Peer Reviewed

Menopause is often overlooked or medicalized, consequently devaluing individual experiences and failing to support individuals experiencing this life event. Family dynamics, death, and taboo further mean that individuals often miss out on information that could help them contextualize their experiences. We examine participant experiences with menopause and explore designs of digital and non-digital legacies for sharing menopause experiences across generations. We conducted semi-structured interviews and design sessions with 17 participants who experienced or are experiencing menopause. We report participant information needs and sense-making practices, including what personalized information participants wish to pass down and preferred formats for intergenerational sharing. Findings highlight the potential of using storytelling and life-logging to create "holistic" memories of the menopause journey, to support self-reflection, and for using legacies to initiate conversations about marginalized health experiences. We identify future design and research opportunities for the HCI and CSCW communities to support intergenerational sharing of non-medicalized and stigmatized health experiences.

This paper presents a mixed-methods study of app-based motorcycle taxis in Thailand to explore the social dynamics of rideshare drivers and their exercised autonomy both through social pressure and a hostile work environment. As motorcycle taxis are open-air vehicles, drivers can be exposed to prolonged air pollution and other weather events, potentially impacting their health. In an initial quantitative study of server-side rideshare logs, we unexpectedly found that drivers do not exercise the autonomy provided by their rideshare platform to avoid air pollution events. This prompted a follow-on investigation through semi-structured interviews of both drivers and passengers in three provinces to explore why these drivers fail to experience the autonomy promised by gig-work in this context and elucidated further examples this lack of autonomy experienced by drivers. Our study sheds light on the social context that may constrain a driver's agency, including financial pressures, weather conditions, conflicts with local taxi organizations, and a false perception that drivers need to work around the ride assignment algorithm to avoid being blacklisted. We find that when leveraging app-based rideshare opportunities, drivers simultaneously perceive increased flexibility in their work hours and a lack of agency to prioritize their health and safety. We conclude with a discussion on potential interventions aimed at mitigating the forces preventing drivers from exercising their autonomy.

Computational notebooks, tools that facilitate storytelling through exploration, data analysis, and information visualization, have become the widely accepted standard in the data science community. These notebooks have been widely adopted through notebook software such as Jupyter, Datalore and Google Colab, both in academia and industry. While there is extensive research to learn how data scientists use computational notebooks, identify their pain points, and enable collaborative data science practices, very little is known about the various accessibility barriers experienced by blind and visually impaired (BVI) users using these notebooks. BVI users are unable to use computational notebook interfaces due to (1) inaccessibility of the interface, (2) common ways in which data is represented in these interfaces, and (3) inability for popular libraries to provide accessible outputs. We perform a large scale systematic analysis of 100000 Jupyter notebooks to identify various accessibility challenges in published notebooks affecting the creation and consumption of these notebooks. Through our findings, we make recommendations to improve accessibility of the artifacts of a notebook, suggest authoring practices, and propose changes to infrastructure to make notebooks accessible.

With this poster we present an initial analysis of the implications of the COVID-19 pandemic and regional lockdowns on Internet traffic and credit transfers within a rural community network in Bokondini, Indonesia. We find initial evidence that phenomena observed in other large-scale datasets around lockdowns did not occur in this context, with little change observed in network utilization. We do find a marginal increase in the frequency and size of transfers in the network and observe some users accessing health information and using health-related applications.