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Clinician Proposed Predictors of Spoken Language Outcomes for Minimally Verbal Children with Autism Spectrum Disorder.

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Title: Clinician Proposed Predictors of Spoken Language Outcomes for Minimally Verbal Children with Autism Spectrum Disorder.
Authors: Trembath, David, Sutherland, Rebecca, Caithness, Teena, Dissanayake, Cheryl, Eapen, Valsamma, Fordyce, Kathryn, Frost, Grace, Iacono, Teresa, Mahler, Nicole, Masi, Anne, Paynter, Jessica, Pye, Katherine, Reilly, Sheena, Rose, Veronica, Sievers, Stephanie, Thirumanickam, Abirami, Westerveld, Marleen, Tucker, Madonna
Source: Journal of Autism & Developmental Disorders. 2021, Vol. 51 Issue 2, p564-575. 12p. 2 Charts.
Subjects: Treatment of autism, Child behavior, Communication, Conceptual structures, Experience, Focus groups, Language & languages, Learning, Medical practice, Social participation, Speech evaluation, Speech therapists, Verbal behavior, Qualitative research, Treatment effectiveness, Attitudes of medical personnel, Children
Abstract: Our aim was to explore insights from clinical practice that may inform efforts to understand and account for factors that predict spoken language outcomes for children with Autism Spectrum Disorder who use minimal verbal language. We used a qualitative design involving three focus groups with 14 speech pathologists to explore their views and experiences. Using the Framework Method of analysis, we identified 9 themes accounting for 183 different participant references to potential factors. Participants highlighted the relevance of clusters of fine-grained social, communication, and learning behaviours, including novel insights into prelinguistic vocal behaviours. The participants suggested the potential value of dynamic assessment in predicting spoken language outcomes. The findings can inform efforts to developing clinically relevant methods for predicting children's communication outcomes. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Autism & Developmental Disorders is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Clinician Proposed Predictors of Spoken Language Outcomes for Minimally Verbal Children with Autism Spectrum Disorder.
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Autism+%26+Developmental+Disorders%22">Journal of Autism & Developmental Disorders</searchLink>. 2021, Vol. 51 Issue 2, p564-575. 12p. 2 Charts.
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  Data: Our aim was to explore insights from clinical practice that may inform efforts to understand and account for factors that predict spoken language outcomes for children with Autism Spectrum Disorder who use minimal verbal language. We used a qualitative design involving three focus groups with 14 speech pathologists to explore their views and experiences. Using the Framework Method of analysis, we identified 9 themes accounting for 183 different participant references to potential factors. Participants highlighted the relevance of clusters of fine-grained social, communication, and learning behaviours, including novel insights into prelinguistic vocal behaviours. The participants suggested the potential value of dynamic assessment in predicting spoken language outcomes. The findings can inform efforts to developing clinically relevant methods for predicting children's communication outcomes. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Autism & Developmental Disorders is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=pbh&AN=148320293
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