Self-Stigma Is Not a Monolith, but Generic Empathy Is: Persona-Conditioned LLM Support for People Who Use Drugs
arXiv:2606.23387v2 Announce Type: replace Abstract: Self-stigma predicts treatment avoidance and disengagement among people who use drugs (PWUD), yet conversational systems aiming to provide support typically treat self-stigma expression as a uniform signal. We present a three-phase, proof-of-concept study of a persona-aware approach to LLM support. Latent Profile Analysis (LPA) on indicator-level features from 1,174 self-stigma expressors on Reddit yields a four-persona typology validated against held-out behavioral and linguistic features. Sequential Bayesian and recurrent neural classifiers
The proliferation of Large Language Models (LLMs) and the increasing focus on their application in sensitive areas like mental and health support necessitates research into nuanced, personalized interactions for better outcomes.
This study demonstrates how LLMs can move beyond generic responses to provide more effective, persona-aware support, potentially improving engagement and reducing negative outcomes like treatment avoidance in vulnerable populations.
Conversational AI support systems can now be designed with a more sophisticated understanding of user psycho-social states, enabling tailored interventions rather than uniform approaches.
- · AI developers focused on healthcare
- · Mental health support services
- · People who use drugs (PWUD)
- · Generic chatbot providers
- · One-size-fits-all digital health solutions
Improved efficacy of AI-driven support platforms for diverse and stigmatized user groups.
Increased trust and adoption of AI assistants in sensitive health and social service sectors.
Ethical frameworks for AI in mental health will need to address persona-specific tailoring and potential for manipulation or over-personalization.
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Read at arXiv cs.CL