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A Multistate Look Into Early Intervention Speech-Language Pathologists' Confidence Identifying and Diagnosing Autism.
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- معلومة اضافية
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- نبذة مختصرة :
Purpose: Although autism can be reliably diagnosed by 18 months of age, long wait times and limited access to qualified providers prevent families from obtaining timely diagnostic services. Trained speech-language pathologists (SLPs) are qualified to diagnose autism, ideally as part of a multidisciplinary team. SLPs working on early intervention (EI) teams are well-situated to help close this diagnostic wait time gap. The purpose of this survey study was to explore EI SLPs' experiences serving autistic children, confidence in identifying and diagnosing autism, and their perceptions of barriers or facilitators to diagnostic confidence and wait times. Method: Two hundred eighty-seven EI SLPs from 23 states responded to survey questions about experiences, beliefs, and confidence in diagnosing autism. Descriptive and inferential statistics were conducted to determine self-reported confidence, barriers, and facilitators. Results: An overwhelming majority of EI SLPs felt confident in their ability to identify autism in toddlers. However, reported confidence in their ability to diagnose autism was much lower. Greater awareness of diagnosis as within our scope of practice, promoting autism acceptance (reducing caregiver resistance), and access to diagnostic tool training and diagnostic experts would reportedly increase confidence and potentially facilitate more timely diagnosis. Conclusions: Findings indicate that EI SLPs are confident in their ability to identify autism in toddlers; however, several barriers prevent them from diagnosing autism. Eliminating these barriers could help EI SLPs reduce the long diagnostic wait times experienced by families. [ABSTRACT FROM AUTHOR]
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Copyright of American Journal of Speech-Language Pathology is the property of American Speech-Language-Hearing Association 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.)
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