This study evaluated the sustained effects of the Research-based Developmentally Informed Parent program (REDI-P) at fifth grade, six years after intervention. Participants were 200 prekindergarten children attending Head Start (55% White, 26% Black, 19% Latinx, 56% male, mean age of 4.45 years at study initiation) and their primary caregivers, who were randomly assigned to a control group or a 16-session home-visiting intervention that bridged the preschool and kindergarten years. In addition, the study explored moderation of sustained effects by parenting risks (e.g., less than high-school education, single-parent status, parental depression, and low parent-child warmth). Growth curves over the course of the elementary years examined outcomes in three domains: child academic performance, social-emotional adjustment, and parent-child functioning. At fifth grade, significant main effects for intervention were sustained in the domains of academic performance (e.g., reading skills, academic motivation, and learning engagement) and parent-child functioning (e.g., academic expectations and parenting stress). Significant moderation by parenting risk emerged on measures of social-emotional adjustment (e.g., social competence and student-teacher relationships); parenting risk also amplified effects on some measures of academic performance and parent-child functioning, with larger effects for children from families experiencing fewer risks. Implications are discussed for the design of preschool home visiting programs seeking to enhance the school success and social-emotional well-being of children living in poverty.
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http://dx.doi.org/10.1016/j.ecresq.2021.03.017 | DOI Listing |
Anat Sci Educ
January 2025
Tissue Engineering Group, Department of Histology, Medical School, University of Granada, Granada, Spain.
The recent coronavirus disease (COVID-19) forced pre-university professionals to modify the educational system. This work aimed to determine the effects of pandemic situation on students' access to medical studies by comparing the performance of medical students. We evaluated the performance of students enrolled in a subject taught in the first semester of the medical curriculum in two pre-pandemic academic years (PRE), two post-pandemic years (POST), and an intermediate year (INT) using the results of a final multiple-choice exam.
View Article and Find Full Text PDFJ Med Radiat Sci
January 2025
Royal Adelaide Hospital, Adelaide, South Australia, Australia.
This letter critically evaluates the conclusions drawn by Li et al. (https://doi.org/10.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Research and Development Center of Optoelectronic Hybrid IC, Guangdong Greater Bay Area Institute of Integrated Circuit and System, Guangzhou 510535, China.
Short-wave infrared (SWIR) imaging has a wide range of applications in civil and military fields. Over the past two decades, significant efforts have been devoted to developing high-resolution, high-sensitivity, and cost-effective SWIR sensors covering the spectral range from 0.9 μm to 3 μm.
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Mechanical and Electrical Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China.
Unsupervised Domain Adaptation for Object Detection (UDA-OD) aims to adapt a model trained on a labeled source domain to an unlabeled target domain, addressing challenges posed by domain shifts. However, existing methods often face significant challenges, particularly in detecting small objects and over-relying on classification confidence for pseudo-label selection, which often leads to inaccurate bounding box localization. To address these issues, we propose a novel UDA-OD framework that leverages scale consistency (SC) and Temporal Ensemble Pseudo-Label Selection (TEPLS) to enhance cross-domain robustness and detection performance.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Research Center in Sports Sciences, Health Sciences and Human Development, CIDESD, CreativeLab Research Community, 5000-801 Vila Real, Portugal.
Artificial Intelligence (AI) is transforming the field of sports science by providing unprecedented insights and tools that enhance training, performance, and health management. This work examines how AI is advancing the role of sports scientists, particularly in team sports environments, by improving training load management, sports performance, and player well-being. It explores key dimensions such as load optimization, injury prevention and return-to-play, sports performance, talent identification and scouting, off-training behavior, sleep quality, and menstrual cycle management.
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