Canonical text (the exact bytes fingerprinted)

TRUVACE RECORD VERSION
record: TRV-2026-1161
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-09-22T06:53:21.921093Z
status: published
lens: g_space
sector: health
headline: Development of an artificial intelligence model to estimate psychiatrist-assessed mental health-related presenteeism
dek: 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…
gain_title: 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.
problem_title: (none)
trace_subject: (none)
gain_reading: 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.
gain_evidence: (none)
problem_reading: (none)
problem_evidence: (none)
quick_read: 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.

The primary outcome measure was the accuracy of the AI model in estimating workers' mental health-related presenteeism. 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.
limitation: 
tag: Evidence-backed gain
key_points: 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 Japanese company.
rundown: 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 Japanese company. The participants comprised 117 white-collar workers who provided a total of 1,631 video responses to a standardized health-status question over a period of 10 working days.
sources:
- peer_reviewed | Journal of Occupational Health | https://doi.org/10.1093/joccuh/uiag055 | 2026-09-21
prev: 0000000000000000000000000000000000000000000000000000000000000000

sha256

12166ffe77688c0d0b84581aa177573772bfd2d86e8842092086ebb56dd9e6a1

previous

0000000000000000000000000000000000000000000000000000000000000000