Bringing a socially assistive robot to the paediatric emergency department: design, development, and usability testing
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
Introduction: Children undergoing medical procedures in paediatric Emergency Departments (EDs) often experience significant pain and distress. Socially Assistive Robots (SARs) offer a promising avenue for delivering distraction and emotional support in these high-pressure environments. This study presents the design, development, and formative evaluation of an AI-enhanced SAR to support children during intravenous insertion (IVI) procedures. Methods: The robot system was developed through a participatory design process involving healthcare professionals, patients, caregivers, and interdisciplinary research teams. The SAR was designed to autonomously adapt its behaviour to the child's affective state using AI planning and social signal processing. A two-cycle usability study was conducted across two Canadian paediatric EDs, involving 25 children and their caregivers. Feedback was collected through observations, interviews, and system logs. Results: The SAR was successfully integrated into clinical workflows, with positive responses from children, caregivers, and healthcare providers. Usability testing identified key technical and interaction challenges, which were addressed through iterative refinement. The final system demonstrated robust performance and was deemed ready for a formal randomised controlled trial. Discussion: This work highlights the importance of co-design, operator control, and environmental adaptability in deploying SARs in clinical settings. Lessons learned from the development and deployment process informed six concrete design guidelines for future SAR implementations in healthcare.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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