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TRUVACE RECORD VERSION record: TRV-2026-0430 version: 1 kind: certified reason: Certified into the record timestamp: 2026-07-20T10:44:14.860712Z status: published lens: p_space sector: health headline: Breaking or Repairing Long-Term Care for Older People? dek: From robots to chatbots, AI technologies in care (nursing) homes have gained policymakers’ attention amid critical issues like staffing shortages. Concurrently, the long-term care sector has become a prime target for technologists due to its global market potential given the growing ageing population. Drawing conceptually on ideas of breakdown and repair, we explore socio-technical discourses of AI-based care for later life. We combine Bruno Latour’s concept of delegation and Madeleine Akrich’s notion of user re… gain_title: (none) problem_title: AI marketing discourses for nursing homes depict older people as passive data sources and care staff as inefficient, reducing complex care to datafication and solutionism trace_subject: (none) gain_reading: (none) gain_evidence: (none) problem_reading: AI marketing discourses for nursing homes depict older people as passive data sources and care staff as inefficient, reducing complex care to datafication and solutionism problem_evidence: Older people were depicted as passive data sources and staff as inefficient quick_read: Published December 15 2025, this peer-reviewed discourse study examined how AI for later life is framed by industry. The authors analyzed the websites of 33 AI companies selling robots to chatbots into care (nursing) homes, using concepts of breakdown and repair, delegation, and user representations to identify dominant narratives. The framing matters because it positions older people as passive data sources and staff as inefficient while presenting AI as a fix for all caregiving challenges amid staffing shortages and market growth from an ageing population. What remains uncertain is whether these website discourses translate into actual care practices or outcomes in homes, since the study does not measure implementation or effects. limitation: tag: Evidence-backed problem key_points: Study analyzed 33 AI companies targeting long-term care using visual, textual, and semiotic analysis of their websites | Theoretical framing combined Bruno Latour's concept of delegation and Madeleine Akrich's notion of user representations | Identified four overarching discourses: ageing carefication, public inefficiencies, AI solutionism, and care datafication | Findings show older people depicted as passive data sources and staff as inefficient while AI positioned as solution to all caregiving challenges rundown: The authors drew on ideas of breakdown and repair to examine how socio-technical discourses may support breaking or repairing long-term care, using Latour's delegation and Akrich's user representations. The corpus was 33 AI companies targeting the sector, examined through website analysis that surfaced discourses of ageing carefication, public inefficiencies, AI solutionism, and care datafication, with implications discussed for caregiving's futures and reimagining AI-human care. sources: - peer_reviewed | Science & Technology Studies | https://doi.org/10.23987/sts.152562 | 2025-12-15 prev: 0000000000000000000000000000000000000000000000000000000000000000
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