Effects of an enhanced choice model of skill-based treatment for students with emotional/behavioral disorders.

The enhanced choice model of skill-based treatment (ECM-SBT; Rajaraman et al., 2021) is a package of behavioral treatment procedures with modifications designed to reduce risks associated with extinction of problem behavior. The skill-based treatment component of this package (Hanley et al., 2014) has been investigated thoroughly in clinical settings, though fewer studies have been conducted in public schools. In this investigation, we systematically replicated Rajaraman et al.'s (2021) demonstration of the ECM-SBT with 3 children enrolled in a public special day school for students with emotional and behavioral disorders. Intervention procedures were associated with increases in targeted alternative responses (i.e., communicative response, tolerance response, and cooperation with instructions) and decreased precursor behavior relative to baseline. Severe problem behavior was rare in both assessment and treatment. Participants chose to spend most appointment time participating in ECM-SBT, indicating preference for treatment procedures over alternative contexts (i.e., free access to a break area with preferred activities; regular classroom instruction). These outcomes suggest ECM-SBT has promise for safely teaching alternatives to problem behavior to children with emotional and behavioral disorders in school settings.

Minimizing Escalation by Treating Dangerous Problem Behavior Within an Enhanced Choice Model.

To address dangerous problem behavior exhibited by children while explicitly avoiding physical management procedures, we systematically replicated and extended the skill-based treatment procedures described by Hanley, Jin, Vanselow, and Hanratty (2014) by incorporating an enhanced choice model with three children in an outpatient clinic and two in a specialized public school. In this model, several tactics were simultaneously added to the skill-based treatment package to minimize escalation to dangerous behavior, the most notable of which involved offering children multiple choice-making opportunities, including the ongoing options to (a) participate in treatment involving differential reinforcement, (b) "hang out" with noncontingent access to putative reinforcers, or (c) leave the therapeutic space altogether. Children overwhelmingly chose to participate in treatment, which resulted in the elimination of problem behavior and the acquisition and maintenance of adaptive skills during lengthy, challenging periods of nonreinforcement. Implications for the safe implementation of socially valid treatments for problem behavior are discussed.

A Predictive Multimodal Framework to Alert Caregivers of Problem Behaviors for Children with ASD (PreMAC).

Autism Spectrum Disorder (ASD) impacts 1 in 54 children in the US. Two-thirds of children with ASD display problem behavior. If a caregiver can predict that a child is likely to engage in problem behavior, they may be able to take action to minimize that risk. Although experts in Applied Behavior Analysis can offer caregivers recognition and remediation strategies, there are limitations to the extent to which human prediction of problem behavior is possible without the assistance of technology. In this paper, we propose a machine learning-based predictive framework, PreMAC, that uses multimodal signals from precursors of problem behaviors to alert caregivers of impending problem behavior for children with ASD. A multimodal data capture platform, M2P3, was designed to collect multimodal training data for PreMAC. The development of PreMAC integrated a rapid functional analysis, the interview-informed synthesized contingency analysis (IISCA), for collection of training data. A feasibility study with seven 4 to 15-year-old children with ASD was conducted to investigate the tolerability and feasibility of the M2P3 platform and the accuracy of PreMAC. Results indicate that the M2P3 platform was well tolerated by the children and PreMAC could predict precursors of problem behaviors with high prediction accuracies.

Outcome summaries of latency-based functional analyses conducted in hospital inpatient units.

Latency-based functional analysis (FA) may be a viable alternative to the standard, rate-based, FA when frequently evoking problem behavior is not advisable. We conducted 18 latency-based FAs of the problem behavior of children diagnosed with autism in inpatient hospital settings and identified functional relations during 44.4% (8 of 18) of latency-based FAs. Implications for conducting FAs of severe problem behavior are discussed.