Using machine-learning methods to identify early-life predictors of 11-year language outcome

Bringing you some selected Open Access journal papers from our portfolio; The Journal of Child Psychology and Psychiatry (JCPP), Child and Adolescent Mental Health journal (CAMH), and JCPP Advances.

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Open Access paper from the JCPP

Background – Language is foundational for neurodevelopment and quality of life, but an estimated 10% of children have a language disorder at age 5. Many children shift between classifications of typical and low language if assessed at multiple times in the early years, making it difficult to identify which children will have persisting difficulties and benefit most from support. This study aims to identify a parsimonious set of preschool indicators that predict language outcomes in late childhood, using data from the population-based Early Language in Victoria Study (n = 839).

Authors; Loretta Gasparini, Daisy A. Shepherd, Edith L. Bavin, Patricia Eadie, Sheena Reilly, Angela T. Morgan, Melissa Wake

First published: 07 December 2022

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