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Jiamin Tu

dblp:398/4395 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2025
0009-0003-5673-0205ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Human-computer interaction and ubiquitous computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Health and well-being technologies · 77% Collaborative and social computing · 23%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Health and well-being technologies › long-term care
home care
0.912025
Exploring Data-Driven Advocacy in Home Health Care Work · CHI 2025
Collaborative and social computing › crowdsourcing › crowd work
worker advocacy
0.312025
Exploring Data-Driven Advocacy in Home Health Care Work · CHI 2025

Methods — techniques the papers use, named apart from their topics

qualitative study · 0.9
YearPublicationVenuePosition
2025 Exploring Data-Driven Advocacy in Home Health Care Work
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.
Joy Ming, Hawi H. Tolera, Jiamin Tu, Ella Yitzhaki, Chit Sum Eunice Ngai, Madeline R. Sterling, Ariel C. Avgar, Aditya Vashistha, Nicola Dell
CHI3