Aligning Data with the Goals of an Organization and Its Workers: Designing Data Labeling for Social Service Case Notes
Apoorva Gondimalla, Varshinee Sreekanth, Govind Joshi, Whitney Nelson, Eunsol Choi, Stephen C. Slota, Sherri R. Greenberg, Kenneth R. Fleischmann, Min Kyung Lee
Abstract
The challenges of data collection in nonprofits for performance and funding reports are well-established in HCI research. Few studies, however, delve into improving the data collection process. Our study proposes ideas to improve data collection by exploring challenges that social workers experience when labeling their case notes. Through collaboration with an organization that provides intensive case management to those experiencing homelessness in the U.S., we conducted interviews with caseworkers and held design sessions where caseworkers, managers, and program analysts examined storyboarded ideas to improve data labeling. Our findings suggest several design ideas on how data labeling practices can be improved: Aligning labeling with caseworker goals, enabling shared control on data label design for a comprehensive portrayal of caseworker contributions, improving the synthesis of qualitative and quantitative data, and making labeling user-friendly. We contribute design implications for data labeling to better support multiple stakeholder goals in social service contexts.
• Human-centered computing → Qualitative research.
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