Using Virtual Human Technology to Examine Weight Bias and the Role of Patient Weight on Student Assessment of Pediatric Pain

Abstract

The purpose of the study was to investigate the influence of weight bias and demographic characteristics on the assessment of pediatric chronic pain. Weight status, race, and sex were manipulated in a series of virtual human (VH) digital images of children. Using a web-based platform, 96 undergraduate students with health care-related majors (e.g., Health Science, Nursing, Biology, and Pre-Medicine) read a clinical vignette and provided five ratings targeting the assessment of each VH child's pain. Students also answered a weight bias questionnaire. Group-based analyses were conducted to determine the influence of the VH child's weight and demographic cues, as well as greater weight bias on assessment ratings. Male and VH children with obesity were rated as more likely to avoid non-preferred activities due to pain compared to female and healthy weight children, respectively (both p < .001). The pain of VH children with obesity was rated as more likely to be influenced by psychological/behavioral issues compared to the pain of healthy weight VH children (p = .022). African American VH children were rated as experiencing significantly greater pain than Caucasian VH children (p = .037). As child weight increased, low weight bias participants felt more sympathy, while high weight bias participants felt less sympathy (p = .002). Also, low weight bias participants showed increased motivation to help, while high weight bias participants showed less motivation to help, as VH patient weight increased (p = .008). Child weight and evaluator weight bias may be influential in the assessment of pediatric pain. If supported by future research, results highlight the importance of training in evidence-based practice and education on weight bias for students majoring in health-care fields.

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