Frontline health workers are the first and often the only access point to basic health care services in low-and-middle income countries. However, the work and the issues frontline health workers face in community health are often invisible, with limited resources to assist them. This study explores the work practices, challenges and roles of frontline health workers in community health with particular focus on pregnancy care in South India. Drawing on the notion of maintenance and articulation work, we describe the maintenance work of frontline health workers maintaining, anticipating, navigating, reconciling, and supporting care infrastructures beyond data collection practices. Our findings also highlight how socio-cultural practices, perceptions, status, and existing systems influence maintenance work practices. Based on our findings, we suggest moving beyond the focus on training and performance to design CSCW tools to support the maintenance work that frontline health workers do to make healthcare infrastructures work in community health.
Health disclosure at work is complicated for people with invisible chronic conditions. Due to the lack of visible symptoms, invisible conditions affect the work life of people in ways that are not obvious to others. This study examines how people disclose and conceal their conditions in the workplace and opens the designs space for this topic. In the first phase, we analyzed posts on two subreddit forums, r/migraine and r/fibromyalgia, and found a range of strategies that individuals use to disclose or conceal their conditions. In the second phase, we created five technological design concepts based on these strategies that were shown to eight people with migraines or fibromyalgia in semi-structured interviews. Based on these phases, we contribute understandings of disclosure of invisible conditions in the workplace for future research, such as potential areas for intervention ranging from individual to societal level efforts, as well as the potential and limitations of relying on empathy from others.
https://doi.org/10.1145/3449147
Shift scheduling strongly impacts the job satisfaction and well-being of healthcare workers, because it heavily influences social life and recreational activities. Due to its complexity and time-consuming nature, shift scheduling becomes increasingly automated. However, existing systems are mostly directed at improving efficiency. The workers' needs and their participation in negotiating shifts do not play a pronounced role. In contrast, we present a nine month case study of an interactive, worker-centered self-scheduling system which allows healthcare workers to participate in shift planning and to negotiate scheduling conflicts autonomously. Our results show that cautiousness about social standing and differences in personal lifestyle inhibited system usage for some workers. Moreover, the workers engaged in several pro-social conflict prevention strategies instead of competitive decision-making. We conclude the paper with detailed design guidelines for computer-supported self-scheduling.
https://doi.org/10.1145/3449219
As online platforms become ubiquitous, there is growing concern that their use can potentially lead to negative outcomes in users’ lives. A central question in the literature studying this phenomenon is whether quantity of use is related to problematic offline effects like reduced sleep. This is often addressed by either analyzing self-reported measures of time spent online, which are generally inaccurate, or using objective metrics derived from server logs or tracking software. However, how these two types of time measures comparatively relate to problematic effects — whether they complement or are redundant with each other in predicting problematicity — remains unknown. Furthermore, transparent research in the literature is hindered by its focus on closed platforms with inaccessible data, as well as selective analytical decisions that may lead to reproducibility issues. In this work, we investigate the relationships between both self-reported and data-derived metrics of time spent and potentially problematic effects arising from use of an open, non-profit online chess platform. These effects include disruptions to sleep, relationships, school and work performance, and self-control. To this end, we distributed a gamified survey to players and linked their responses with publicly-available game logs. We find problematic effects to be associated with both self-reported and data-derived usage measures to similar degrees. However, analytical models incorporating both self-reported and actual time explain problematic effects significantly more effectively than models with either type of measure alone. Furthermore, these results persist across thousands of possible analytical decisions when using a robust and transparent statistical framework. This suggests that the two methods of measuring time spent measure contain distinct, complementary information about problematic usage outcomes and should be used in conjunction with each other.
https://doi.org/10.1145/3449160
Effective ways to measure employee job satisfaction are fraught with problems of scale, misrepresentation, and timeliness. Current methodologies are limited in capturing subjective differences in expectations, needs, and values at work, and they do not lay emphasis on demographic differences, which may vary people’s perceptions of job satisfaction. This study proposes an approach to assess job satisfaction by leveraging large-scale social media data. Starting with an initial Twitter dataset of 1.5M posts, we examine two facets of job satisfaction, pay and supervision. By adopting a theory-driven approach, we first build machine learning classifiers to assess perceived job satisfaction with an average AUC of 0.84. We then study demographic differences in perceived job satisfaction by geography, sex, and race in the U.S. For geography, we find that job satisfaction on Twitter exhibits insightful relationships with macroeconomic indicators such as financial wellbeing and unemployment rates. For sex and race, we find that females express greater pay satisfaction but lower supervision satisfaction than males, whereas Whites express the least pay and supervision satisfaction. Unpacking linguistic differences, we find contrasts in different groups’ underlying priorities and concerns, e.g., under-represented groups saliently express about basic livelihood, whereas the majority groups saliently express about self-actualization. We discuss the role of frame of reference and the “job satisfaction paradox”, conceptualized by organizational psychologists, in explaining our observed differences. We conclude with theoretical and sociotechnical implications of our work for understanding and improving worker wellbeing.
https://doi.org/10.1145/3449241
In two studies, we investigate how users choose virtual backgrounds and how these backgrounds influence viewers’ impressions. First, we developed a web prototype allowing users to apply different virtual backgrounds to their camera views. We then asked users to select backgrounds that they believed would change viewers’ perceptions of their personality traits. We applied a subset of the virtual backgrounds participants selected to a subset of videos from the First Impression Dataset. We then ran an online study on Amazon MTurk to compare participants’ personality trait ratings for subjects in three conditions: with the selected virtual backgrounds, with a gray screen instead of a background, and with the original video backgrounds. The selected virtual backgrounds did not change the personality trait ratings in the intended direction. Instead, virtual background use of any kind results in a consistent “muting effect” that mitigates very high or low ratings compared to the original background.
https://doi.org/10.1145/3476044
Traditional meetings involve extensive sitting, which negatively impacts the health of attendees. Understanding how technology can facilitate integrating physical activity into the workplace, such as in walking meetings, is vital to improving workplace wellbeing. To that end, we applied a mixed-method approach to explore requirements and opportunities for walking meetings. We conducted an online questionnaire and a series of interviews with early adopters of walking meetings and created design fictions based on their feedback. We evaluated the design fictions with a second questionnaire and garnered additional feedback from the original early adopters. Based on our findings, we derived four dimensions associated with walking meetings: practical, environmental, social, and cognitive facets. We define attributes, challenges, and opportunities within these dimensions which are important for designing systems that support walking meetings. Our work identifies key considerations for developing systems that integrate physical activity into communication activities.
https://doi.org/10.1145/3476088
Digital intervention tools against problematic smartphone usage help users control their consumption on smartphones, for example, by setting a time limit on an app. However, today's social media apps offer a mix of quasiessential and addictive features in an app (e.g., Instagram has following feeds, recommended feeds, stories, and direct messaging features), which makes it hard to apply a uniform logic for all uses of an app without a nuanced understanding of feature-level usage behaviors. We study when and why people regret using different features of social media apps on smartphones. We examine regretful feature uses in four smartphone social media apps (Facebook, Instagram, YouTube, and KakaoTalk) by utilizing feature usage logs, ESM surveys on regretful use collected for a week, and retrospective interviews from 29 Android users. In determining whether a feature use is regretful, users considered different types of rewards they obtained from using a certain feature (i.e., social, informational, personal interests, and entertainment) as well as alternative rewards they could have gained had they not used the smartphone (e.g., productivity). Depending on the types of rewards and the way rewards are presented to users, probabilities to regret vary across features of the same app. We highlight three patterns of features with different characteristics that lead to regretful use. First, "following"-based features (e.g., Facebook's News Feed and Instagram's Following Posts and Stories) induce habitual checking and quickly deplete rewards from app use. Second, recommendation-based features situated close to actively used features (e.g., Instagram's Suggested Posts adjacent to Search) cause habitual feature tour and sidetracking from the original intention of app use. Third, recommendation-based features with bite-sized contents (e.g., Facebook's Watch Videos) induce using "just a bit more," making people fall into prolonged use. We discuss implications of our findings for how social media apps and intervention tools can be designed to reduce regretful use and how feature-level usage information can strengthen self-reflection and behavior changes.
https://doi.org/10.1145/3479600