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Full-Text Articles in Medicine and Health Sciences

Ehealth Education And Support For Pediatric Hearing Aid Management: Parent Goals, Questions And Challenges, Natalie Nichols, Karen F. Munoz, Guadalupe G. San Miguel, Michael P. Twohig Jan 2022

Ehealth Education And Support For Pediatric Hearing Aid Management: Parent Goals, Questions And Challenges, Natalie Nichols, Karen F. Munoz, Guadalupe G. San Miguel, Michael P. Twohig

Communicative Disorders and Deaf Education Student Research

Purpose: To investigate parent goals, questions, and challenges that emerged during coaching phone calls in an eHealth program designed to provide education and support for hearing aid management.

Methods: Coaching phone calls were audio-recorded, transcribed and qualitatively analyzed for emergent themes within the categories of goals, questions, and challenges.

Results: Emergent themes revealed parent goals were focused on self-efficacy, routines, device care and child development. Emergent themes for questions revealed parents asked questions related to the device care, audiology appointments, confirmation of learning, and child development. For challenges emergent themes revealed parents’ own struggles (e.g., with emotions), issues related to …


Autoscore: An Open-Source Automated Tool For Scoring Listener Perception Of Speech, Stephanie A. Borrie, Tyson S. Barrett, Sarah E. Yoho Leopold Jan 2019

Autoscore: An Open-Source Automated Tool For Scoring Listener Perception Of Speech, Stephanie A. Borrie, Tyson S. Barrett, Sarah E. Yoho Leopold

Psychology Faculty Publications

Speech perception studies typically rely on trained research assistants to score orthographic listener transcripts for words correctly identified. While the accuracy of the human scoring protocol has been validated with strong intra- and inter-rater reliability, the process of hand-scoring the transcripts is time-consuming and resource intensive. Here, an open-source computer-based tool for automated scoring of listener transcripts is built (Autoscore) and validated on three different human-scored data sets. Results show that not only is Autoscore highly accurate, achieving approximately 99% accuracy, but extremely efficient. Thus, Autoscore affords a practical research tool, with clinical application, for scoring listener intelligibility of speech.