“A Safe Space for Sharing Feelings”: Perspectives of Children with Lived Experiences of Anxiety on Social Robots
Bibliographic record
Abstract
Social robots have the potential to support health and quality of life for children experiencing anxiety. We engaged families with lived experiences of pediatric anxiety in social robot development to explore desired design features, application areas, and emotion functionalities of social robots in anxiety care. We conducted 10 online co-creation workshops with (1) children with anxiety aged 7–13 (n = 24) with their family members (n = 20), and (2) youth with anxiety aged 14–18 (n = 12). Workshop participation included a validated robot expectations scale, anonymous polls, and discussion. Transcripts and text responses were subjected to content analysis. A lived experience expert group provided feedback throughout the research. Participants desired a pet-like robot with a soft texture, expressive eyes, and emotion detection to support activities of daily living. Specific anxiety-related applications included breathing exercises, managing distressing thoughts, and encouragement. Emotional alignment, the design of a robot’s emotional display, and the emotional impacts of an interaction were discussed. Privacy and the replacement of human interaction were concerns. We identify pediatric anxiety-specific design features, applications, and affective considerations for existing and future social robots. Our findings highlight the need for customizability and robust emotional functionality in social robot technologies intended to support the health and care of children living with anxiety.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".