77 Qualitative Needs Assessment of Child and Caregiver Perspectives to Inform Design of an Artificial Intelligence-enhanced Social Robot to Improve Paediatric Emergency Care
Bibliographic record
Abstract
Abstract Background Children routinely experience under-treated pain and distress related to medical procedures, such as intravenous insertions (IVI), which can have negative consequences in both the short-term (e.g., fear, inability to complete procedure) and long-term (e.g., needle phobia, healthcare avoidance). Socially assistive robots (SARs) are a promising tool to manage pain and distress in paediatric healthcare settings. As we work towards building a more developmentally adaptive and interactive SAR, we must first understand how children and families perceive SAR to create a safe and ethical tool. Objectives The aim of this study was to understand children’s and caregivers’ perceptions of interacting with an artificial intelligence (AI)-enhanced SAR as a distraction tool in the paediatric emergency department (ED) to improve their IVI experience. Design/Methods We conducted semi-structured interviews and focus groups with children and their caregivers from two Canadian paediatric EDs between April 2021 to January 2022, with interview transcription and analysis conducted concurrently until thematic saturation was achieved. Qualitative content analysis was performed independently by two team members, who met regularly to discuss the coding scheme and key themes, and facilitate iterative adjustments to the interview script based on emerging themes. Results Nineteen children (mean age 8.42 years [SD 2.21]) and twelve caregivers were included. Three main themes were identified: (1) Overall ED experience, (2) General acceptance of a SAR, and (3) Suggested SAR features to support child engagement. Most participants were comfortable in the ED but identified long wait times and lack of technological supports (e.g., iPads) as an impediment to positive experience. Most participants expressed excitement and comfort surrounding robot technology. However, concerns were raised about photo/video capture by the SAR and the possibility of technical failure resulting in child disappointment or disengagement. Suggestions for potential robot features were unique to the phase of IVI: before IVI (developmentally appropriate procedure explanation, creation of a shared distraction plan); during IVI (variety and choice of distractions, SAR capability to stop or alter course); and after IVI (debriefing and positive reinforcement). Conclusion Overall, AI-enhanced SARs were perceived by children and caregivers as a promising tool to distract children. Insights collected will be used to inform the ethical and emotionally safe design of an AI-enhanced SAR. Next steps include development and usability testing of the SAR, subsequent evaluation in the paediatric ED via a randomized controlled trial, and clinical implementation.
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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 | 0.022 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".