Ethical considerations in the use of social robots for supporting mental health and wellbeing in older adults in long-term care
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Social robots are increasingly being utilized to address mental health challenges in older adults, such as depression, anxiety, and loneliness. However, ethical concerns surrounding their use are insufficiently explored in empirical research. This paper examines the ethical challenges and mitigation strategies for implementing social robots in long-term care settings. Drawing from insights gained from research across two Canadian studies involving Paro and Lovot, we highlight the critical role of an equity-focused approach to ensure the ethical use of social robots. We advocate for the respectful inclusion of the voices and desires of marginalized groups, such as older adults with dementia. Key ethical issues discussed include inequitable access, consent, substitution of human care, and concerns about infantilization. Our empirical work offers practical strategies to navigate these challenges, aiming to ensure that social robots promote mental health and wellbeing in an ethically responsible manner for older adults living in long-term care.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it