There are increasing interests in developing AI tools to identify and address individual-level social determinants of health in both health care and human service settings. These activities are part of social care integration, which at the individual level involves identifying individuals with social risks (awareness) and connecting them with relevant social care resources (assistance). Social care providers such as community health workers and social workers are deemed critical stakeholders in both settings. Chatbots have shown feasibility and acceptability for social risk screening in emergency departments and primary care centers. However, we do not know if a screening chatbot is worth developing for the safety net health care and human service settings, where there are visitors with greater social needs and less organizational resources. The study aims to investigate the perceived value proposition of an AI-based chatbot for social risk screening from the perspectives of social care providers in safety net health care and human service organizations. Providers’ perceived value propositions of other AI-based applications for social care integration were also examined. We conducted semi-structured interviews with 19 social care providers who have experience with awareness and/or assistance from 16 safety net health care and human service organizations in Michigan. Interview questions focused on their experiences and challenges regarding awareness and assistance when applicable. A simulated screening chatbot based on ChatGPT-4o was also used to solicit their feedback on the technology. The nonadoption, abandonment, scale-up, spread, and sustainability (NASSS) framework was used to guide data analysis. Interview transcripts were first coded deductively, then inductively. Social care providers perceived the screening chatbot as offering limited value. This is mainly because many participants engaged in assistance activities and they noted addressing social needs is a multi-step process requiring follow-up that screening chatbots do not provide. In addition, they valued cultivating trust as many patients/clients have high social needs and lack trust in the health care system, and felt chatbots present new challenges for maintaining essential trust and care quality with clients/patients. Instead of a screening chatbot, we identified that technologies to reduce documentation burden could improve providers’ efficiency and potentially increase time spent with patients/clients. This is because social risks and needs documentation generates administrative burden for providers. We also found that technologies to improve referral accuracy and engagement could improve providers’ effectiveness, as study participants have overall limited access to technologies that typically support referral-related activities. Social care providers in the safety net preferred AI-based applications for addressing documentation burden and social needs assistance rather than for social risk screening. Future strategies to develop AI tools for social care integration should align with social care providers’ professional values and focus on equity-centered care.

Communication with child patients is challenging due to their developing ability to express emotions and symptoms. Additionally, healthcare providers often have limited time to offer resources to parents. By leveraging AI to facilitate free-form conversations, our study aims to design an AI-driven chatbot to bridge these gaps in child-parent-provider communication. We conducted two studies: 1) design sessions with 12 children with cancer and their parents, which informed the development of our chatbot, ARCH, and 2) an interview study with 15 pediatric care experts to identify potential challenges and refine ARCH’s role in pediatric communication. Our findings highlight three key roles for ARCH: providing an expressive outlet for children, offering reassurance to parents, and serving as an assessment tool for providers. We conclude by discussing design considerations for AI-driven chatbots in pediatric communication, such as creating communication spaces, balancing the expectations of children and parents, and addressing potential cultural differences.

With new payment systems to prompt more sophisticated data activities, primary care practices are developing technological capabilities to manage patient care and information. One burgeoning capability is the collection of social determinants of health (SDOH) data and using that information to provide social care. This study describes the information infrastructure and technological capabilities developed by community health centers (CHCs) and examines the factors influencing SDOH data integration and management in primary care practice. It offers health care leaders insights and strategies to build capacity for managing social care and quality. An observational design was used to examine the technological capabilities of CHCs in Michigan via a practice survey, and factors related to developing information infrastructure were qualitatively explored. The practice survey, semi-structured interviews, and national health center data were analyzed. Sociotechnical systems and organizational theories were used to develop the survey and interview guide. A sample of Michigan CHCs (n = 15) was recruited for the study. The practice survey was administered to CHC leaders, clinicians, and staff (n = 27). Semi-structured interviews (n = 25) were then conducted to explore infrastructural, organizational, and technological factors associated with managing social care and information. Michigan CHCs developed capabilities to exchange patient information with state and local partners. Data were typically shared with maternal and infant health (n = 5, 33.3

In democratic societies around the world, the number of science policy decisions is increasing. One of the fundamental principles of democracy is that citizens should be able to understand the issues before them. Using a 63-year cross-sectional US data set, we use confirmatory factor analysis to construct and test a two-dimensional measure of attitude to science and technology that has been relatively stable over the last six decades. Previous and current research tells us that only one in three US adults is scientifically literate, meaning that trust in scientific expertise is important to many citizens. We find that trust in scientific expertise polarized during the Trump administration. Using the same data set, we construct two structural equation models to determine the factors that predict positive attitudes toward science and technology. Comparing 2016 and 2020, we find that the Trump attacks on science did not reduce public support for science.

Social media systems are as varied as they are pervasive. They have been almost universally adopted for a broad range of purposes including work, entertainment, activism, and decision making. As a result, they have also diversified, with many distinct designs differing in content type, organization, delivery mechanism, access control, and many other dimensions. In this work, we aim to characterize and then distill a concise design space of social media systems that can help us understand similarities and differences, recognize potential consequences of design choice, and identify spaces for innovation. Our model, which we call Form-From, characterizes social media based on (1) the form of the content, either threaded or flat, and (2) from where or from whom one might receive content, ranging from spaces to networks to the commons. We derive Form-From inductively from a larger set of 62 dimensions organized into 10 categories. To demonstrate the utility of our model, we trace the history of social media systems as they traverse the Form-From space over time, and we identify common design patterns within cells of the model.

Research on public attitudes toward science and technology policy has relied on surveys taken at single points in time. These surveys fail to indicate how these attitudes develop or change. In this study, we use data from the Longitudinal Study of American Life that has followed a national sample of Generation X for 33 years-from middle school to midlife. We demonstrate that the critical period for the formation of attitudes toward science and technology is the 15-18 years after high school-college, work, family, and career. The attitudes formed in this period remain stable for most individuals during midlife. This work provides an important perspective for scientists, engineers, and the leadership of the scientific community in their efforts to foster positive attitudes toward science and technology and to understand the roots of concerns and reservations about science.

The public acceptance of evolution remains a contentious issue in the United States. Numerous investigations have used national cross-sectional studies to examine the factors associated with the acceptance or rejection of evolution. This analysis uses a 33-year longitudinal study that followed the same 5000 public-school students from grade 7 through midlife (ages 45-48) and is the first to do so in regard to evolution. A set of structural equation models demonstrate the complexity and changing nature of influences over these three decades. Parents and local influences are strong during the high school years. The combination of post-secondary education and occupational and family choices demonstrate that the 15 years after high school are the switchyards of life.

In Detroit, the largest Black-majority city in the United States, municipal authorities have deployed an array of surveillance technologies with the promise of containing crime and improving community safety. This article draws from a cross-sectional survey of over two thousand Detroit residents and multi-year community-based fieldwork in Detroit's Eastside to examine local perceptions of policing surveillance technologies. Our survey reveals that respondents, notably those in more vulnerable positions, report higher perceived safety levels with policing surveillance cameras in their neighborhoods. However, when triangulating these results with insights from our fieldwork, we argue that these survey findings should not be taken as public support for surveillance. Alongside this seeming buy-in is a widely shared "better than nothing" imaginary among residents from impacted communities. "Better than nothing," for the residents, is a pragmatic compromise and maneuver between being aware of the inherent flaws of surveillance technologies and settling for any available resource or hope. This notion of "better than nothing" unveils residents' prolonged wait for digital justice and institutional accountability, which we show is where racialized infrastructural harm and exploitation are enacted along the temporal dimension. Our findings offer practical insights for counter-surveillance advocacy efforts.

In Detroit, the largest Black-majority city in the United States, municipal authorities have deployed an array of surveillance technologies with the promise of containing crime and improving community safety. This article draws from a cross-sectional survey of over two thousand Detroit residents and multi-year community-based fieldwork in Detroit’s Eastside to examine local perceptions of policing surveillance technologies. Our survey reveals that respondents, notably those in more vulnerable positions, report higher perceived safety levels with policing surveillance cameras in their neighborhoods. However, when triangulating these results with insights from our fieldwork, we argue that these survey findings should not be taken as public support for surveillance. Alongside this seeming buy-in is a widely shared “better than nothing” imaginary among residents from impacted communities. “Better than nothing,” for the residents, is a pragmatic compromise and maneuver between being aware of the inherent flaws of surveillance technologies and settling for any available resource or hope. This notion of “better than nothing” unveils residents’ prolonged wait for digital justice and institutional accountability, which we show is where racialized infrastructural harm and exploitation are enacted along the temporal dimension. Our findings offer practical insights for counter-surveillance advocacy efforts.

Introduction:This study aimed to map the maturity of precision oncology as an example of a Learning Health System by understanding the current state of practice, tools and informatics, and barriers and facilitators of maturity.Methods:We conducted semi-structured interviews with 34 professionals (e.g., clinicians, pathologists, and program managers) involved in Molecular Tumor Boards (MTBs). Interviewees were recruited through outreach at 3 large academic medical centers (AMCs) (n = 16) and a Next Generation Sequencing (NGS) company (n = 18). Interviewees were asked about their roles and relationships with MTBs, processes and tools used, and institutional practices. The interviews were then coded and analyzed to understand the variation in maturity across the evolving field of precision oncology.Results:The findings provide insight into the present level of maturity in the precision oncology field, including the state of tooling and informatics within the same domain, the effects of the critical environment on overall maturity, and prospective approaches to enhance maturity of the field. We found that maturity is relatively low, but continuing to evolve, across these dimensions due to the resource-intensive and complex sociotechnical infrastructure required to advance maturity of the field and to fully close learning loops.Conclusion:Our findings advance the field by defining and contextualizing the current state of maturity and potential future strategies for advancing precision oncology, providing a framework to examine how learning health systems mature, and furthering the development of maturity models with new evidence.

The bereaved interact with photos of the deceased in various ways to maintain continuing bonds. Extant research in HCI and interaction design suggests leveraging data on photos’ contexts (e.g., time and place) to support photo use by the bereaved. Extending this research, we interviewed 17 bereaved parents to examine how the bereaved might also interact with photos’ content (e.g., color schemes, facial expressions). We characterize their interactions as processes of meaning-making that enabled them to keep their finite photos relevant and maintain continuing bonds with their children. By identifying meaning-making processes in terms of interactions with both photos’ content and contexts, we open up possibilities for supporting photo use for the bereaved that leverage content-related data. Our findings more broadly highlight the importance of accounting for subjective meaning-making processes in data-driven approaches to photo use in HCI and computer vision.

Noticing differently commits to stepping out of familiar reference frameworks while attending to oft-neglected actors, relations, and ways of knowing for design. Photovoice is an arts- and community-based participatory approach allowing individuals to communicate their lives and stories about pressing community concerns through photography. This paper bridges photovoice and the commitment to noticing in HCI and design through a photovoice project with Detroit residents on safety and surveillance. The photovoice process—alongside the production, reflection, and dissemination of photographs—makes residents’ everyday situations legible and sensible, allowing both community members and researchers to orient to and engage with multiple viewpoints, sensibilities, and temporal trajectories. This process confronts the invisibility of both the sociotechnical infrastructures (in our case, surveillance infrastructures) and minoritized communities’ relational ontologies. By advocating participatory noticing in design research, we show the opportunities for adopting arts- and community-based participatory approaches in decentering dominant ways of knowing and seeing, while at the same time fostering community capacity and relations for future potentialities.

The acquisition of information and its use in decision making and coping by people with health concerns have garnered much attention in CSCW. This study investigated patients' information behavior during a critical treatment, in vitro fertilization (IVF). Based on in-depth interviews with 29 IVF patients, this study uncovered several underlying drivers and mechanisms accounting for patients' information behavior. Their behavior is shown to be driven by coping concerns - specifically, dealing with the unpredictability of the treatment outcome, overcoming feelings of powerlessness and the sense of being out of control, and managing difficult emotionality. These factors shape patients' information needs and drive their behaviors in seemingly irrational but ultimately logical and adaptive ways. In contrast to the conventional wisdom that patients typically seek information that can help them fill knowledge gaps to resolve treatment uncertainty and foster their positive emotions, we discovered that in response to the desire to control their perceptions of irresolvable uncertainty and the difficult emotional needs of the moment, IVF patients frequently used ?calibrated uncertainty" - a psychological mechanism or state driven by the simultaneous seeking of varying levels and contradictory valences of certainty - to actively conduct targeted searches and actively use the information sought. This behavior has not always been understood by the IVF clinicians, who have assumed that information was primarily for knowledge transfer. This study shows that coping, emotion regulation, and information-seeking are inextricably bound together in the patient experience, and that this intertwining must be considered in the clinical setting for physician-patient communication and for patient-facing information. The findings have several valuable design implications for improved health informatics technology and service delivery systems.

Safety has been used to justify the expansion of today’s large-scale surveillance infrastructures in American cities. Our work offers empirical and theoretical groundings on why and how the safety-surveillance conflation that reproduces harm toward communities of color must be denaturalized. In a photovoice study conducted in collaboration with a Detroit community organization and a university team, we invited 11 Black mid-aged and senior Detroiters to use photography to capture their lived experiences of navigating personal and community safety. Their photographic narratives unveil acts of “everyday noticing” in negotiating and maintaining their intricate and interdependent relations with human, non-human animals, plants, spaces, and material things, through which a multiplicity of meaning and senses of safety are produced and achieved. Everyday noticing, as simultaneously a survival skill and a more-than-human care act, is situated in residents’ lived materialities, while also serving as a site for critiquing the reductive and exclusionary vision embedded in large-scale surveillance infrastructures. By proposing an epistemological shift from surveillance-as-safety to safety-through-noticing, we invite future HCI work to attend to the fluid and relational forms of safety that emerge from local entanglement and sensibilities.

Algorithmic systems have infiltrated many aspects of our society, mundane to high-stakes, and can lead to algorithmic harms known as representational and allocative. In this paper, we consider what stigma theory illuminates about mechanisms leading to algorithmic harms in algorithmic assemblages. We apply the four stigma elements (i.e., labeling, stereotyping, separation, status loss/discrimination) outlined in sociological stigma theories to algorithmic assemblages in two contexts : 1) "risk prediction" algorithms in higher education, and 2) suicidal expression and ideation detection on social media. We contribute the novel theoretical conceptualization of algorithmic stigmatization as a sociotechnical mechanism that leads to a unique kind of algorithmic harm: algorithmic stigma. Theorizing algorithmic stigmatization aids in identifying theoretically-driven points of intervention to mitigate and/or repair algorithmic stigma. While prior theorizations reveal how stigma governs socially and spatially, this work illustrates how stigma governs sociotechnically.

The ways that humans acquire information is undergoing a fundamental change comparable with the introduction of Gutenberg's printing press and broadcast systems. Using the literature and a growing body of empirical evidence, including national surveys in 2017, 2019, and 2020, we describe a model of normal space science information acquisition that specifies the roles of education, salience, subject-matter literacy, and navigation skills in the decision to seek information. We contrast this normal model with two models of event-driven or special space science information acquisition, using (1) the 2017 total solar eclipse (TSE) and (2) the 50th anniversary of the first lunar landing in 1969 as examples. We conclude with a discussion of the implications of a just-in-time space science information acquisition system for the space science community, including scientists, leaders, and educators.

Chronic health conditions are becoming increasingly prevalent. As part of chronic care, sharing patient-generated health data (PGHD) is likely to play a prominent role. Sharing PGHD is increasingly recognized as potentially useful for not only monitoring health conditions but for informing and supporting collaboration with caregivers and healthcare providers. In this paper, we describe a new design for the fine-grained control over sharing one's PGHD to support collaborative self-care, one that centers on giving people with health conditions control over their own data. The system, Data Checkers (DC), uses a grid-based interface and a preview feature to provide users with the ability to control data access and dissemination. DC is of particular use in the case of severe chronic conditions, such as spinal cord injuries and disorders (SCI/D), that require not just intermittent involvement of healthcare providers but daily support and assistance from caregivers. In this paper, after providing relevant background information, we articulate our steps for developing this innovative system for sharing PGHD including (a) use of a co-design process; (b) identification of design requirements; and (c) creation of the DC System. We then present a qualitative evaluation of DC to show how DC satisfied these design requirements in a way that provided advantages for care. Our work extends existing research in the areas of Human-Computer Interaction (HCI), Computer-Supported Cooperative Work (CSCW), Ubiquitous Computing (Ubicomp), and Health Informatics about sharing data and PGHD.

The Covid-19 pandemic posed new issues about vaccination and contagious diseases that had not been the focus of public policy debate in the United States since the tuberculosis pandemic of the late 19th century and the early 20th century. Using a national address-based probability sample of American adults in 2020 and a structural equation model, this analysis seeks to understand the role of education, age, gender, race, education, partisanship, religious fundamentalism, biological literacy, and understanding of the coronavirus to predict individual intention concerning taking the Covid-19 vaccine. Given the substantial changes in the United States since the tuberculosis pandemic, it is important to understand the factors that drive acceptance and hesitancy about Covid-19 vaccination. We find that education, biological literacy, and understanding of the coronavirus were strong positive predictors of willingness to be vaccinated and religious fundamentalism and conservative partisanship were strong negative predictors of intent to vaccinate. These results should be encouraging to the scientific community.