Socially assistive robots in health and social care: Acceptance and cultural factors. Results from an exploratory international online survey
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
AIM: This study explored the views of an international sample of registered nurses and midwives working in health and social care concerning socially assistive robots (SARs), and the relationship between dimensions of culture and rejection of the idea that SARs had benefits in these settings. METHODS: An online survey was used to obtain rankings of (among other topics) the extent to which SARs have benefits for health and social care. It also asked for free text responses regarding any concerns about SARs. RESULTS: Most respondents were overwhelmingly positive about SARs' benefits. A small minority strongly rejected this idea, and qualitative analysis of the objections raised by them revealed three major themes: things might go wrong, depersonalization, and patient-related concerns. However, many participants who were highly accepting of the benefits of SARs expressed similar objections. Cultural dimensions of long-term orientation and uncertainty avoidance feature prominently in technology acceptance research. Therefore, the relationship between the proportion of respondents from each country who felt that SARs had no benefits and each country's ratings on long-term orientation and uncertainty avoidance were also examined. A significant positive correlation was found for long-term orientation, but not for uncertainty avoidance. CONCLUSION: Most respondents were positive about the benefits of SARs, and similar concerns about their use were expressed both by those who strongly accepted the idea that they had benefits and those who did not. Some evidence was found to suggest that cultural factors were related to rejecting the idea that SARs had benefits.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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