(PS4-14) Using Text Mining to Examine Differences in Problems Reported by Boys and Girls with ADHD

Abstract Text: Attention-Deficit/Hyperactivity Disorder (ADHD) presents differently across genders, with boys more frequently exhibiting hyperactive and impulsive behaviors and girls displaying inattentive symptoms (Stibbe et al., 2020). Research suggests that gender differences in ADHD symptoms may vary depending on the assessment method used (Slobodin & Davidovitch, 2019). Traditional ADHD assessments rely heavily on parent and teacher reports, while self-reports rely on individuals’ descriptions of experiences and symptoms (Haynes et al., 2009). Idiographic assessments focus on tailoring assessment methods to the specific needs and characteristics of an individual, but are underutilized in ADHD assessment practices (Haynes et al., 2009). This study leverages the Top Problems Assessment (TPA), an idiographic measure that allows youth and caregivers to report their most pressing concerns, to examine gender specific trends in youth with ADHD (Weisz et al., 2011). Practitioners use the TPA to match clients with a specific treatment focus (i.e., anxiety, conduct, depression, or trauma) and aid in collaborative decision-making between clinicians and families. This study used 1,042 youth (ages 3-18, 66% (N = 688) male) TPA responses diagnosed with ADHD from 2013 to 2024 from mental health service agencies providing evidence-based treatments (e.g., Modular Approach to Therapy for Children) to youth from underresourced communities. Word cloud analysis generated high-frequency words across male and female responses. Female clients most frequently used words like “feel”, “want”, and “school,” while male clients used “feel”, “angry”, and “trouble.” The word cloud results suggest that females with ADHD are more likely to express their problems in terms of personal desires and academic challenges, whereas males express more frustration and behavioral difficulties. This study will use text mining and sentiment analysis, utilizing computer algorithms to derive words and themes from textual data, to understand how youth with ADHD and their caregivers conceptualize top problems (Raja et al., 2008). Our findings highlight the potential of culturally and contextually responsive youth-driven idiographic assessments in capturing nuanced gender differences in ADHD-related challenges. Examining gender-specific trends in idiographic assessments for youth with ADHD can reveal important themes and language that allow for more affirming care. Future studies can use text mining analyses with the TPA to explore further individual, cultural, and community differences. Clinicians and researchers can use the TPA to see how gender may influence treatment needs, potentially leading to tailored and affirming interventions that address the unique challenges faced by youth with ADHD.