Older adult perspectives on emotion and stigma in social robots
Bibliographic record
Abstract
Introduction: Social robot adoption by older adults and people living with dementia is limited by several barriers, including a lack of emotional alignment with social robots and perceptions of stigma around social robot use. The goal of this work was to address these barriers by engaging end-users in discussions on factors that could affect emotional connection to a social robot and considerations around using a social robot in public. Methods: = 17). Older adults with and without dementia were in their 50s - 80s, and care partners were in their 30s - 70s. Seven older adults, seven care partners, and one older adult with dementia indicated that they had used a robot before. Online workshops had 4-8 participants each, and began with video demonstrations of pet-like robot MiRo and tabletop robot T-Top, as well as a live demonstration of MiRo. Participants completed the Multi-Dimensional Robot Attitude Scale before the workshops and the Psychosocial Impact of Assistive Devices Scale and two Zoom polls during the workshops. The audio and chat responses from the workshops were transcribed verbatim and content analysis was performed on the transcripts. Results: Our analysis revealed three broad themes and 10 subthemes. In their discussions on features and applications, participants highlighted preferred forms of communication with a robot and ways in which a robot could support connection between people. For example, robots could improve the quality of communication between care partners and the person for whom they care. While many agreed that a social robot should match their level of emotion and interactivity, participants had different preferences for social robot emotional range and display features. Finally, participants discussed considerations around showing a robot to other people; several participants suggested that a robot could help raise awareness of ageing and dementia while others shared concerns about stigma and attracting negative attention from an audience. Discussion: Incorporating these findings into the design and implementation of social robots will result in devices that are better-suited to the needs of older adults, people living with dementia, and care partners.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".