Older Adults Interacting With Social Robots: Toward Greater Social Participation and Connection
Bibliographic record
Abstract
Abstract Some older adults require stimulation, learning, or assistance in their interactions, particularly when having disabilities. Social robots are promising to foster their participation and connection, and prevent situations of isolation. To our knowledge, Tabletop (T-Top) presents the most advanced communication and autonomous reasoning capabilities, but little is known about how older adults interact and perceived their experience with this robot. This study thus aimed to explore how older adults interact and perceive their experience with the social robot T-Top. An exploratory qualitative clinical research design was used with semi-directed interviews and observation of six older adults living in one senior residence. Older participants interacted with T-Top twice for about 30 minutes, an observation grid was used to identify their reactions and interactions, followed by interviews with a semi-structured guide to explore their experience and satisfaction. During the interaction sessions, all older adults expressed joy and surprise and maintained interested eye contact with the robot. The majority of older adults engaged in conversation with T-Top and actively participated. Although all participants appreciated the robot’s responsiveness and interactive behaviour, and, for most of them, its thinking and listening skills, some older adults were bothered by lack of perseverance and artificial appearance of T-Top. This study highlighted that social robots like T-Top should be personalized to older adults’ needs, preferences, and habits to facilitate interactions. More studies are needed to evaluate how such promising interventions can be used to foster older adults’ social participation and connection, and to prevent situations of isolation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".