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20,788 reports · page 805 of 2079

OtherEurope PMC10 Aug 2026

Distinct Cognitive Linguistic Profiles in Struggling Elementary School Learners

Abstract Low academic achievement in reading and writing reflects substantial variability in underlying cognitive–linguistic mechanisms that is not always fully captured in empirical research. This study examined learning profiles among children with persistent low academic achievement using a multidimensional framework integrating linguistic and neurocognitive domains. A teacher-referred sample of students in the 3rd to 5th grades was classified a priori into three groups based on semantic definition and phonological awareness and compared on standardized measures of reading, writing, arithmetic, memory, attention, and executive functions. Three distinct learning profiles were identified: one characterized by preserved semantic and phonological skills despite persistent academic difficulties; a second marked by phonological awareness weaknesses accompanied by working memory limitations; and a third showing combined semantic and phonological impairments associated with broader neurocognitive vulnerabilities, particularly in working and semantic memory. These findings demonstrate that similar academic outcomes may arise from functionally distinct cognitive–linguistic pathways and support profile-based, mechanism-oriented approaches to the study, assessment, and support of children with low academic achievement in reading and writing.

Lagus S, Befi-Lopes DM

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Peer-reviewed articleEurope PMC6 Jul 2026

Family Environment and Early Childhood Education as Predictors of Learning Outcomes from Birth to Age 5: A Systematic Review and Meta‑Analysis

Abstract The period from birth to school entry (ages 0-5 years) is characterized by rapid cognitive, socio-emotional, and behavioral development that is influenced by the family environment and early childhood education (ECE). Few reviews have synthesized evidence about the combined effects of these two domains, and interactive effects still less so. This meta-analysis synthesized studies investigating the relationship between family environmental factors and ECE quality on learning outcomes in children from birth through kindergarten entry, with predictor measurement occurring within the first 48 months of life. A systematic search of empirical studies was conducted on ERIC, PubMed, Scopus, Web of Science, Embase, and Google Scholar (January 2000 - December 2025) according to PRISMA 2020 guidelines. Random effects models were applied for analyses, and heterogeneity, publication bias, and moderators were explored using Comprehensive Meta‑Analysis (Version 3). Twenty-five studies (N = 65,657 children) were included. The overall pooled effect was moderate-to-large (g = 0.410, 95% CI [0.362, 0.459]). Family environment quality showed a moderate-to-large effect (g = 0.52, 95% CI [0.48, 0.56]), whereas ECE quality showed a moderate effect (g = 0.38, 95% CI [0.34, 0.42]). Only three studies included both predictors simultaneously; exploratory meta-analysis suggested potential cumulative advantage (β = 0.23, p < 0.001), though these results are preliminary due to limited studies. Cognitive-linguistic outcomes showed the largest effects (g = 0.61). Sensitivity analyses confirmed that including studies with outcomes up to 60 months did not alter substantive conclusions. Family environment quality and ECE quality are independently associated with early learning outcomes, with family environment showing slightly larger effect sizes. Findings support integrated investments in both contexts to maximize early learning.

Diamanti K, Eslahi AV, Adamopoulos I

DevelopmentRead at source

Peer-reviewed articleEurope PMC31 Jul 2026

How children learn complex environmental concepts through digital storytelling: divergent pathways to knowledge and stewardship intention.

Digital storytelling is increasingly employed in sustainability education to communicate complex place-based environmental issues. However, it remains unclear how primary school students encode concept-dense content during brief narrative viewing, and how such encounters relate to content knowledge (CK) and place-based sustainability stewardship intention (PESI). Using the industrial heritage of the Dagushan Iron Mine as a place-based context, this classroom follow-up study examined whether CK and PESI showed different patterns of association during brief narrative viewing. In the main phase, 452 students in Years 1-6 watched a five-minute animation and completed a seven-item CK test plus experience scales assessing experiential dimensions including interest, engagement, emotional response, audiovisual experience, educational value, and PESI. Associations were analyzed using multiple regression and structural equation modelling. CK was measured at baseline and immediately post-viewing, with a supplementary follow-up conducted 2 weeks later assessing CK and retrospective perceived cognitive load. Results showed selective uptake under brief viewing. Interest positively predicted CK, whereas audiovisual experience and educational value showed inverse associations with CK in the structural model, consistent with a processing-demand account, with additional supporting evidence from retrospective perceived cognitive-load findings at follow-up. CK was not associated with PESI; instead, PESI was linked to grade level and experiential appraisals. At follow-up, higher retrospective perceived extraneous cognitive load was associated with lower CK. Overall, brief narrative animations can support early engagement with complex environmental content, while conceptual understanding and stewardship-oriented intention may emerge through partially distinct learning processes. For classroom practice, the findings suggest that brief narrative animations are effective for introducing complex environmental topics and stimulating students' initial engagement, but should be supplemented with instructional scaffolding, guided explanation, and structured follow-up activities to support deeper conceptual understanding.

Bian X, Brown A, Marques B

DevelopmentPre/postCNRead at source

Peer-reviewed articleEurope PMC23 Sept 2026

Intersectional Effects of the FIT FIRST 10 School-Based Intervention Across Sex and Parental Country of Birth on Physical and Psychosocial Health in Danish Primary Schools.

Background Children with a migration background often show poorer health profiles than majority peers. Schools provide an important setting for equitable physical activity promotion. FIT FIRST 10 (FF10) delivers high-intensity, inclusive multisport activities in Danish primary schools. Objectives To examine whether FF10 effects on physical and psychosocial outcomes differed across intersections of sex and parental country of birth (0 or 1 Danish-origin parent [0-1DOP] vs 2 Danish-origin parents [2DOP]), hypothesizing greater benefits among girls with 0-1DOP. Methods This subgroup analysis included 1175 children aged 7-11 years from 27 schools. 1050 had information on sex and parental country of birth (control: n = 413, FF10: n = 637). The analytical sample for the primary outcome (cardiorespiratory fitness) comprised 798 children with complete data for all variables included in the adjusted model. Results FF10 did not improve the primary outcome in any subgroup (all P ≥ .310), and no between-subgroup differences were observed. Within-subgroup effects showed reductions in fat mass, fat mass index, body mass index, and resting heart rate among boys with 2DOP (all P ≤ .026) and improved psychological well-being among girls with 0-1DOP (P = .024). Between-subgroup contrasts favored boys with 2DOP over boys with 0-1DOP for cardiometabolic outcomes and girls with 0-1DOP over girls with 2DOP for psychological well-being. Small within-subgroup declines were observed in self-reported flexibility (girls with 2DOP) and physical condition (boys with 0-1DOP). Conclusion Inclusive multisport programs in Danish primary schools may enhance psychological well-being among girls with 0-1DOP, but uneven physiological effects highlight the need to optimize implementation to achieve equitable benefits.

Eckert C, Krustrup P, Koch S et al.

DevelopmentProtectionPractitionerDKRead at source

Peer-reviewed articleEurope PMC13 Jul 2026

Early childhood exposure to general anesthesia and subsequent risk of learning disabilities: a systematic review and meta-analysis of cohort studies.

Background Potential neurodevelopmental consequences of early childhood exposure to general anesthesia (GA) remain uncertain, with existing studies reporting inconsistent findings regarding later learning disabilities (LDs). This meta-analysis assessed whether early childhood exposure to general anesthesia is associated with subsequent LDs and whether risks differ by exposure frequency, duration, or LD subtype. Methods PubMed, Embase, Ovid, CINAHL, Web of Science, and the Cochrane Library were searched from inception to September 1, 2024. Cohort studies reporting LD outcomes after early childhood GA exposure were included. The primary meta-analysis was restricted to independent cohorts to avoid double-counting participants, whereas overlapping cohorts and studies reporting non-clinical educational outcomes were analyzed separately in sensitivity or secondary analyses. Random-effects models were used to pool risk ratios (RRs), study quality was assessed using the Newcastle-Ottawa Scale, and certainty of evidence was evaluated using GRADE. Results Seven cohort studies comprising 167,848 children were analyzed. The primary meta-analysis, restricted to three independent cohorts reporting clinically diagnosed learning disabilities (LDs), showed that early childhood exposure to general anesthesia (GA) was associated with an increased risk of subsequent LDs (RR = 1.23, 95% CI: 1.06-1.43). Exploratory subgroup analyses showed no significant association following a single GA exposure (RR = 1.02, 95% CI: 0.89-1.16), whereas multiple GA exposures were associated with a significantly increased risk of LDs (RR = 2.29, 95% CI: 1.72-3.05). Similarly, no increased risk was observed for GA durations shorter than 60 min, whereas exposures longer than 60 min were associated with progressively higher risk estimates. The certainty of evidence ranged from very low to moderate, with low-certainty evidence for the primary outcome. Conclusion Low-certainty evidence suggests that early childhood exposure to GA is associated with an increased risk of subsequent LDs, with larger pooled effect estimates observed following repeated or prolonged exposures. These findings should be interpreted cautiously because residual confounding related to underlying disease, surgical complexity, and hospitalization cannot be excluded. In addition, all included cohorts comprised children born in 2000 or earlier; therefore, the findings may not fully reflect contemporary pediatric anesthesia practice. Further well-designed prospective studies are required. Trial registration PROSPERO, CRD42024603831.

Zhou H, Jin Y, Zhou H et al.

DevelopmentExperimentalRead at source

Peer-reviewed articleEurope PMC28 Jul 2026

Path and Bayesian network analyses in the complex design of a well-being survey via New Zealand's Integrated Data Infrastructure.

Introduction In mental health research involved with sensitive features and privacy issues, using integrated data offers an efficient alternative to traditional approaches such as interviews or in-person data collection. These conventional methods frequently face logistical barriers, including low response rates among study populations. Using integrated data from multiple existing administrative and survey sources provides protection for participants and economic savings for researchers, despite being constrained by the limitations of the original data sources. Our research questions were: 1) Can we conduct path and network analyses of school absenteeism, psychosocial factors, and mental health outcomes using the integrated survey data from New Zealand's Integrated Data Infrastructure (IDI)? and 2) How can we account for the complex design of the General Social Survey (GSS) to ensure representative inference? Materials and methods The study population was New Zealand youth enrolled in school, aged 15 years and older in 2018, who participated in the 2018 GSS. The study analyzed the Ministry of Education data integrated with the 2018 GSS survey data from New Zealand's IDI. To explore the relationship between outcomes of including the WHO-5, "perceived life worthwhileness", "perceived general health", and predictors including school absenteeism, psychosocial factors, and family factors, quantile mixed-effects regression, path analysis, and Bayesian network (BN) analysis were used. The complex survey design was calibrated using replicated weights from the GSS, a design-based method for complex sampling, in path and regression analyses, and bootstrap resampling, in BN analysis. Results In descending order from the weighted path model, overall life satisfaction (standardized path coefficient: 0.36), perceived health condition (0.34), ease in accepting cultural identity (0.12), and trust in the education system (0.07) were all significantly (positively) related to students' mental health well-being (WHO-5). Perceived life worthwhileness correlated with the WHO-5 but was only connected to overall life satisfaction. Similar factors were identified for perceived health, with additional attributing factors, such as fear of crime in the area and the family's overall well-being. Conclusion The integration of Bayesian networks and path analysis offers rigorous methods for detecting complex interrelationships among integrated mental health outcomes from population data and variables from a complex survey design.

Zeng IS, Hao J, Foster M et al.

DevelopmentNZRead at source

Peer-reviewed articleEurope PMC11 Aug 2026

The Pediatric Pulmonary Hypertension International Risk Score: A Prediction Model for Outcomes Using Machine Learning.

Background Risk prediction is fundamental to pulmonary hypertension (PH) guideline-based care, yet pediatric-specific risk prediction models remain limited, relying primarily on single predictors, expert opinion, or application of adult models to children. The authors developed and externally validated a data-driven 1-year risk prediction model for pediatric PH. Methods Pediatric patients with PH (n=345; World Symposium on Pulmonary Hypertension groups 1 and 3) enrolled in the Pediatric Pulmonary Hypertension Network Registry (2014-2020; 50.4% male; median age, 4.9 years [interquartile range, 1.9-10.3]) were split into training (80%) and test cohorts (20%). The Dutch National Registry for Pulmonary Hypertension in Childhood (n=155 [1993-2020]) and the Spanish Registry of Pediatric Pulmonary Hypertension (n=327 [2009-2023]) were used for external validation. From 176 variables, BorutaSHAP feature selection with random forest identified 16 predictors for a 1-year outcome of time to death, transplant, Potts shunt, or atrial septostomy, modeled using extreme gradient boosting. Performance was assessed with the area under the receiver operating characteristic curve, confusion matrices, calibration, and Kaplan-Meier event-free survival. Results The final model achieved an area under the receiver operating characteristic curve of 0.90 (0.79-0.97) and 99% (96%-99%) negative predictive value in testing, dividing participants into 3 groups with strong outcome discrimination. External validation showed an area under the receiver operating characteristic curve of 0.76 (Dutch National Registry for Pulmonary Hypertension in Childhood, 0.70-0.81) and 0.77 (Spanish Registry of Pediatric Pulmonary Hypertension, 0.73-0.82) with negative predictive values of 93% (93%-97%) and 96% (93%-97%), respectively. Kaplan-Meier analysis significantly differentiated outcomes by risk group. Conclusions This multicenter, validated model provides good 1-year risk prediction in pediatric PH across World Symposium on Pulmonary Hypertension groups 1 and 3, providing a robust tool for clinical risk stratification to guide therapy and addressing a gap in pediatric PH care.

Griffiths M, Chinni BK, Lokhorst C et al.

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Peer-reviewed articleEurope PMC7 Aug 2026

Computer Games on the Development of Physical Motor Skills and Social Interaction in Preschool Children: Quasi-Experimental Study.

Background As information technology becomes increasingly pervasive in early childhood education, the impact of digital gameplay on developmental outcomes has emerged as a critical research frontier. Objective This study evaluates the efficacy of computer game-based instructional activities in enhancing the gross motor skills and social interactions of preschool children. Methods This prospective quasi-experimental study involved 61 preschool children (aged 4-6 y; 36 males, 25 females) recruited via convenience sampling from a private preschool in central Taiwan. The selection criteria included regular attendance and the absence of severe physical or cognitive disabilities. Participants were assigned to either an experimental group (n=41) or a control group (n=20) based on existing classroom structures. The intervention spanned 4 weeks, featuring 1-hour weekly computer game sessions integrated into the curriculum, while the control group followed the standard curriculum. Data were gathered through preintervention and postintervention assessments using standardized scales for physical motor and social interaction development, supplemented by a learning interest questionnaire and structured qualitative observations. Results We used an analysis of covariance to assess the impact of interventions on the physical motor and social development of preschool children, adjusting for baseline differences using prior test scores as covariates. The experimental group exhibited significantly greater improvements in total physical motor development scores compared to the control group (mean 4.33, SD 0.06 vs mean 4.02, SD 0.09; P<.001). Specifically, marked gains were observed in advanced motor skills: standing on one leg for 5 seconds, walking heel-to-toe for 5 steps, and hopping on one leg 5 times. Post intervention, no statistically significant differences were found between groups regarding total social interaction scores. However, the children reported high engagement, with a mean preference score of 4.18 (SD 0.79). Qualitative observations of 3 children with developmental delays revealed a transformative shift from passive observation to active social engagement and positive affect. Conclusions This study demonstrates that digital game interventions can effectively facilitate gross physical motor gains in early childhood. It is innovative in its integration of digital gameplay specifically targeted at gross motor skill acquisition. Furthermore, it differs from existing literature by providing empirical evidence of how game-based instruction can be seamlessly embedded into preschool physical education. This study highlights the potential of computer game-based instruction as a viable supplementary tool for physical education.

Chang IC, Yen CE

DevelopmentExperimentalTWRead at source

Peer-reviewed articleEurope PMC31 Jul 2026

Machine Learning Techniques to Predict Fetal Nutritional Status.

Objectives Malnutrition remains the leading cause of child mortality in Tanzania, with over 34% of children under 5 years of age affected by stunting and approximately 5% experiencing acute malnutrition. This study aimed to develop a machine learning model to predict fetal nutritional status using maternal and clinical data, thereby enabling early risk identification for health workers and parents and facilitating timely intervention. To enhance practical applicability, the model was deployed within a mobile application to provide accessible, real-time predictions that support prompt clinical and behavioral responses. Methods Using a dataset collected in Tanzania, the performance of multiple binary classification algorithms-logistic regression, multi-layer perceptron, random forest, extreme gradient boosting, and light gradient boosting machine (LightGBM)-was compared using the geometric mean and F-measure. These models were trained on clinical data from 11,703 pregnant women to predict fetal nutritional status based on maternal and clinical variables. Results The results indicated that the LightGBM algorithm achieved the best overall performance in predicting fetal nutritional status. The most influential predictors included maternal age, weight, fetal age, hemoglobin level, number of meals per day, medical history, and education level. Additionally, 93% of respondents reported satisfaction with the application's predictive functionality, supporting its potential utility for early intervention in low-resource settings. Conclusions These findings highlight the potential of data-driven approaches to address public health challenges in maternal and child health. The proposed model may enable healthcare providers to make timely, informed decisions that improve maternal and fetal outcomes, ultimately contributing to the reduction of child malnutrition in Tanzania.

Mduma N, Laizer H

SurvivalDevelopmentPractitionerTZRead at source

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