Social Robot Interactions in a Pediatric Hospital Setting: Perspectives of Children, Parents, and Healthcare Providers
Bibliographic record
Abstract
Socially assistive robots are embodied technological artifacts that can interact socially with people. These devices are increasingly investigated as a means of mental health support in different populations, especially for alleviating loneliness, depression, and anxiety. While the number of available, increasingly sophisticated social robots is growing, their adoption is slower than anticipated. There is much effort to determine the effectiveness of social robots in various settings, including healthcare; however, little is known about the acceptability of these devices by the following distinct user groups: healthcare providers, parents, and children. To better understand the priorities and attitudes of social robot users, we carried out (1) a survey of parents and children who have previously been admitted to a hospital and (2) a series of three modified focus group meetings with healthcare providers. The online survey (n = 71) used closed and open-ended questions as well as validated measures to establish the attitudes of children and parents towards social human–robot interaction and identify any potential barriers to the implementation of a robot intervention in a hospital setting. In the focus group meetings with healthcare providers (n = 10), we identified novel potential applications and interaction modalities of social robots in a hospital setting. Several concerns and barriers to the implementation of social robots were discussed. Overall, all user groups have positive attitudes towards interactions with social robots, provided that their concerns regarding robot use are addressed during interaction development. Our results reveal novel social robot application areas in hospital settings, such as rapport-building between patients and healthcare providers and fostering patient involvement in their own care. Healthcare providers highlighted the value of being included and consulted throughout the process of child–robot interaction development to ensure the acceptability of social robots in this setting and minimize potential harm.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".