Observer-Independent Assessment of Content Overlap in Mental Health Questionnaires: Large Language Model–Based Study

Observer-Independent Assessment of Content Overlap in Mental Health Questionnaires: Large Language Model–Based Study

Mental disorders are frequently evaluated using questionnaires, which have been developed over the past decades for the assessment of different conditions. Despite the rigorous validation of these tools, high levels of content divergence have been reported for questionnaires measuring the same construct of psychopathology. Previous studies that examined the content overlap required manual symptom labeling, which is observer-dependent and time-consuming.

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Using large language models (LLMs) like GPT to analyze mental health questionnaires can objectively reveal how much different tools actually overlap in the symptoms they assess, reducing the time and subjectivity of traditional expert reviews.



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