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Improving Evidence-Based Practice Through the Development and Validation of Key Words to Identify Intervention Articles in Communication Disorders.

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Title: Improving Evidence-Based Practice Through the Development and Validation of Key Words to Identify Intervention Articles in Communication Disorders.
Authors: Nisha, Sonia Islam1, Wang, Qingyun2, Yu, Charles2, Ji, Heng2, Hendricks, Alison Eisel1 ahendric@buffalo.edu
Source: Language, Speech & Hearing Services in Schools. Apr2026, Vol. 57 Issue 2, p525-540. 16p.
Subject Terms: *Database searching, *Data analysis, *Artificial intelligence, *Speech-language pathology, *Experimental design, *Inter-observer reliability, Treatment of communicative disorders, Subject headings, Research funding, Research evaluation, Statistical sampling, Data curation, Natural language processing, Statistics, Acquisition of data, Medical research, Evidence-based medicine, Sensitivity & specificity (Statistics), Abstracting & indexing services
Company/Entity: American Speech-Language-Hearing Association
Abstract: Purpose: Although evidence-based practice (EBP) promotes better clinical practice, implementing it in speech-language pathology is challenging. A limited number of intervention studies and inconsistent use of key words in abstracts complicates the implementation of EBP. Given time constraints in clinical practice, identifying relevant research from study abstracts would provide an efficient method for identifying intervention studies. In this project, we compare the classification accuracy of an artificial intelligence (AI)-created set of key words and a set of evidencebased key words that were developed through an analysis of commonly used words in intervention abstracts from American Speech-Language-Hearing Association (ASHA) journals. Method: Abstracts from three major ASHA journals were crawled using Selenium and WebDriver Manager, creating a large database of abstracts for analysis. In Study 1, a random sample of 180 abstracts was annotated as reporting on intervention or nonintervention studies to develop a set of evidence-based key words. In Study 2, classification accuracy was calculated to validate these key words by comparing them with a set of AI-generated (ChatGPT-4.0) key words. Results: The results suggested that 12%-15% of studies report on an intervention study. Evidence-based key words had higher sensitivity (85%) and a higher positive predictive value (27%) but a lower specificity rate (67%). Also, the likelihood ratio suggested that evidence-based key words had a moderate capacity to identify nonintervention studies (true negatives) accurately. Conclusions: Evidence-based key words have the potential to accurately classify intervention studies, addressing a key barrier to EBP implementation in clinical practice. Future research should focus on refining key words by integrating AI and promoting standardized journal reporting by incorporating uniform key words in abstracts. [ABSTRACT FROM AUTHOR]
Copyright of Language, Speech & Hearing Services in Schools 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.)
Database: Education Research Complete
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  Data: Improving Evidence-Based Practice Through the Development and Validation of Key Words to Identify Intervention Articles in Communication Disorders.
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  Data: <searchLink fieldCode="AR" term="%22Nisha%2C+Sonia+Islam%22">Nisha, Sonia Islam</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Wang%2C+Qingyun%22">Wang, Qingyun</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Yu%2C+Charles%22">Yu, Charles</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Ji%2C+Heng%22">Ji, Heng</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Hendricks%2C+Alison+Eisel%22">Hendricks, Alison Eisel</searchLink><relatesTo>1</relatesTo><i> ahendric@buffalo.edu</i>
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  Data: <searchLink fieldCode="JN" term="%22Language%2C+Speech+%26+Hearing+Services+in+Schools%22">Language, Speech & Hearing Services in Schools</searchLink>. Apr2026, Vol. 57 Issue 2, p525-540. 16p.
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  Data: Purpose: Although evidence-based practice (EBP) promotes better clinical practice, implementing it in speech-language pathology is challenging. A limited number of intervention studies and inconsistent use of key words in abstracts complicates the implementation of EBP. Given time constraints in clinical practice, identifying relevant research from study abstracts would provide an efficient method for identifying intervention studies. In this project, we compare the classification accuracy of an artificial intelligence (AI)-created set of key words and a set of evidencebased key words that were developed through an analysis of commonly used words in intervention abstracts from American Speech-Language-Hearing Association (ASHA) journals. Method: Abstracts from three major ASHA journals were crawled using Selenium and WebDriver Manager, creating a large database of abstracts for analysis. In Study 1, a random sample of 180 abstracts was annotated as reporting on intervention or nonintervention studies to develop a set of evidence-based key words. In Study 2, classification accuracy was calculated to validate these key words by comparing them with a set of AI-generated (ChatGPT-4.0) key words. Results: The results suggested that 12%-15% of studies report on an intervention study. Evidence-based key words had higher sensitivity (85%) and a higher positive predictive value (27%) but a lower specificity rate (67%). Also, the likelihood ratio suggested that evidence-based key words had a moderate capacity to identify nonintervention studies (true negatives) accurately. Conclusions: Evidence-based key words have the potential to accurately classify intervention studies, addressing a key barrier to EBP implementation in clinical practice. Future research should focus on refining key words by integrating AI and promoting standardized journal reporting by incorporating uniform key words in abstracts. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Language, Speech & Hearing Services in Schools 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.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ehh&AN=192859142
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        Value: 10.1044/2025_LSHSS-25-00144
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        Text: English
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      – SubjectFull: Speech-language pathology
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              Text: Apr2026
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