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This study explores the relation between oral language, spoken dialect variation, and reading achievement among Black children from low-income backgrounds, with an emphasis on identifying within-group variability. Few studies have examined how these variables interact to influence literacy outcomes. Using data from 797 children in Grades 1 to 4 (ages: 6-11 years), we conducted a two-part analysis. First, confirmatory factor analysis was used to assess the structure of language, dialect variation, and reading performance. The study found that while these skills are interconnected, they remain distinct constructs. Second, latent profile analysis was used to explore heterogeneity in language and reading skills within the sample, revealing distinct profiles of strengths and weaknesses. While children with higher dialect density of African American English were more likely to show lower literacy performance, dialect variation alone did not predict specific literacy profiles. These findings suggest that oral language proficiency and dialect variation should be considered when designing interventions to improve reading outcomes for Black children. This study contributes to the understanding of how dialect variation influences reading achievement and highlights the need for culturally responsive literacy instruction that values linguistic diversity.
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http://dx.doi.org/10.1055/a-2662-8110 | DOI Listing |
PLoS One
September 2025
Department of Mathematics, Xavier University of Louisiana, New Orleans, Louisiana, United States of America.
It is well-known that income can correlate with the academic performance of K-12 students in the United States (U.S.).
View Article and Find Full Text PDFFront Public Health
September 2025
Department of Health Care Sciences, Marie Cederschiöld University, Stockholm, Sweden.
Purpose: This study investigates how older foreign-born adults in Sweden experience and navigate social connectedness as a determinant of wellbeing.
Methods: Employing Glaser's grounded theory methodology, we collected qualitative data through individual ( = 1) and focus group ( = 5) interviews with 23 participants aged 60 + representing four distinct cultural-linguistic groups: Arabic, Finnish, Spanish, and Chinese speakers.
Results: The analysis identified "" as the core category, encompassing three dimensions: (1) , (2) , and (3) .
Alpha Psychiatry
August 2025
Information Sciences and Technology, George Mason University, Fairfax, VA 22030, USA.
Background: Herein, we report on the initial development, progress, and future plans for an autonomous artificial intelligence (AI) system designed to manage major depressive disorder (MDD). The system is a web-based, patient-facing conversational AI that collects medical history, provides presumed diagnosis, recommends treatment, and coordinates care for patients with MDD.
Methods: The system includes seven components, five of which are complete and two are in development.
JMIR Res Protoc
September 2025
University of Nevada, Las Vegas, Las Vegas, NV, United States.
Background: In-hospital cardiac arrest (IHCA) remains a public health conundrum with high morbidity and mortality rates. While early identification of high-risk patients could enable preventive interventions and improve survival, evidence on the effectiveness of current prediction methods remains inconclusive. Limited research exists on patients' prearrest pathophysiological status and predictive and prognostic factors of IHCA, highlighting the need for a comprehensive synthesis of predictive methodologies.
View Article and Find Full Text PDFMedicine (Baltimore)
September 2025
Diagnosis and Treatment Center for Children, The Affiliated Hospital to Changchun University of Chinese Medicine, Changchun, Jilin Province, China.
Rationale: Phelan-McDermid syndrome, also known as chromosome 22q13.3 deletion syndrome, is a genetic disorder primarily caused by a chromosome 22q13.3 deletion or mutation.
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