Abstract
Parents Anonymous®, a social service agency, provides a comprehensive ecosystem of supports to parents through its National Parent & Youth Helpline and mutual support groups. The verbal support provided through the Helpline is the core of this public service. We present a computational user research approach that learns from call transcripts to guide continuous improvement across the helpline–group system. From real conversations, we extract clear, actionable signals: how emotions shift during a call, which needs recur over time and when people are most likely to reach out. These insights feed back into everyday practice: refining counsellors’ training and responses, timing follow-ups, and planning schedules and outreach. The approach adds no burden to callers, fits existing staff workflows, and complements practitioner expertise. We offer a practical roadmap for public teams to pair service design with data-driven feedback so helplines.
Keywords
child welfare, computational user research, parental hotlines, service design
DOI
https://doi.org/10.21606/drs.2026.2370
Citation
Ilhan, A.O., Harris, E.C., and Oygur, I. (2026) Computational User Research for a Public Helpline for Continuous Service Improvement: Parents Anonymous® as a Service Ecosystem, in Simeone, L., Gray, C. M., Verhoeven, A., de Götzen, A., Bakırlıoğlu, Y., Zohar, H., Stead, M., and Buwert, P. (eds.), DRS2026: Edinburgh, 8–12 June, Edinburgh, United Kingdom. https://doi.org/10.21606/drs.2026.2370
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Included in
Computational User Research for a Public Helpline for Continuous Service Improvement: Parents Anonymous® as a Service Ecosystem
Parents Anonymous®, a social service agency, provides a comprehensive ecosystem of supports to parents through its National Parent & Youth Helpline and mutual support groups. The verbal support provided through the Helpline is the core of this public service. We present a computational user research approach that learns from call transcripts to guide continuous improvement across the helpline–group system. From real conversations, we extract clear, actionable signals: how emotions shift during a call, which needs recur over time and when people are most likely to reach out. These insights feed back into everyday practice: refining counsellors’ training and responses, timing follow-ups, and planning schedules and outreach. The approach adds no burden to callers, fits existing staff workflows, and complements practitioner expertise. We offer a practical roadmap for public teams to pair service design with data-driven feedback so helplines.