“It’s always happy to see me”: Exploring LOVOT robots as companions for older adults
Bibliographic record
Abstract
Background: AI-enabled social robots present the potential to resolve the loneliness and social isolation of older adults in long-term care (LTC). There is limited research on how older adults perceive and make sense of these robots and how human-robot companionship is formed. This study investigated older adults' experiences using LOVOT, a social robot. Methods: Using an ethnographic study design, we introduced LOVOT robots to a Canadian LTC home for four weekly interaction sessions. Thirty-six residents, seven family members and two healthcare staff participated. Data collection involved observational field notes and conversational interviews. The analysis was guided by ikigai, a Japanese well-being concept. Findings: Reflexive thematic analysis identified four key themes. 1) Joy: The robot offers joy and excitement through interactions. 2) Acceptance: For older adults with mobility or cognitive impairments, LOVOT gives consistent positive responses, offering a sense of unconditional acceptance. 3) Creativity: The robot's non-verbal communication allows older adults to grow creative imagination, encouraging personal expression and expanding interaction possibilities. 4) "Not for me": Not all participants like the LOVOT robot. Conclusion: AI-enabled social robots show potential in supporting the psychosocial needs of older adults, which have broader implications for LTC practices and future research directions. Future research should further explore the creative utility of social robots among LTC residents.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".