From "Help" to Helpful: A Hierarchical Assessment of LLMs in Mental e-Health Applications

Philipp Steigerwald; Jens Albrecht

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arXiv · 2602.18443

From "Help" to Helpful: A Hierarchical Assessment of LLMs in Mental e-Health Applications

Philipp Steigerwald·Jens Albrecht

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Abstract

Psychosocial online counselling frequently encounters generic subject lines that impede efficient case prioritisation. This study evaluates eleven large language models generating six-word subject lines for German counselling emails through hierarchical assessment - first categorising outputs, then ranking within categories to enable manageable evaluation. Nine assessors (counselling professionals and AI systems) enable analysis via Krippendorff's $\alpha$, Spearman's $\rho$, Pearson's $r$ and Kendall's $\tau$. Results reveal performance trade-offs between proprietary services and privacy-preserving open-source alternatives, with German fine-tuning consistently improving performance. The study addresses critical ethical considerations for mental health AI deployment including privacy, bias and accountability.

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Philipp Steigerwald, Jens Albrecht. 2026-01-12. From "Help" to Helpful: A Hierarchical Assessment of LLMs in Mental e-Health Applications. https://arxiv.org/abs/2602.18443

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