Toward labor-sensitive research: Health data labor and targeted inclusion in precision medicine research

Daan Kenis and Ina Devos

Social Science & Medicine, 2025, vol. 383, issue C

Abstract: Data-intensive healthcare has an equity problem. As data-intensive healthcare expands, historical injustices embedded in existing datasets limit its benefits for marginalized groups. While national and international Precision Medicine Research (PMR) initiatives – like All of Us (US) and the 100k Genome Project (UK) – aim to address these gaps through targeted inclusion, such strategies carry exploitative potential. Representative data serves not only underserved communities but also public and commercial interests, raising additional ethical concerns about how data gaps are filled. This paper critically examines targeted inclusion efforts and discussions thereof in PMR ethics, which tend to overlook the risks and social conditions participants face during research participation. Drawing on the concept of ‘clinical labor’, we introduce the idea of health data labor – the often invisible, unequal labor involved in health data generation. This framing helps expose how the burdens and risks of PMR participation may follow existing social inequities. We argue that without addressing these inequities in the participation process, inclusionary efforts risk reinforcing injustice. We conclude by proposing ‘labor-sensitive research design’ – a set of practices to make PMR more equitable in both process and outcome.

Keywords: Data-intensive healthcare; Precision medicine research; Clinical labor; Data work; Structural injustice (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0277953625007750
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:socmed:v:383:y:2025:i:c:s0277953625007750

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
http://www.elsevier. ... _01_ooc_1&version=01

DOI: 10.1016/j.socscimed.2025.118444

Access Statistics for this article

Social Science & Medicine is currently edited by Ichiro (I.) Kawachi and S.V. (S.V.) Subramanian

More articles in Social Science & Medicine from Elsevier
Bibliographic data for series maintained by Catherine Liu ().