Ethical analysis of informed consent methods in longitudinal cohort studies: A Chinese perspective.

  • Published In: Developing World Bioethics, 2025, v. 25, n. 2. P. 129 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Li, Kun; Huang, Mingtao; Zhai, Xiaomei; Wang, Chen 3 of 3

Abstract

In longitudinal cohort studies involving large populations over extended periods, informed consent entails numerous urgent challenges. This paper explores challenges regarding informed consent in long‐term, large‐scale longitudinal cohort studies based on the longitudinal and dynamic nature of such research. It analyzes and evaluates widely recognized broad consent and dynamic consent methods, highlighting limitations concerning their ability to adapt to evolving research objectives and participant perspectives. This paper discusses trust‐based informed consent and emphasizes the needs to establish and maintain trust with research participants and to balance information disclosure with respect for participants' autonomy. Informed consent in long‐term studies is an evolving process that must adapt to changing research environments. Based on participant trust, researchers should observe and assess potential research risks. Finally, the paper recommends enhancing institutional credibility, implementing reconsent procedures, and ensuring robust ethical oversight to safeguard participants' rights despite the complexity of modern biomedical research. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Developing World Bioethics. 2025/06, Vol. 25, Issue 2, p129
  • Document Type:Article
  • Subject Area:Health and Medicine
  • Publication Date:2025
  • ISSN:1471-8731
  • DOI:10.1111/dewb.12460
  • Accession Number:185726264
  • Copyright Statement:Copyright of Developing World Bioethics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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