aceA Large Language Model-Based System for Depression Symptom Identification: Iterative Qualitative Co-Design Studies
Background: AI technologies have considerable potential to support mental health practitioners in identifying depression symptoms. However, existing tools are often developed with limited practitioner involvement and evaluated primarily on technical performance, leaving questions of clinical relevance, ethical design, and practitioner trust insufficiently addressed. Objective: This study aimed to examine how iterative co-design with mental health practitioners can inform the development of an AI-based model for…
Eight mental health practitioners said the expanded 10-category version of the tool was clearer and more useful for preassessment than the first version.
The study gathered qualitative practitioner feedback from small groups and did not independently validate the model. The authors say larger-scale validation, formal safety evaluation and culturally diverse datasets are still needed.
Evidence
- Peer-reviewedJMIR Formative Research2026-11-03
How should this claim be treated?
Truvace Impact Record TRV-2026-2361, v2: “A Large Language Model-Based System for Depression Symptom Identification: Iterative Qualitative Co-Design Studies.” Truvace, 2026-11-04. /record/TRV-2026-2361 (accessed at citation time). sha256 0f305c66c85590b2…
Calibration history
Every change to this record since certification, in the open.
Editorial pass: sharper headline; gain/problem restated as concrete outcomes.
Certified into the record
How to verify without trusting this page
Fetch the canonical text of any version from /api/record/TRV-2026-2361 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.