Using Community Detection Analysis to Elucidate Caregivers’ Mental Models of Pediatric Concussion Symptoms

• Main Authors: Emma Goodman, Logan Boe, Melissa Thye, Jessica Mirman
• Format: Article
• Language: English
• Published: MDPI AG 2018-08-01
• Series: Safety
• Subjects: concussion; clustering analysis; mental models; adolescence; injury; health
• Online Access: http://www.mdpi.com/2313-576X/4/3/35

Due to a culture of resistance around concussion reporting, novel methods are needed to reveal implicit beliefs that could affect symptom reporting. The goal of this study was to elucidate caregivers’ mental models of pediatric concussion symptoms using an exploratory community detection analysis (CDA). Caregivers (n = 76) of adolescents 10–15 years old participated in a survey that assessed their intentions of seeking medical treatment for 12 injury symptoms following their child’s involvement in three hypothetical injury scenarios. We used a series of analyses of variance (ANOVAs) to compare injury symptoms across these scenarios and CDA to determine if caregivers implicitly group symptoms together. We then used logistic regressions to further explore associations between the CDA-identified symptom indices and known factors of injury risk. There were no differences in the likelihood to seek treatment for symptoms across injury scenarios; however, the CDA revealed distinct symptom clusters that were characterized by the degree of risk for non-treatment and symptom type. We observed associations between injury risk factors and intentions of seeking medical treatment for the higher-risk indices. Results indicate that caregivers’ mental models of concussion symptoms are nuanced, not monolithic. Therefore, it is inaccurate to measure intentions to seek treatment for concussion without taking these nuances into consideration.