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Exploring Trade-Offs for Online Mental Health Matching: Agent-Based Modeling Study.
Yuhan Liu1, Anna Fang2, Glen Moriarty3
1Department of Computer Science, Princeton University, Princeton, NJ, United States.
JMIR Formative Research
|October 1, 2024
Summary
Agent-based modeling optimizes online mental health community (OMHC) matching algorithms. Topic-based matching improves support seeker-counselor interactions, especially for marginalized groups, balancing satisfaction and success rates.
Area of Science:
- Computational Social Science
- Human-Computer Interaction
- Health Informatics
Background:
- Online mental health communities (OMHCs) offer accessible support but struggle with effective user matching.
- Current matching mechanisms in OMHCs are often naive, hindering optimal user interactions.
Purpose of the Study:
- To apply agent-based modeling (ABM) for designing and evaluating online community matching algorithms.
- To uncover trade-offs between different matching algorithms for support seekers and volunteer counselors in OMHCs.
Main Methods:
- Developed an agent-based simulation framework using comprehensive OMHC data (Jan 2020-Apr 2022).
- Validated the simulation against existing matching mechanisms.
- Utilized the validated simulation as a sandbox to test various matching algorithms, including deferred acceptance, first-come-first-served, and topic-based matching.
Main Results:
- Algorithmic choices create trade-offs; intelligent matching can increase waiting times for support seekers.
- Topic-based matching significantly improved chat ratings and reduced blocking incidents for all user groups, particularly benefiting underaged and gender minority populations.
- Filter-based matching for specific demographics improved outcomes for those groups but decreased overall user satisfaction.
Conclusions:
- Agent-based modeling is a valuable tool for understanding design considerations and trade-offs in OMHCs.
- Algorithmic matching, particularly topic-based approaches, shows potential for improving support quality and equity for marginalized users in online mental health settings.
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