Mixed-Method Examination of Latinx Teachers' Perceptions of Daily Behavioral Report Card Interventions to Support Students with ADHD.

Daily behavioral report cards (DRC) are an efficacious intervention for children with ADHD, yet there is little information on Latinx teachers' perceptions about ADHD and preferences related to behavioral treatment. The purpose of the current study was to examine the feasibility and acceptability of behavioral consultation with Latinx teachers and students, with a particular focus on the DRC. Participants (n = 23) included elementary school teachers (100% Hispanic/Latinx, 96% female) working with predominantly Hispanic/Latinx students. We leveraged a convergent, mixed-method design to evaluate feasibility, acceptability, as well as several potentially associated factors (i.e., perceptual, practical/logistical, individual, and cultural factors). Quantitative and qualitative measures and analyses were guided by the Consolidated Framework for Intervention Research. We found that Latinx teachers' Daily Report Card (DRC) completion rates (80%) were comparable to previous studies with predominantly non-Latinx white teachers and students. Quantitative indicators of acceptability were also similar to the prior literature. Few variables were associated with DRC completion rates, with the exception of teacher self-report of stress and satisfaction, which were both positively associated with completion rates. Qualitative findings expanded quantitative trends; thematic analyses revealed two overarching themes, that (1) teachers' attitudes toward behavioral interventions matter a great deal, and that (2) teachers' perceived behavioral control over DRC implementation depends a lot on the environment. Findings highlight the importance of stakeholders' perspectives, including teachers, in translating research to practice in real world settings.

Leveraging Machine Learning to Identify Predictors of Receiving Psychosocial Treatment for Attention Deficit/Hyperactivity Disorder.

This study aimed to identify factors associated with receiving psychosocial treatment for ADHD in a nationally representative sample. Participants were 6630 youth with a parent-reported diagnosis of ADHD from the 2016-2017 National Survey of Children's Health. Machine learning analyses were performed to identify factors associated with receipt of psychosocial treatment for ADHD. We examined potentially associated factors in the broad categories of variables hypothesized to affect problem recognition (e.g., severity, mental health comorbidities); the decision to seek treatment; service selection (e.g., insurance coverage) and service use. We found that three machine learning models unanimously identified parent-reported ADHD severity (mild vs. moderate/severe) as the factor that best distinguishes between children who receive psychosocial treatment for ADHD and those who do not. Receive operating characteristic curve analysis revealed the following model performance: classification and regression tree analysis (area under the curve; AUC = .68); an ensemble model (AUC = .71); and a deep, multi-layer neural network (AUC = .72), as well as comparison to a logistic regression model (AUC = .69). Further, insurance coverage of mental/behavioral health needs emerged as a salient factor associated with the receipt of psychosocial treatment. Machine learning models identified risk and protective factors that predicted the receipt of psychosocial treatment for ADHD, such as ADHD severity and health insurance coverage.