Nurse educators must continually improve their teaching skills through innovation. However, research about the process used by faculty members to transform their teaching methods is limited. This collaborative study uses classroom action research to describe, analyze, and address problems encountered in implementing cooperative learning in two undergraduate nursing courses. After four rounds of action and reflection, the following themes emerged: students did not understand the need for structured cooperative learning; classroom structure and seating arrangement influenced the effectiveness of activities; highly structured activities engaged the students; and short, targeted activities that involved novel content were most effective. These findings indicate that designing specific activities to prepare students for class is critical to cooperative learning.
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http://dx.doi.org/10.3928/01484834-20100224-06 | DOI Listing |
J Am Med Inform Assoc
January 2025
Institute of Data Science, National University of Singapore, 117602, Singapore.
Objectives: This study introduces Smart Imitator (SI), a 2-phase reinforcement learning (RL) solution enhancing personalized treatment policies in healthcare, addressing challenges from imperfect clinician data and complex environments.
Materials And Methods: Smart Imitator's first phase uses adversarial cooperative imitation learning with a novel sample selection schema to categorize clinician policies from optimal to nonoptimal. The second phase creates a parameterized reward function to guide the learning of superior treatment policies through RL.
Cells
December 2024
Department of Herbal Pharmacology, College of Korean Medicine, Gachon University, 1342 Seongnamdae-ro, Sujeong-gu, Seongnam-si 13120, Republic of Korea.
The NLRP3 inflammasome, plays a critical role in the pathogenesis of rheumatoid arthritis (RA) by activating inflammatory cytokines such as IL1β and IL18. Targeting NLRP3 has emerged as a promising therapeutic strategy for RA. In this study, a multidisciplinary approach combining machine learning, quantitative structure-activity relationship (QSAR) modeling, structure-activity landscape index (SALI), docking, molecular dynamics (MD), and molecular mechanics Poisson-Boltzmann surface area MM/PBSA assays was employed to identify novel NLRP3 inhibitors.
View Article and Find Full Text PDFBehav Anal Pract
December 2024
Department of Special Education and Rehabilitation Counseling, Utah State University, Logan, UT USA.
Unlabelled: Children with autism spectrum disorder (ASD) may have difficulty engaging in cooperative communication during classroom learning center activities with peers. This study examined the effects of using an activity schedule intervention package on the rate of contextually appropriate cooperative exchanges for children with ASD during classroom learning centers. In this study, children with ASD worked together in participant partnerships to complete learning center activities.
View Article and Find Full Text PDFProteomics
January 2025
School of Public Health, University of Haifa, Haifa, Israel.
Chronic kidney disease (CKD) poses a significant and growing global health challenge, making early detection and slowing disease progression essential for improving patient outcomes. Traditional diagnostic methods such as glomerular filtration rate and proteinuria are insufficient to capture the complexity of CKD. In contrast, omics technologies have shed light on the molecular mechanisms of CKD, helping to identify biomarkers for disease assessment and management.
View Article and Find Full Text PDFBrain Res Bull
January 2025
School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou 511442, China; Guangdong Province Key Laboratory of Biomedical Engineering, South China University of Technology, Guangzhou 510006, China; Department of Nuclear Medicine and Radiology, Institute of Development, Aging and Cancer, Tohoku University, Sendai 980-8575, Japan. Electronic address:
The methodology of machine learning with multi-omics data has been widely adopted in the discriminative analyses of schizophrenia, but most of these studies ignored the cooperative interactions and topological attributes of multi-omics networks. In this study, we constructed three types of brain graphs (BGs), three types of gut graphs (GGs), and nine types of brain-gut combined graphs (BGCGs) for each individual. We proposed a novel methodology of multi-omics graph convolutional network (MO-GCN) with an attention mechanism to construct a classification model by integrating all BGCGs.
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