Home‐care robots – Attitudes and perceptions among older people, carers and care professionals in Ireland: A questionnaire study
Bibliographic record
Abstract
Many countries face major challenges to ensure that their health and social care systems are ready for the growing numbers of older people (OP). As a way of realising ageing in place, assistive technologies such as home-care robots are expected to play a greater role in the future. In Asia and Europe, robots are gradually being adopted as a public policy solution to the workforce shortage. Yet, there is still a strongly held belief that such technologies should not be part of human and personal care services such as OP's care. However, there has been little research into attitudes and perceptions of potential users regarding home-care robots which can provide companionship and support with activities of daily living. To explore these in more detail, a questionnaire study was carried out in Finland, Ireland and Japan. This study reports findings from the Irish cohort (114 older people [OP], 8 family carers and 56 Health and Social Care Professionals [HSCPs]). Seventy per cent of the total respondents (N = 178) reported being open to the use of home-care robots, and only one quarter had a negative image of robots. People with care responsibilities in their private capacity expressed more interest in, and readiness to use, home-care robots, while stressing the importance of 'privacy protection' and 'guaranteed access to human care'. Both OP and HSCPs identified observation and recording of OP's mental and physical condition as desirable functions of such robots, whereas practical functions such as fall prevention and mobility support were also deemed desirable by HSCPs. There is generally positive interest in home-care robots among Irish respondents. Findings strongly suggest that the interest is generated partly by great need among people who deliver care. Should such robots be developed, then careful consideration must be given to user-centred design, ethical aspects and national care policy.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".