Few studies in HCI4D have examined the lived experiences of women with pregnancy complications. We conducted a qualitative study with 15 pregnant women to gain an in-depth understanding of the context in which pregnancy takes place and everyday experiences living with complications in rural North-West India. To complement our interviews, we conducted six focus groups with three pregnant women, three community health workers and three members of an NGO. Our study reveals insights about the challenges and experiences of the pregnant women with complications while navigating the physical, spatial, social and emotional aspects of antenatal care as part of complex and contradictory structures and settings of their everyday life. We argue that the design of digital health in support of pregnancy care for the Global South must center around supporting the navigational work done by the pregnant women and their families.We summarize research in two areas including an overview of public health strategies and challenges to improve maternal health in India, and digital health in the Global South, with focus on the Indian context.
The healthcare sector is undergoing large changes in which technology is given a more active role in both in-clinic and out-of-clinic care. Authoritative healthcare models such as compliance and adherence which relies on asymmetric patient-doctor relationships are being challenged as society, patient roles and care contexts transforms, for example when care activities move into non-clinical contexts.Concordance is an alternative model proposed by the medical field that favours an equal and collaborative patient-doctor relationship in the negotiation of care. Similarly, HCI researchers have applied diverse models of engagement in IT design ranging from authoritative models (e.g. perceiving people as human factors to design for) to more democratic design processes (e.g. Participatory Design). IT design has also been crafted as on-going processes that are integrated parts of everyday use. Based on the best practice of participation from the medical and the HCI fields, we identify critical alternatives for healthcare design. These alternatives highlight opportunities with ongoing design processes in which the design of care regimens and care IT are perceived as one process.
This paper examines the causes and consequences of legibility as an organizing principle in the design of digital agriculture (DA) systems in the United States. Legibility refers to systems of governance that use simplified understandings of a situation to control and direct action upon it. Legibility in digital agriculture systems occurs at the confluence of two traditions of legibility: the data-driven model common in the design of digital systems, and tactics for the control of nature and labor that have developed in the United States since the foundation of the colonies. Our argument draws from (1) a historical analysis of broader patterns of agricultural technology and racialized land dispossession in what is now the United States and (2) empirical fieldwork that examines the adoption and maintenance of digital agriculture systems in rural New York State. We describe the role that legibility historically has played in the development of agricultural systems in the US, and their consequences for who is able to farm and how. This history raises the questions: What is made legible to whom? In that process, what becomes illegible? While legibility promises transparent and environmentally beneficial control, in our fieldwork we find that the demands of legibility are also restructuring the physical landscape, creating additional invisible labor, producing systems that are brittle to real-world conditions on farms, and creating opaque systems that block people from adapting to their circumstances. In reading our fieldwork together with the historical case, we demonstrate the pressures that are shaping the stakes, subject, and objects of legibility in agricultural technology. As more data-driven systems are used for environmental contexts, the CSCW community needs to extend its ways to understand how data-driven systems impact land, labor, and resources.
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