aceDevelopment of an artificial intelligence model to estimate psychiatrist-assessed mental health-related presenteeism
Objectives This study aimed to contribute to the development of an AI-based system that supports worker health and productivity by enabling early detection of presenteeism. We tested whether an AI model could assess mental health-related presenteeism with accuracy comparable to that of psychiatrists and whether the frequency of application use was comparable between avatar-based and real-person interfaces. Methods This study aimed to design a multivariable prediction model among white-collar employees in a Japan…
Results The AI model achieved an overall accuracy of 72.2%, a macro F1 score of 0.64, and a weighted F1 score of 0.73 compared with psychiatrists' ratings.
Evidence
- Peer-reviewedJournal of Occupational Health2026-09-21
How should this claim be treated?
Truvace Impact Record TRV-2026-1161, v1: “Development of an artificial intelligence model to estimate psychiatrist-assessed mental health-related presenteeism.” Truvace, 2026-09-22. /record/TRV-2026-1161 (accessed at citation time). sha256 12166ffe77688c0d…
Calibration history
Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.
Certified into the record
How to verify without trusting this page
Fetch the canonical text of any version from /api/record/TRV-2026-1161 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.