"If This Person is Suicidal, What Do I Do?": Designing Computational Approaches to Help Online Volunteers Respond to Suicidality

要旨

Online platforms provide support for many kinds of distress, including suicidal thoughts and behaviors. However, because many platforms restrict suicidal talk, volunteers on these platforms struggle with how to help suicidal people who come for support. We interviewed 11 volunteer counselors in a large online support platform, including after they role-played conversations with varying severities of suicidality, to explore practices and challenges when identifying and responding to suicidality. We then presented Speed Dating design concepts around emotional preparation and support, real-time guidance, training, and suicide detection. Participants wanted more support and preparation for conversations with suicidal people, but were conflicted about AI-based technologies, including trade-offs between potential benefits of conversational agents for training and limitations of prediction or real-time response suggestions, due to the sensitive, context-dependent decisions that volunteers must make. Our work has important implications for nuanced considerations and design choices around developing digital mental health technologies.

著者
Logan Stapleton
Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
Sunniva Liu
Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
Cindy Liu
Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
Irene Hong
Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
Stevie Chancellor
University of Minnesota, Minneapolis, Minnesota, United States
Robert E. Kraut
Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
Haiyi Zhu
Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
論文URL

doi.org/10.1145/3613904.3641922

動画

会議: CHI 2024

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2024.acm.org/)

セッション: Remote Presentations: Highlight on Health

Remote Sessions
13 件の発表
2024-05-15 18:00:00
2024-05-16 02:20:00