# Advancing Mental Health Pre-Screening: A New Custom GPT for Psychological Distress Assessment

arXiv: https://arxiv.org/abs/2408.01614 · HTML: https://mlsanity.com/paper/2408.01614/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 24 references that are in the map.
- First posted on arXiv 3 Aug 2024.

## Builds on (most cited first, 1 of 1)

- [AVEC 2019 Workshop and Challenge: State-of-Mind, Detecting Depression with AI, and Cross-Cultural Affect Recognition](https://mlsanity.com/paper/1907.11510/) (2019)

## Built on by (most cited first, 5 of 5)

- [SouLLMate: An Application Enhancing Diverse Mental Health Support with Adaptive LLMs, Prompt Engineering, and RAG Techniques](https://mlsanity.com/paper/2410.16322/) (2024)
- [A Layered Multi-Expert Framework for Long-Context Mental Health Assessments](https://mlsanity.com/paper/2501.13951/) (2025)
- [Attacking LLMs and AI Agents: Advertisement Embedding Attacks Against Large Language Models](https://mlsanity.com/paper/2508.17674/) (2025)
- [When Compliance Data Masquerades as Evaluation: Measurement Validity for Deployed AI Systems](https://mlsanity.com/paper/2609.13642/) (2026)
- [LLMersion: A Local-First AI Agent Framework for Low-Cost Home Language Learning toward Educational Equity](https://mlsanity.com/paper/2609.29672/) (2026)

## Abstract (arXiv)

This study introduces 'Psycho Analyst', a custom GPT model based on OpenAI's GPT-4, optimized for pre-screening mental health disorders. Enhanced with DSM-5, PHQ-8, detailed data descriptions, and extensive training data, the model adeptly decodes nuanced linguistic indicators of mental health disorders. It utilizes a dual-task framework that includes binary classification and a three-stage PHQ-8 score computation involving initial assessment, detailed breakdown, and independent assessment, showcasing refined analytic capabilities. Validation with the DAIC-WOZ dataset reveals F1 and Macro-F1 scores of 0.929 and 0.949, respectively, along with the lowest MAE and RMSE of 2.89 and 3.69 in PHQ-8 scoring. These results highlight the model's precision and transformative potential in enhancing public mental health support, improving accessibility, cost-effectiveness, and serving as a second opinion for professionals.