Development of a robust corpus for automated evaluation of online health information in Chinese using the DISCERN scale
The resulting Chinese OHI corpus, annotated via a standardized DISCERN framework and refined through iterative calibration, provides a robust benchmark for training and evaluating machine learning models. This resource lays the foundation for scalable, reliable automated quality assessment of OHI in Chinese public health settings.
Authors
- Shanli Chen (ORCID: https://orcid.org/0000-0001-7847-874X)
- Jianbo Lei (ORCID: https://orcid.org/0000-0002-1744-0235)
- Junhao Ma (ORCID: https://orcid.org/0009-0006-3308-5284)
- Jun Liang (ORCID: https://orcid.org/0000-0002-0551-6706)
- Xingxi Li (ORCID: https://orcid.org/0000-0001-5530-6355)
- Christopher G. Chute (ORCID: https://orcid.org/0000-0001-5437-2545)
- Edmund Ting
- Qichuan Fang
Institutions
- Zhejiang Chinese Medical University (CN)
- Johns Hopkins University (US)
- Peking University (CN)
- Johns Hopkins Medicine (US)
- Southwest Medical University (CN)
- Affiliated Hospital of Southwest Medical University (CN)
- Second Affiliated Hospital of Zhejiang University (CN)
- Hangzhou Medical College (CN)
- Tsinghua University (CN)
Publication Details
- Journal
- Journal of the American Medical Informatics Association
- Published
- 2025-11-12
- DOI
- https://doi.org/10.1093/jamia/ocaf175
- Primary Topic
- Health Literacy and Information Accessibility
- Type
- article
- Field-Weighted Citation Impact
- 0.00