Therapist-Facing Evidence-Linked Session Copilot with Progressive-Context Retrieval, Support-Strategy Control, Safety-Oriented Escalation, and Explanation Cards
Keywords:
Conversational Retrieval, Mental-Health Decision Support, Evidence Attribution, Emotional Support Strategy, Selective Prediction, Human Oversight, Explanation CardsAbstract
Conversational language models can organize large collections of prior counseling exchanges, yet unsupported advice, opaque ranking, and unsafe automation limit their use in mental-health settings. This study evaluated a therapist-facing copilot that retrieves source-linked behavioral-health-coach responses, exposes operational support strategies, and routes safety cues to review. The primary corpus comprised 6,310 question–response pairs derived from anonymized caregiver interviews in MentalChat16K. A separate synthetic component contributed 9,747 usable pairs only in an auxiliary feature-fitting condition. A component-grouped 60/20/20 split compared BM25, word and character TF–IDF, 128-dimensional latent semantic analysis, and four-way reciprocal-rank fusion. Progressive-context tests revealed the original description cumulatively, while strategy-controlled maximal-marginal-relevance extraction assembled cards from the real training library. Reciprocal-rank fusion achieved MRR@10 of 0.4491 and Recall@5 of 0.6220; character TF–IDF performed best at 0.6007 and 0.7071. In grouped out-of-fold evaluation, the character model reproduced narrow and broad phrase proxies with recall 0.5000 and 0.4324. Source-linked cards attained 1.000 exact attribution coverage and three-excerpt completeness. These findings establish an offline retrieval, provenance, confidence-routing, and policy-proxy profile, not therapeutic efficacy. Interpretation and action remained under professional control.
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