doi.org/10.1016/j.dib.2019.104182 | DOI Listing |
Glob Ment Health (Camb)
December 2024
Faculty of Nursing, University of Alberta.
Background: The COVID-19 pandemic brought to light the need to address the psychosocial and mental health needs of refugees and internally displaced persons in low- and middle-income countries. COVID-19 prevention measures slowed essential services and healthcare, creating unique challenges for refugees and IDPs, including economic insecurity and societal instability. All of these factors may contribute to the reported declines in their psychosocial well-being.
View Article and Find Full Text PDFPatterns (N Y)
December 2024
Medical Robot Research Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
This study developed an artificial intelligence (AI) system using a local-global multimodal fusion graph neural network (LGMF-GNN) to address the challenge of diagnosing major depressive disorder (MDD), a complex disease influenced by social, psychological, and biological factors. Utilizing functional MRI, structural MRI, and electronic health records, the system offers an objective diagnostic method by integrating individual brain regions and population data. Tested across cohorts from China, Japan, and Russia with 1,182 healthy controls and 1,260 MDD patients from 24 institutions, it achieved a classification accuracy of 78.
View Article and Find Full Text PDFCureus
December 2024
Pathology, BLDE (Deemed to be University) Shri B.M. Patil Medical College, Hospital, and Research Centre, Vijayapura, IND.
Introduction Occupational health hazards are a significant concern for pathologists due to their unique work environment. These professionals face risks from prolonged microscope use, exposure to chemicals such as formalin, and handling sharp instruments, leading to issues such as musculoskeletal disorders and needlestick injuries. Addressing these hazards is crucial for their well-being and the overall efficiency of medical diagnostics.
View Article and Find Full Text PDFMol Inform
January 2025
Faculty of Information Technology, HUTECH University, 700000, Ho Chi Minh City, Vietnam.
In recent times, graph representation learning has been becoming a hot research topic which has attracted a lot of attention from researchers. Graph embeddings have diverse applications across fields such as information and social network analysis, bioinformatics and cheminformatics, natural language processing (NLP), and recommendation systems. Among the advanced deep learning (DL) based architectures used in graph representation learning, graph neural networks (GNNs) have emerged as the dominant and highly effective framework.
View Article and Find Full Text PDFJAMA Netw Open
January 2025
Network Science Institute, Northeastern University, Boston, Massachusetts.
Importance: Efforts to understand the complex association between social media use and mental health have focused on depression, with little investigation of other forms of negative affect, such as irritability and anxiety.
Objective: To characterize the association between self-reported use of individual social media platforms and irritability among US adults.
Design, Setting, And Participants: This survey study analyzed data from 2 waves of the COVID States Project, a nonprobability web-based survey conducted between November 2, 2023, and January 8, 2024, and applied multiple linear regression models to estimate associations with irritability.
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