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The words in children's language learning environments are strongly predictive of cognitive development and school achievement. But how do we measure language environments and do so at the scale of the many words that children hear day in, day out? The quantity and quality of words in a child's input are typically measured in terms of total amount of talk and the lexical diversity in that talk. There are disagreements in the literature whether amount or diversity is the more critical measure of the input. Here we analyze the properties of a large corpus (6.5 million words) of speech to children and simulate learning environments that differ in amount of talk per unit time, lexical diversity, and the contexts of talk. The central conclusion is that what researchers need to theoretically understand, measure, and change is not the total amount of words, or the diversity of words, but the function that relates total words to the diversity of words, and how that function changes across different contexts of talk.
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http://dx.doi.org/10.1111/cogs.12592 | DOI Listing |
Ann Biomed Eng
September 2025
Department of Midwifery, Faculty of Health Sciences, Sakarya University, 54100, Sakarya, Turkey.
The incorporation of AI-supported language models into the healthcare sector holds significant potential to revolutionize nursing education, research, and clinical practice. Within this framework, ChatGPT has emerged as a valuable tool for personalizing educational materials, enhancing academic productivity, expediting clinical decision-making processes, and optimizing research efficiency. In the realm of nursing education, ChatGPT offers numerous advantages, including the preparation of course content, facilitation of student assessments, and the development of simulation-based learning environments.
View Article and Find Full Text PDFEnviron Sci Pollut Res Int
September 2025
Department of Research Analytics, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, India.
Wien Klin Wochenschr
September 2025
Medizinische Klinik und Poliklinik IV, LMU-Klinikum München, München, Germany.
Objective: The study aims to elucidate a possible effect of individual reflection (IR) or group reflection (GR) on short-term and long-term memory retention in a large group lecture-based environment.
Methods: In this quasi-experimental study 656 medical students were enrolled to compare the impact of IR and GR directly after the lectures and 2 months later. Students were divided into two groups and given two different lectures using IR or GR in a cross-over fashion.
J Nurs Educ
September 2025
Union, Kentucky and Phi Gamma Chapter-Sigma Theta Tau International Nursing Honor Society, Indianapolis, Indiana.
Introduction: Effective triage in the emergency department (ED) is essential for optimizing resource allocation, improving efficiency, and enhancing patient outcomes. Conventional systems rely heavily on clinical judgment and standardized guidelines, which may be insufficient under growing patient volumes and increasingly complex presentations.
Methods: We developed a machine learning triage model, MIGWO-XGBOOST, which incorporates a Multi-strategy Improved Gray Wolf Optimization (MIGWO) algorithm for parameter tuning.