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Prenatal exposure to vitamin D is thought to be critical for optimal fetal neurodevelopment, yet vitamin D deficiency is apparent in a growing proportion of...
The current study provides preliminary evidence that machine learning algorithms provide equivalent predictive accuracy to traditional methods for language difficulties in middle childhood
Fiona Pete Stanley Azzopardi FAA FASSA MSc MD FFPHM FAFPHM FRACP FRANZCOG HonDSc HonDUniv HonFRACGP HonMD HonFRCPCH HonLLB (honoris causa) PhD, FRACP
The aim of this study was to investigate the language outcomes of 7-year-old children with and without a history of late language emergence at 24 months.
The primary objectives of this study were to determine the prevalence of late language emergence (LLE) and to investigate the predictive status of maternal...
The increasing need for speech and language therapy (SLT) services, coupled with poor employment retention rates, poses serious cost-benefit considerations.
The aim of this research note is to encourage child language researchers and clinicians to give careful consideration to the use of domain-specific tests as a proxy for language; particularly in the context of large-scale studies and for the identification of language disorder in clinical practice.
Natural Language Sampling (NLS) offers clear potential for communication and language assessment, where other data might be difficult to interpret. We leveraged existing primary data for 18-month-olds showing early signs of autism, to examine the reliability and concurrent construct validity of NLS-derived measures coded from video-of child language, parent linguistic input, and dyadic balance of communicative interaction-against standardised assessment scores. Using Systematic Analysis of Language Transcripts (SALT) software and coding conventions, masked coders achieved good-to-excellent inter-rater agreement across all measures.
The idea of the '30 million word gap' suggests families from more socioeconomically advantaged backgrounds engage in more verbal interactions with their child than disadvantaged families. Initial findings from the Language in Little Ones (LiLO) study up to 12 months showed no word gap between maternal education groups.
Unmet language and literacy needs are common among young people who are involved with youth justice systems. However, there is limited research regarding the functional text-level language skills of this population with regard to narrative macrostructure (story grammar) and microstructure (semantics and syntax) elements. In this study, we examined macrostructure and microstructure elements in the oral and written narrative texts of 24 adolescent students of a youth detention centre. The students, who were aged 14- to 17- years, were all speakers of Standard Australian English, and 11 (46%) students met criteria for language disorder (LD).