Extending prior HCI and CSCW research on the invisible challenges domestic care workers face, we examine how childcare workers, particularly nannies, experience and manage workplace risks. Drawing on interviews with 21 nannies, we identified three interrelated risks—physical, emotional, and financial—arising from structural and relational constraints in employers’ homes. Through the lens of risk work, we show how these multi-dimensional constraints create tensions that hinder nannies’ direct risk mitigation strategies. This often compels them to prioritize indirect risk management to avoid tensions, leaving risks themselves unresolved. Our study highlights the need for future research and sociotechnical interventions that address domestic childcare workers’ unique constraints, identify their coping strategies through a risk work lens, and illuminate the risks obscured by indirect coping. We further call for recognizing the limitations of both personal tools and employer-centered home technologies, and propose worker-centered, reciprocal interventions as well as virtual and psychological separation in the workplace.

While prior research has focused on providers, caregivers, and adult patients, little is known about adolescents’ perceptions of AI in health learning and management. Utilizing design fiction and co-design methods, we conducted seven workshops with 23 adolescents (aged 14-17) to understand how they anticipate using health AI in the context of a family celiac diagnosis. Our findings reveal that adolescents have four main envisioned roles of health AI: enhancing health understanding and help-seeking, reducing cognitive burden, supporting family health management, and providing guidance while respecting their autonomy. We also identified nuanced trust and a divided view toward emotional support from health AI. These findings suggest that adolescents perceive AI’s value as a tool that moves them from efficiency to meaning–one that creates time for valued activities. We discuss opportunities for future health AI systems to be designed to encourage adolescent autonomy and reflection, while also supporting meaningful, dialectical activities.

OBJECTIVE: Ambient artificial intelligence (AI) documentation is increasingly used to draft clinical notes from patient-provider conversations, but how clinicians revise and finalize these drafts is not well understood. This qualitative content analysis study characterizes real-world edits to AI-generated drafts and identifies opportunities for improvement of AI design and the implementation process. MATERIALS AND METHODS: Eight coders analyzed clinical documentation generated by ambient AI from 200 clinical encounters. We developed an inductive coding framework with 11 codes across 3 categories: clinical content, terminology, and language style. Interrater reliability was assessed using Cohen's kappa. We then applied thematic analysis to synthesize patterns across the coded edits. RESULTS: The most frequently edited content pertained to clinical facts including orders (eg, procedures, lab tests) (40.0%), symptoms (30.3%), medication prescriptions (27.3%), and diagnosis descriptions (25.9%). In comparison, edits related to terminology use (11.6%) and language style (7.2%) were less frequent. The results of our thematic analysis show that most edits can be categorized into one of the following 5 types: to revise factual discrepancies, to add medical specialty-specific details, to express diagnostic certainties, to convert patient expressions into objective assessments recorded in medical terms, and to reorganize or condense content. CONCLUSION AND DISCUSSION: Clinicians routinely revise ambient AI drafts to modify factual details and clinical specificity. Future work on AI development and clinical implementation should emphasize specialty customization and support personalized documentation practices, alongside clinician education that promotes robust and consistent review routines to ensure documentation quality.

The paradigm of Logical Execution Time (LET) tasks is widely adopted by major tool vendors for designing deterministic and time-predictable software in multi-core systems, particularly in the automotive industry. To extend the use of LET in distributed environments, System Level Logical Execution Time (SL-LET) has been developed to effectively manage communication and delays between networked devices. However, there is currently a lack of open-source tools available for SL-LET, and the task allocation and scheduling problem for SL-LET remains unsolved.

With the rise of carework platforms, domestic childcare workers such as nannies are increasingly seeking new jobs through technology-mediated marketplaces (TMMs), and scholarly interest in this context has been growing. However, little is known about how these workers navigate their career journeys-from finding motivation and developing skills to seeking job opportunities, choosing families, negotiating terms, and managing job turnover. Through interviews with 21 career nannies, our findings reveal that they face numerous challenges in balancing the dual nature of care work throughout their career cycle: a transactional nature pursuing monetary compensation through their labor and a relational nature bonding with employer families for quality care. We argue that the transactional and relational nature of nanny work simultaneously or independently influence each stage of the career cycle, giving rise to three key challenges: balancing these dual natures, building fair and compatible relationships with employers, and maintaining career identity for professional development. We then discuss how the design of TMMs and job-seeking resources can be optimized to address these challenges.