Improving children's learning and development in conflict-affected countries is critically important for breaking the intergenerational transmission of violence and poverty. Yet there is currently a stunning lack of rigorous evidence as to whether and how programs to improve learning and development in conflict-affected countries actually work to bolster children's academic learning and socioemotional development. This study tests a theory of change derived from the fields of developmental psychopathology and social ecology about how a school-based universal socioemotional learning program, the International Rescue Committee's Learning to Read in a Healing Classroom (LRHC), impacts children's learning and development. The study was implemented in three conflict-affected provinces of the Democratic Republic of the Congo and employed a cluster-randomized waitlist control design to estimate impact. Using multilevel structural equation modeling techniques, we found support for the central pathways in the LRHC theory of change. Specifically, we found that LRHC differentially impacted dimensions of the quality of the school and classroom environment at the end of the first year of the intervention, and that in turn these dimensions of quality were differentially associated with child academic and socioemotional outcomes. Future implications and directions are discussed.
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http://dx.doi.org/10.1017/S0954579416001139 | DOI Listing |
Eur J Dent Educ
January 2025
QU Health College of Dental Medicine, Qatar University, Doha, Qatar.
Aims: This study aimed to evaluate the impact of community-based dental education (CBDE) on the learning experiences of undergraduate dental students and recent dental graduates from two diverse geographical regions.
Methods: The study followed a cross-sectional design, conducted online using Google Forms, with ethical approval from Qatar University. A non-probability purposive sampling method was used to recruit dental students and recent graduates from three institutions in India and one in Qatar.
Physiol Plant
January 2025
Laboratory of Biochemistry, Institut Químic de Sarrià, Universitat Ramon Llull, Barcelona, Spain.
Photosynthetic microalgae are promising green cell factories for the sustainable production of high-value chemicals and biopharmaceuticals. The chloroplast organelle is being developed as a chassis for synthetic biology as it contains its own genome (the plastome) and some interesting advantages, such as high recombinant protein titers and a diverse and dynamic metabolism. However, chloroplast engineering is currently hampered by the lack of standardized cloning tools and Design-Build-Test-Learn workflows to ease genomic and metabolic engineering.
View Article and Find Full Text PDFMed Phys
January 2025
Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
Background: Kidney tumors, common in the urinary system, have widely varying survival rates post-surgery. Current prognostic methods rely on invasive biopsies, highlighting the need for non-invasive, accurate prediction models to assist in clinical decision-making.
Purpose: This study aimed to construct a K-means clustering algorithm enhanced by Transformer-based feature transformation to predict the overall survival rate of patients after kidney tumor resection and provide an interpretability analysis of the model to assist in clinical decision-making.
Eur J Nucl Med Mol Imaging
January 2025
The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
Purpose: The study explores the role of multimodal imaging techniques, such as [F]F-PSMA-1007 PET/CT and multiparametric MRI (mpMRI), in predicting the ISUP (International Society of Urological Pathology) grading of prostate cancer. The goal is to enhance diagnostic accuracy and improve clinical decision-making by integrating these advanced imaging modalities with clinical variables. In particular, the study investigates the application of few-shot learning to address the challenge of limited data in prostate cancer imaging, which is often a common issue in medical research.
View Article and Find Full Text PDFMed Phys
January 2025
Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, China.
Background: Online adaptive radiotherapy (OART) and rapid quality assurance (QA) are essential for effective heavy ion therapy (HIT). However, there is a shortage of deep learning (DL) models and workflows for predicting Monte Carlo (MC) doses in such treatments.
Purpose: This study seeks to address this gap by developing a DL model for independent MC dose (MCDose) prediction, aiming to facilitate OART and rapid QA implementation for HIT.
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