Pain experiences of marginalized children in the emergency department: A scoping review protocol.

INTRODUCTION: Pain affects all children, and in hospitals across North America, this pain is often undertreated. Children who visit the emergency department (ED) experience similar undertreatment, and they will often experience a painful procedure as part of their diagnostic journey. Further, children and their caregivers who experience social injustices through marginalization are more likely to experience healthcare disparities in their pain management. Still, most of our knowledge about children's pain management comes from research focused on well-educated, white children and caregivers from a middle- or upper-class background. The aim of this scoping review is to identify, map, and describe existing research on (a) how aspects of marginalization are documented in randomized controlled trials related to children's pain and (b) to understand the pain treatment and experiences of marginalized children and their caregivers in the ED setting. METHODS AND ANALYSIS: The review will follow Joanna Briggs Institute methodology for scoping reviews using the Participant, Concept, Context (PCC) framework and key terms related to children, youth, pain, ED, and aspects of marginalization. We will search Medline, Embase, PsychInfo, CINAHL, Web of Science, Cochrane Library Trials, iPortal, and Native Health Database for articles published in the last 10 years to identify records that meet our inclusion criteria. We will screen articles in a two-step process using two reviewers during the abstract and full-text screening stages. Data will be extracted using Covidence for data management and we will use a narrative approach to synthesize the data. ETHICS AND DISSEMINATION: Ethical approval is not required for this review. Findings will be disseminated in academic manuscripts, at academic conferences, and with partners and knowledge users including funders of pain research and healthcare professionals. Results of this scoping review will inform subsequent quantitative and qualitative studies regarding pain experiences and treatment of marginalized children in the ED.

Exploring the needs of children and caregivers to inform design of an artificial intelligence-enhanced social robot in the pediatric emergency department.

Background & Objective: Socially assistive robots (SARs) are a promising tool to manage children's pain and distress related to medical procedures, but current options lack autonomous adaptability. The aim of this study was to understand children's and caregivers' perceptions surrounding the use of an artificial intelligence (AI)-enhanced SAR to provide personalized procedural support to children during intravenous insertion (IVI) to inform the design of such a system following a user-centric approach. Methods: This study presents a descriptive qualitative needs assessment of children and caregivers. Data were collected via semi-structured individual interviews and focus groups. Participants were recruited from two Canadian pediatric emergency departments (EDs) between April 2021 and January 2022. Results: Eleven caregivers and 19 children completed 27 individual interviews and one focus group. Three main themes were identified: A. Experience in the clinical setting, B. Acceptance of and concerns surrounding SARs, and C. Features that support child engagement with SARs. Most participants expressed comfort with robot technology, however, concerns were raised about sharing personal information, photographing/videotaping, and the possibility of technical failure. Suggestions for feature enhancements included increasing movement to engage a child's attention and tailoring language to developmental age. To enhance the overall ED experience, participants also identified a role for the SAR in the waiting room. Conclusion: Artificial intelligence-enhanced SARs were perceived by children and caregivers as a promising tool for distraction during IVIs and to enhance the overall ED experience. Insights collected will be used to inform the design of an AI-enhanced SAR.