The importance of developing care‐worker‐centered robotic aides in long‐term care
Bibliographic record
Abstract
Recent research points to the fact that new medical technological innovations are just as relevant in the context of long-term care or chronic care as they are in the context of acute care. In the spirit of the Nuffield Foundation recommendations, this paper explores the possibilities of using robotic aides in long-term care and identifies the tensions that must be considered and addressed if robotics is to be introduced successfully in nursing homes. Our examination is two-pronged. First, we delve into a fundamental issue surrounding AI, namely that of consciousness. We argue that automation will always have only a limited use in caregiving since caregiving as an activity requires the use of human-type, that is, organic, consciousness. We support the thesis that the emergence and formation of human-type consciousness require feelings such as empathy and the sense of touch, which, in turn, create the sense of kinship with fellow human beings. And second, we examine the benefits as well as risks of using robotic aides such as ZORA and PARO in long-term care facilities. More specifically, we look at ZORA's use in a group setting, and PARO's use in an individual setting. We emphasize that long-term care is one-on-one care, including but not limited to intimate care. Crucially, we argue that touch is at the heart of this type of care. We argue that some of the tensions with the use of robotic aides are generated precisely because of the lack of human touch.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".