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Extending participant feedback beyond clinical studies: A modular system designed to connect researchers and
Alicia Giordimaina Carmichael1, Donna Walter1, Brandon Patric Labbree1
1Institute for Social Research, University of Michigan, Ann Arbor, MI, USA.
Introduction:
Declining participant engagement threatens human subjects research. Participant feedback systems (PFS) may combat this decline by empowering participants to evaluate their research experiences and share that feedback with researchers to identify targets for improvement. PFS signal that participant experiences are prioritized, making the request for feedback itself an intervention. PFS design work remains largely confined to clinical research. This exploratory study investigates the design parameters of extending PFS to nonclinical research. We conducted focus groups with nonclinical stakeholders: Experienced research participants (ERP) and research team members (RTM).
Methods:
ERP focus groups were organized by affinity (LGBTQIA+, BIPOC, persons with disabilities, neurodivergent, and a general group). RTM focus groups were organized by unit within the University of Michigan. Transcripts were analyzed using inductive thematic analysis.
Results:
Ten focus groups (ERP: 5, n = 25; RTM: 5, n = 26) identified key PFS design considerations: (1) motivations for feedback, (2) feedback collection, and (3) feedback delivery. ERP and RTM collectively preferred anonymous web-based surveys with six potential topic areas: communication, respect, being valued, receiving value, burden, and safety. Feedback delivery faced two key design tensions: balancing institutional standardization with study-specific insights and aligning leadership's preference for high-level summaries with frontline staff's need for detailed, real-time feedback.
Conclusion:
Expanding PFS to nonclinical research requires balancing centralization and study-specific flexibility. While centralization enhances consistency, the diversity of nonclinical studies necessitates adaptable implementation. A hybrid model is proposed to optimize feasibility. Future research should refine and test this model.
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