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What Makes Digital Support Effective? How Therapeutic Skills Affect Clinical Well-Being

Published: 26 April 2024 Publication History

Abstract

Online mental health support communities, in which volunteer counselors provide accessible mental and emotional health support, have grown in recent years. Despite millions of people using these platforms, the clinical effectiveness of these communities on mental health symptoms remains unknown. Although volunteers receive some training on the therapeutic skills proven effective in face-to-face environments, such as active listening and motivational interviewing, it is unclear how the usage of these skills in an online context affects people's mental health. In our work, we collaborate with one of the largest online peer support platforms and use both natural language processing and machine learning techniques to examine how one-on-one support chats on the platform affect clients' depression and anxiety symptoms. We measure how characteristics of support-providers, such as their experience on the platform and use of therapeutic skills (e.g. affirmation, showing empathy), affect support-seekers' mental health changes. Based on a propensity-score matching analysis to approximate a random-assignment experiment, results shows that online peer support chats improve both depression and anxiety symptoms with a statistically significant but relatively small effect size. Additionally, support providers' techniques such as emphasizing the autonomy of the client lead to better mental health outcomes. However, we also found that the use of some behaviors, such as persuading and providing information, are associated with worsening of mental health symptoms. Our work provides key understanding for mental health care in the online setting and designing training systems for online support providers.

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    cover image Proceedings of the ACM on Human-Computer Interaction
    Proceedings of the ACM on Human-Computer Interaction  Volume 8, Issue CSCW1
    CSCW
    April 2024
    6294 pages
    EISSN:2573-0142
    DOI:10.1145/3661497
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    Published: 26 April 2024
    Published in PACMHCI Volume 8, Issue CSCW1

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    Author Tags

    1. machine learning
    2. mental health
    3. online communities
    4. peer support
    5. social computing

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    • (2024)Online Allocation with Replenishable Budgets: Worst Case and BeyondACM SIGMETRICS Performance Evaluation Review10.1145/3673660.365507352:1(57-58)Online publication date: 13-Jun-2024
    • (2024)Online Allocation with Replenishable Budgets: Worst Case and BeyondAbstracts of the 2024 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems10.1145/3652963.3655073(57-58)Online publication date: 10-Jun-2024

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