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Improving Evidence-Based Practice Through the Development and Validation of Key Words to Identify Intervention
Sonia Islam Nisha1, Qingyun Wang2, Charles Yu2
1Department of Communicative Disorders and Sciences, University at Buffalo, NY.
Developing evidence-based keywords can improve the identification of intervention studies in speech-language pathology, overcoming challenges in implementing evidence-based practice (EBP). This aids clinicians in efficiently finding relevant research.
Area of Science:
- Speech-language pathology
- Clinical research
- Information science
Background:
- Implementing evidence-based practice (EBP) in speech-language pathology is hindered by a scarcity of intervention studies and inconsistent keyword usage.
- Efficiently identifying relevant research from abstracts is crucial for clinicians facing time constraints.
Purpose of the Study:
- To compare the classification accuracy of AI-generated keywords versus evidence-based keywords derived from intervention study abstracts.
- To assess the effectiveness of different keyword sets in identifying intervention studies within ASHA journals.
Main Methods:
- A database of abstracts from three ASHA journals was created.
- 180 abstracts were annotated to develop evidence-based keywords.
- Classification accuracy was evaluated by comparing evidence-based keywords against AI-generated keywords (ChatGPT-4.0).
Main Results:
- Approximately 12%-15% of studies reported on intervention research.
- Evidence-based keywords demonstrated higher sensitivity (85%) and positive predictive value (27%) but lower specificity (67%).
- Evidence-based keywords showed moderate accuracy in identifying non-intervention studies.
Conclusions:
- Evidence-based keywords show promise in accurately classifying intervention studies, addressing a significant barrier to EBP implementation.
- Future work should integrate AI to refine keywords and advocate for standardized keyword reporting in journal abstracts.
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