“Like another human being in the room”: a community case study of smart speakers to reduce loneliness in the oldest-old
Bibliographic record
Abstract
This community case study examined the potential benefits of smart speakers to tackle loneliness in the oldest old adults living in supported accommodation. The program was established as a collaboration between the supported accommodation provider and a technology company to explore the feasibility of smart speakers to alleviate resident loneliness. Loneliness in later life often accompanies a shrinking social circle, loss of a spouse or increased disability. People aged 85 years of age and over are increasingly likely to experience these life events, leading to an increased risk of social isolation and loneliness. Five older people, mean age 90 years of age, who resided in supported accommodation, were given a smart speaker for 8 weeks to examine their experience with the voice assistant. The experiences of the five older adults are explored as case studies, with each person interviewed both before and after receiving the smart speaker. All five valued their smart speaker, recognised its potential for tackling loneliness, and wanted to keep it. The three most lonely individuals reported that their smart speaker made them feel less lonely and isolated through two mechanisms: (i) creating a presence and (ii) having some control over their situation. Although only a small study, these experiences suggest providing smart speakers for lonely and isolated oldest-old people, could be one way to help combat loneliness in community settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".