Publications by authors named "Jia-Qi Jing"
Article Synopsis
- The Gilliam Autism Rating Scale-Third Edition (GARS-3) has been adapted to create the Chinese version (CV-GARS-3) aimed at improving autism spectrum disorder (ASD) screening in China due to a lack of adaptive research.
- The study involved 362 individuals with ASD, 126 with typical development, and 103 with other disorders to assess the psychometric properties of CV-GARS-3, revealing overall strong reliability and validity across its subscales.
- Results indicated that CV-GARS-3 is a promising tool for ASD screening, with high sensitivity and specificity; however, improvements are needed for better cultural adaptation in the Chinese context.
The impact of the Coronavirus Disease 2019 (COVID-19) on society is continuous, resulting in negative psychological consequences. Given the vulnerability and sensitivity to the environment among preschool children, their emotional and behavioral problems deserve more attention. The current study aimed to explore the impact of the epidemic on preschool children's mental health by determining the pooled prevalence of emotional and behavioral problems amidst the Coronavirus Disease 2019 pandemic and to reveal potential reasons for variations between studies.
View Article and Find Full Text PDFEarly childhood serves as a critical period for neural development and skill acquisition when children are extremely susceptible to the external environment and experience. As a crucial experiential stimulus, physical activity is believed to produce a series of positive effects on brain development, such as cognitive function, social-emotional abilities, and psychological well-being. The World Health Organization recommends that children engage in sufficient daily physical activity, which has already been strongly advocated in the practice of preschool education.
View Article and Find Full Text PDFA Review on Autism Spectrum Disorder Screening by Artificial Intelligence Methods.
J Autism Dev Disord
June 2024
Article Synopsis
- The study examines the role of artificial intelligence in early screening and diagnosis of autism spectrum disorders (ASD), highlighting the need for improved detection methods due to subtle developmental differences.
- A review of 43 research articles categorized recognition markers into gaze behaviors, facial expressions, motor movements, voice features, and task performance, reporting AI screening accuracy ranging from 62.13% to 100%.
- The findings suggest that while AI recognition shows promise for identifying ASD in children, ongoing enhancements in the screening models are necessary for better accuracy and effective early intervention.