A biomedical ethical analysis of using socially assistive robots with an animal-like form with elderly individuals in institutionalized care
Bibliographic record
Abstract
Abstract Animal-like Socially Assistive Robots (A-SARs) are primarily intended to improve patients' quality of life in institutional eldercare facilities and to reduce caregiver workload by simulating the benefits of Animal Assisted Therapy. A-SARs are used by elderly residents, caregivers, family members, and researchers in institutionalized eldercare. These robots are used to promote emotional, social, and imaginative engagement as well as empathy and communication. While benefits of usage has been extensively evaluated, the ethical implications of replacing human interactions with A-SARs has not been considered in-depth.The overarching objective of this thesis was to examine the ethical considerations for using A-SARs in eldercare. A critical interpretive literature review revealed that major ethical concerns were centered around interpretations of the rights of elderly residents, caregiver expectations, and family obligations. The use of A-SARs was not considered inherently unethical if it was used to improve communication and existing relationships. Ethical theories used to support the considerations found in the literature were: duty-based deontology, virtue ethics, ethics of care, and the capabilities approach. The wider Patient-Centered Clinical Method provided a more in-depth theoretical analysis approach for Canadian eldercare. The four components of the model—1) exploring health, disease, and the illness experience, 2) understanding the whole person, 3) finding common ground, and 4) enhancing the patient-doctor relationship—were analyzed independently in light of the major ethical considerations raised about the use of A-SARs in institutional eldercare. The legal framework of the Ontario Long Term Home Act was taken as an example of normative requirements that governs the ethical expectations of resident rights and caregiver expectations. The conclusion of this thesis suggests that the legislation is compatible with patient-centered care and guides the ethical expectations that ground caregiver-resident relationships when using A-SARs. Furthermore, considerations from the capabilities approach enrich how capabilities would impact caregiving relationships. The insights in this thesis presents a relevant contribution to the applied utilization, ethical concerns, and legislative considerations for future discussions on the impact of A-SARs use on institutional eldercare practices. The findings from this thesis may be useful for future discussions in the dynamic field of socially assistive robots and eldercare
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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 | 0.048 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.014 | 0.024 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".