arbiter
JournalsPricingAccount

Breaking or Repairing Long-Term Care for Older People?

Bárbara Barbosa Neves et al.

Science and Technology Studies2025https://doi.org/10.23987/sts.152562article
AJG 2
Weight
0.41

What the paper says

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 representations to frame how these discourses can support breaking or repairing long-term care. Through this theoretical lens, we analysed 33 AI companies targeting the sector. Visual, textual, and semiotic analysis of their websites identified overarching discourses on ageing carefication, public inefficiencies, AI solutionism, and care datafication. Older people were depicted as passive data sources and staff as inefficient, positioning AI as the solution to all caregiving challenges. We consider implications for caregiving’s futures and reimagining AI-human care.

2 citations

Open paper page →

Cite this paper

https://doi.org/https://doi.org/10.23987/sts.152562

Or copy a formatted citation

@article{bárbara2025,
  title        = {{Breaking or Repairing Long-Term Care for Older People?}},
  author       = {Bárbara Barbosa Neves et al.},
  journal      = {Science and Technology Studies},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.23987/sts.152562},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

Breaking or Repairing Long-Term Care for Older People?

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.41

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.25 × 0.4 = 0.10
M · momentum0.55 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.