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Investigating research study participant compensation practices at a California academic and research institution

Published online by Cambridge University Press:  31 March 2025

Mariam Carson*
Affiliation:
School of Medicine, University of California San Francisco, San Francisco, CA, USA
Paula Fleisher
Affiliation:
Clinical and Translational Science Institute, University of California San Francisco, San Francisco, CA, USA
Rana Barar
Affiliation:
Clinical and Translational Science Institute, University of California San Francisco, San Francisco, CA, USA
Li Zhang
Affiliation:
Department of Medicine, University of California San Francisco, San Francisco, CA, USA Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA
Elizabeth Tioupine
Affiliation:
Institutional Review Board, University of California San Francisco, San Francisco, CA, USA
Hilary Seligman
Affiliation:
Clinical and Translational Science Institute, University of California San Francisco, San Francisco, CA, USA Department of Medicine, University of California San Francisco, San Francisco, CA, USA Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA Department of Family and Community Medicine, University of California San Francisco, San Francisco, CA, USA
*
Corresponding author: M. Carson; Email: Mariam.carson@ucsf.edu
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Abstract

Introduction:

While providing compensation for participation in research studies is common, there is an ongoing debate surrounding compensation models and how they can be equitably applied. This work attempts to better understand the landscape of research compensation by evaluating factors associated with compensation of research study participants across instiutional review board (IRB)-approved studies at a single academic institution in California.

Methods:

We extracted all IRB applications for social, behavioral, educational, and public policy research studies between January 1, 2019, and December 31, 2021, at the University of California, San Francisco. Compensation amounts, time estimates for participation, and location of study activities (hybrid, remote, in-person) were extracted from free text entries in the IRB application and reorganized into discrete variables. Multivariable logistic regression was used to assess factors associated with receiving payment after adjusting for time.

Results:

We analyzed 403 unique IRB applications. Studies held at public hospitals and clinics were more likely to provide compensation to study participants, whereas studies held at the university hospitals and clinics were less likely to provide compensation. Unfunded studies also were less likely to provide compensation to research study participants. While participants that were classified as “economically/educationally disadvantaged” and “unable to read, speak, or understand English” within the institution’s IRB application were more likely to receive compensation, those that had “diminished capacity to consent” were less likely to receive compensation.

Conclusions:

While there are multiple frameworks for compensation, there is still significant variability in compensation strategies. Institutions should center equity in considering standardized approaches to compensation for research participation.

Information

Type
Research Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of Association for Clinical and Translational Science
Figure 0

Table 1. Summary of IRB application demographics

Figure 1

Table 2. Payment odds by study site, adjusted for time required for study participation (multivariable)

Figure 2

Table 3. Compensation by funding source and study population, adjusted for time required (multivariable)

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