Exploring Reinforcement Learning for Fluid Transitions Between Clinical Mental Healthcare and Everyday Wellness Support

CLAIM (Claim-Specific Citation Network audit, sometimes called CSN) is a forensic method for testing whether a scientific or medical claim's authority is supported by evidence or by citation dynamics. It detects citation bias, amplification, citation diversion, citation transmutation, dead-end citation, and back-door invention.

The ASSAY skill runs a structured, CLAIM-compatible extraction and integrity assessment on an article. Output is a verdict (sound, mixed, flagged, problematic, or cascade), a count of claims extracted, the central key claim, and an integrity note describing the structural read.

This scan restricts ASSAY to peer-reviewed publications and preprint servers. Journalism, opinion pieces, and government documents are evaluated under different frameworks (CAIHL for power and agency; editor's note for context).

ASSAY found the central claims partially supported. Some scaffolding holds; other parts of the argument lean on weaker or contested evidence. Read with the integrity note in mind.

A reinforcement-learning agent can manage transitions between clinical mental healthcare and everyday wellness support more fluidly than current rule-based handoff workflows, with the agent's objective formulated over the trajectory rather than the visit.

Total claims extracted from the article: 6. The key claim is the single most load-bearing assertion the rest of the argument depends on.

The clinical-to-wellness transition is the right problem to identify but the wrong layer for unsupervised RL — the reward shaping the preprint relies on will encode the deployer's objective, not the patient's continuity-of-care objective, and the two will diverge on at least the most clinically important cases (relapse, crisis, decompensation). The preprint advances the architectural conversation but does not address the consent envelope that the handoff dissolves. Preprint, not peer-reviewed.

Editor's note: The interesting layer the preprint sits on is the handoff. The clinic does not own everyday wellness; the wellness app does not own clinical care. The RL agent is being proposed as the bridge — and the bridge is exactly where the consent envelope dissolves.

Summary: arXiv: Reinforcement-learning approach to managing transitions between clinical mental healthcare episodes and everyday wellness/maintenance support — the handoff layer between the clinic and the consumer app.

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