We investigated the relationship between process quality in early childhood education and care (ECEC) and children’s socio-emotional development in a meta-analysis of longitudinal studies. Our multi-level meta-analysis of 31 publications reporting on 16 longitudinal studies (N = 17,913 children, age: 2.5–18 yrs) demonstrates that the process quality of ECEC is a small but significant predictor of children’s socio-emotional development over time (ES = 0.103, SE = 0.026, p < 0.001, 95% CI: 0.052–0.155). This longitudinal association extends to the age of 18 years in our sample. Process quality of ECEC is, thus, a significant and stable predictor of children’s socio-emotional development and well-being from toddlerhood to adolescence. The longitudinal relationship was moderated by the type of care (center-based vs. home-based) and the informant (parent, professional caregiver, external assessor, or self-report of the child). Implications for future ECEC research are discussed.
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Objective: To evaluate psychometrics of wearable devices measuring physical activity (PA) in ambulant children with gait abnormalities due to neuromuscular conditions. Data Sources: We searched PubMed, Embase, PsycINFO, CINAHL, and SPORTDiscus in March 2023. Study Selection: We included studies if (1) participants were ambulatory children (2-19y) with gait abnormalities, (2) reliability and validity were analyzed, and (3) peer-reviewed studies in the English language and full-text were available. We excluded studies of children with primarily visual conditions, behavioral diagnoses, or primarily cognitive disability. We performed independent screening and inclusion, data extraction, assessment of the data, and grading of results with 2 researchers. Data Extraction: Our report follows Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We assessed methodological quality with Consensus-based Standards for the selection of health measurement instruments. We extracted data on reported reliability, measurement error, and validity. We performed meta-analyses for reliability and validity coefficient values. Data Synthesis: Of 6911 studies, we included 26 with 1064 participants for meta-analysis. Results showed that wearables measuring PA in children with abnormal gait have high to very high reliability (intraclass correlation coefficient [ICC]+, test-retest reliability=0.81; 95% confidence interval [CI], 0.74-0.89; I2=88.57%; ICC+, interdevice reliability=0.99; 95% CI, 0.98-0.99; I2=71.01%) and moderate to high validity in a standardized setting (r+, construct validity=0.63; 95% CI, 0.36-0.89; I2=99.97%; r+, criterion validity=0.68; 95% CI, 0.57-0.79; I2=98.70%; r+, criterion validity cutoff point based=0.69; 95% CI, 0.58-0.80; I2=87.02%). The methodological quality of all studies included in the meta-analysis was moderate. Conclusions: There was high to very high reliability and moderate to high validity for wearables measuring PA in children with abnormal gait, primarily due to neurological conditions. Clinicians should be aware that several moderating factors can influence an assessment.
DOCUMENT
Recent research has indicated an increase in the likelihood and impact of tree failure. The potential for trees to fail relates to various biomechanical and physical factors. Strikingly, there seems to be an absence of tree risk assessment methods supported by observations, despite an increasing availability of variables and parameters measured by scientists, arborists and practitioners. Current urban tree risk assessments vary due to differences in experience, training, and personal opinions of assessors. This stresses the need for a more objective method to assess the hazardousness of urban trees. The aim of this study is to provide an overview of factors that influence tree failure including stem failure, root failure and branch failure. A systematic literature review according to the PRISMA guidelines has been performed in databases, supported by backward referencing: 161 articles were reviewed revealing 142 different factors which influenced tree failure. A meta-analysis of effect sizes and p-values was executed on those factors which were associated directly with any type of tree failure. Bayes Factor was calculated to assess the likelihood that the selected factors appear in case of tree failure. Publication bias was analysed visually by funnel plots and results by regression tests. The results provide evidence that the factors Height and Stem weight positively relate to stem failure, followed by Age, DBH, DBH squared times H, and Cubed DBH (DBH3) and Tree weight. Stem weight and Tree weight were found to relate positively to root failure. For branch failure no relating factors were found. We recommend that arborists collect further data on these factors. From this review it can further be concluded that there is no commonly shared understanding, model or function available that considers all factors which can explain the different types of tree failure. This complicates risk estimations that include the failure potential of urban trees.
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