Predictors of Treatment Response for Preschool Children With Developmental Language Disorder
AffiliationUniv Arizona, Dept Speech Language & Hearing Sci
Univ Arizona, Dept Epidemiol & Biostat
MetadataShow full item record
CitationKapa, L. L., Meyers-Denman, C., Plante, E., & Doubleday, K. (2020). Predictors of Treatment Response for Preschool Children With Developmental Language Disorder. American Journal of Speech-Language Pathology, 29, 2082-2096.
RightsCopyright © 2020 American Speech-Language-Hearing Association
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at email@example.com.
AbstractPurpose: Enhanced Conversational Recast treatment is an effective intervention for remediating expressive grammatical deficits in preschool age children with developmental language disorder (DLD), but not all children respond equally well. In this study, we sought to identify which child-level variables predict response to treatment of morphological deficits. Method: Predictor variables of interest, including pre-intervention test scores and target morpheme production, age, and mother’s level of education (proxy for socio-economic status) were included in analyses. The sample included 105 children (M = 5;1) with DLD who participated in five weeks of daily Enhanced Conversational Recast treatment. Classification and regression tree analysis was used to identify covariates that predicted children’s generalization of their trained grammatical morpheme, as measured by treatment effect size d. Results: Our analysis indicates that SPELT-P 2 scores and PPVT-4 scores significantly predicted the degree of benefit a child derived from Enhanced Conversational Recast treatment. Specifically, a SPELT-P 2 score above 75 (but still in the impaired range, < 87) combined with a high PPVT-4 score (> 100) yielded the largest treatment effect size, whereas a SPELT-P 2 score below 75 predicted the smallest treatment effect size. Other variables included in the model did not significantly predict treatment outcomes. Conclusion: Understanding individual differences in response to treatment will allow service providers to make evidence-based decisions regarding how likely a child is to benefit from Enhanced Conversational Recast treatment and the expected magnitude of the response based on the child’s background characteristics.
VersionFinal accepted manuscript
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