The incidence of autism has increased tremendously over the past 20 years; however, the tools used for diagnosis and educational identification have largely remained the same. Diagnostic and educational decisions are based on observations and interactions to identify hallmark skill deficits associated with autism. Research demonstrates behaviors are affected by the environment, and real-time data collection is more accurate than reflective methods.
The problem is current autism diagnostic and educational identification tools lack essential features. The Autism Diagnostic Observation Schedule (ADOS), considered the gold standard, relies on contrived settings, and lacks an observational comparison to same aged peers. Autism behavior rating scales, rely on reflective data collection, and are not context specific.
The purpose of this study was to gain feedback from stakeholders (clinicians, educators, parents) about the perceived efficacy and feasibility of a context-specific autism behavior rating tool with real time data collection methods for diagnosis and educational planning. Results showed stakeholders confirmed the perceived efficacy of the context-specific tool for improved accuracy for diagnosis, more specific information for educational planning, and increased understanding for parents to support their child's learning needs.
Even though the tool was useful, stakeholder feedback also indicated the tool lacked feasibility for teacher use and may be better suited for administration by school psychologists or other trained professionals. The increased specificity and accuracy provided by a context-specific autism behavior rating tool has the potential to affect the future of autism evaluations and educational planning; thereby influencing the future life function of individuals with autism.
| Identifer | oai:union.ndltd.org:pdx.edu/oai:pdxscholar.library.pdx.edu:open_access_etds-4302 |
| Date | 14 November 2016 |
| Creators | Panaccione, Kathleen Marie |
| Publisher | PDXScholar |
| Source Sets | Portland State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Dissertations and Theses |
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