Autism

1009L - The Efficacy of Telepractice to Deliver Evidence Based Interventions to Children with ASD

Thursday, November 17, 2022

10:30 AM – 11:30 AM CT

Format: 1-Hour Seminar (In-Person)

Audience: Students, International Affiliates, Researchers, Related Professionals, Assistants, Early Career Professionals (under 5 years)

Keywords: Autism, Telepractice, Single-subject design, Visual analysis, Student outcomes

Presenting Author(s)

Mary Andrianopoulos

University of Massachusetts Amherst
Myranda Marotta

University of Massachusetts Amherst
Colleen Gargan

Syracuse University
Sarah Fefer

University of Massachusetts Amherst School Psychology

Author Only (Not Presenting)(s)

Craig Wells

University of Massachusetts Amherst Center for Educational Assessment

The COVID-19 pandemic forced practitioners around the globe to abruptly implement remote services despite significant challenges. Although this disruption to normalcy was less than ideal, one theme that emerged is that Telepractice is a viable method for providing services to students with autism. Telepractice was found to be preferrable for some students with autism and created the opportunity to achieve individualized goals with good accuracy and minimal assistance. This study utilized a quasi-experimental, single-subject, multiple-group, time-series design. Data were collected for 21 students who participated in Telepractice and on-site phases of SLP evidence-based interventions. Statistical and visual analyses of the data reveal promising evidence that suggests Telepractice services are at least as effective as the in-person delivery models.

Learning Objectives:

Recognize empirical outcomes of evidence-based research regarding Telepractice delivery models for providing speech and language services to children on the autism spectrum
Identify clients with autism who are best suited for Telepractice services
Explain empirical methods to measure student outcomes using Telepractice to deliver evidence-based interventions to students with autism using data driven methods