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Natural Language Response Formats for Assessing Depression and Worry With Large Language Models: A Sequential

Zhuojun Gu1, Katarina Kjell1, H Andrew Schwartz2

  • 1Lund University, Skåne, Sweden.

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Summary
This summary is machine-generated.

Large language models effectively score mental health from various text formats. Different response types show high validity and reliability, suggesting flexibility for clinical use.

Keywords:
anxietyartificial intelligencedepressionlarge language modelsnatural languagenatural language processingpsychological assessment

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Area of Science:

  • Natural Language Processing
  • Computational Psychiatry
  • Machine Learning in Healthcare

Background:

  • Large language models (LLMs) can quantify mental health from user descriptions.
  • Previous LLM analyses often combined diverse text response types.
  • The differential impact of response formats on LLM analysis validity and reliability is not well understood.

Purpose of the Study:

  • To develop and compare the validity and reliability of LLM-based mental health scoring across different response formats.
  • To investigate the performance of LLMs using closed-ended (word lists) to open-ended (text) response formats.
  • To assess the external validity of LLM-derived scores against clinical outcomes like sick leave.

Main Methods:

  • Developed four response formats: word lists, descriptive words, phrases, and free text.
  • Trained machine learning models on word embeddings from participant responses (N=963) to predict depression/worry scores.
  • Employed a Sequential Evaluation with Model Pre-Registration (SEMPR) design, testing pre-registered models on a prospective sample (N=145).
  • Evaluated concurrent, incremental, face, discriminant, and external validity, alongside prospective and test-retest reliability.

Main Results:

  • Pre-registered models demonstrated strong validity and reliability, achieving high accuracy (r=0.60-0.79) in the prospective sample.
  • LLM analyses showed external validity, correlating with self-reported sick leave and healthcare visits.
  • The free-text response format yielded the strongest correlations with external outcomes, matching or exceeding traditional rating scales in most cases (9/12).

Conclusions:

  • LLM-based mental health assessment is valid and reliable across various response formats.
  • The choice of response format can be tailored to specific clinical needs and desired outcomes.
  • LLMs offer a flexible and accurate tool for mental health quantification, with text formats showing particular promise for external validity.