Exploring Data-Driven Advocacy in Home Health Care Work
🤖 AI Summary
This study addresses data empowerment challenges faced by home care workers: how to collect data with minimal burden while safeguarding privacy, bridging the tension between workers’ and advocates’ divergent data expectations and practices, and reconciling individual rights with collective advocacy goals. Employing qualitative methods—including in-depth interviews with 11 care workers and 15 advocates, plus participatory workshops—we integrate data ethics frameworks with co-design principles. We identify, for the first time, the “data expectation–practice gap” as the central contradiction; propose a novel “advocate-as-data-steward” paradigm and a multi-source data integration mechanism to support heterogeneous advocacy objectives; and surface three key implementation barriers, distilling four actionable pathways. The findings yield a theory-informed, data-driven advocacy model and practice guidelines specifically designed for low-wage frontline health workers.
Application Category
📝 Abstract
This paper explores opportunities and challenges for data-driven advocacy to support home care workers, an often overlooked group of low-wage, frontline health workers. First, we investigate what data to collect and how to collect it in ways that preserve privacy and avoid burdening workers. Second, we examine how workers and advocates could use collected data to strengthen individual and collective advocacy efforts. Our qualitative study with 11 workers and 15 advocates highlights tensions between workers' desires for individual and immediate benefits and advocates' preferences to prioritize more collective and long-term benefits. We also uncover discrepancies between participants' expectations for how data might transform advocacy and their on-the-ground experiences collecting and using real data. Finally, we discuss future directions for data-driven worker advocacy, including combining different kinds of data to ameliorate challenges, leveraging advocates as data stewards, and accounting for workers' and organizations' heterogeneous goals.
Problem
Research questions and friction points this paper is trying to address.
Data Utilization
Privacy Balance
Expectation-Reality Gap
Innovation
Methods, ideas, or system contributions that make the work stand out.
Data Utilization
Privacy Protection
Worker Well-being
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