Exploring Alternative Socio-Technical Systems for Careful Data Work in Recovery Contexts

Research Background and Issues

• What problems or challenges did the authors identify?
  Nonprofit organizations (such as volunteer and community organizations) face significant pressures in data work, needing to comply with government-mandated standards and compete for funding. However, these organizations encounter notable challenges in practices, information systems, and tools for capturing data related to service provision. These requirements may also conflict with the organizations' missions and values.

• Why is this issue important?
  Volunteer organizations play a critical role in promoting mental health recovery, but the pressures of data work can divert these organizations from their socially driven missions, especially under resource constraints. Furthermore, existing data-driven systems may be overly technical, hindering service providers from gaining a holistic and comprehensive understanding of their users, thereby affecting service quality.

• Research Motivation and Related Work
  Inspired by data feminism and the ethics of care, the authors explore how to design data work that embodies care and resists the dominant practices of "data extractivism." This study is based on long-term collaboration with a UK-based mental health volunteer support organization, Recovery College Collective (ReCoCo), aiming to address the aforementioned issues and propose alternative approaches that better meet community needs.

Solution

• What methods or solutions did the authors propose?
  Through collaboration with the ReCoCo community, the authors designed a data collection tool called "Empower Flower" to capture richer dimensions of individual recovery journeys while promoting more thoughtful data practices. Empower Flower is a self-report scale designed to measure various aspects of recovery, such as social connections and self-worth.

• What is innovative about this solution?

  1. Community-Driven: Empower Flower is designed based on the principles of community recovery, emphasizing qualitative data and user experience rather than simple quantification.
  2. Challenging Data-Driven Systems: It challenges the technical and formalized nature of mainstream data work, shifting the focus from technical data to individuals and their unique experiences.
  3. Relational and Trust-Building: The tool is not only used for data collection but also serves as a means to foster relationships and dialogue between students and staff.

• What are the implementation steps and key technologies used?

  1. Introduce Empower Flower during the student registration process, with staff assistance.
  2. Regularly update the scale to reflect students' progress in their recovery journey.
  3. Combine paper-based and online systems for data storage and ensure transparent management. However, current data processing still involves manual input and efficiency challenges.

Research Outcomes

• What specific outcomes were achieved?

  1. Documented ReCoCo's data practices, including data collection and management processes.
  2. Proposed Empower Flower as an alternative data work tool, which performed well in community and recovery contexts, promoting more thoughtful and human-centered data practices.

• What advantages does it have compared to existing solutions?
  Empower Flower captures multidimensional human experiences, such as social connections and a sense of belonging, more effectively than government-mandated clinical tools. This tool aligns better with the dynamic focus on individual needs and holistic understanding in the recovery process.

• What were the experimental or evaluation results?
  Interviews (including staff, student volunteers, and government officials) validated that Empower Flower fosters trust relationships, enriches data quality, and enhances students' reflections on their personal recovery journeys. The tool was highly accepted within the community and was described as "more than just a data tool."

• Limitations and Future Directions

  1. Resource Constraints: Technical functionalities such as data visualization are underdeveloped, and data processing remains labor-intensive and time-consuming.
  2. External Pressures: Government standards may demand more detailed reporting, potentially diverging from ReCoCo's values.
  3. Future Directions: Design sociotechnical systems that enable communities to better understand and utilize data while creating more data visualization pathways to showcase recovery progress.

Through this research, the authors not only documented the data practices of a mental health recovery community but also provided design guidelines and new research directions for the HCI field. They advocate for using technology to support communities' own data cultures and values while resisting the dominant logic of the data-driven economy.