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Disease frames and their consequences for stigma and medical research funds

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  • Arseniev-Koehler, Alina
  • Best, Rachel Kahn

Abstract

Illnesses are often understood as criminal acts, as medically treatable conditions, or through metaphors of battles and journeys. Theorists suggest that frames vary across diseases and over time in systematic ways, and that frames have concrete consequences for the distribution of resources. But data limitations have prevented scholars from testing these hypotheses. We combine word embeddings and regression analysis to examine four frames for 104 conditions in news media. Our corpus includes over four million news documents published between 1980 and 2018. First, we study the determinants of disease framing by examining which diseases tend to be medicalized, criminalized, and linked to battle and journey metaphors. We find evidence for systematic links between the demographic characteristics of affected individuals and the extent to which diseases are medicalized or criminalized. Next, we examine disease frames’ consequences for stigma and federal medical research funding. While medical and criminal frames are associated with higher levels of stigma, battle and journey frames are associated with less stigma. And while medical, criminal, and battle frames are associated with more research funding, journey frames are associated with less. Together, our results identify the ways in which the social construction of disease reflects and reinforces social inequality.

Suggested Citation

  • Arseniev-Koehler, Alina & Best, Rachel Kahn, 2025. "Disease frames and their consequences for stigma and medical research funds," Social Science & Medicine, Elsevier, vol. 372(C).
  • Handle: RePEc:eee:socmed:v:372:y:2025:i:c:s0277953625002783
    DOI: 10.1016/j.socscimed.2025.117949
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    References listed on IDEAS

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    1. Rachel Kahn Best & Alina Arseniev-Koehler, 2023. "The Stigma of Diseases: Unequal Burden, Uneven Decline," American Sociological Review, , vol. 88(5), pages 938-969, October.
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    5. Alina Arseniev-Koehler & Jacob G. Foster, 2022. "Machine Learning as a Model for Cultural Learning: Teaching an Algorithm What it Means to be Fat," Sociological Methods & Research, , vol. 51(4), pages 1484-1539, November.
    6. Saguy, Abigail C. & Frederick, David & Gruys, Kjerstin, 2014. "Reporting risk, producing prejudice: How news reporting on obesity shapes attitudes about health risk, policy, and prejudice," Social Science & Medicine, Elsevier, vol. 111(C), pages 125-133.
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