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School-based day treatment is an intensive mental health service for children with social, emotional, and behavioral difficulties. Research on day treatment is scarce and descriptions of program models are lacking. We used stimulated recall interviews to explore the moment-to-moment processes and strategies of classroom staff in a day treatment program for children in kindergarten and Grade 1. Several processes and strategies used by staff emerged from the thematic analysis of the interviews. These included a process of individualized intervention, characterized by a continual and cyclical process of attunement, responsiveness, assessment, and evaluation, using a team-based approach, noticing positives about children, a climate of positive relationships, staff regulating their own emotions, being flexible while also being firm and consistent, and seeing children from a developmental perspective. More specific strategies used by staff (e.g., token economy) also emerged from the interviews. Implications for future research and teacher training are discussed.

Robots are increasingly entering the social sphere and taking on more sophisticated roles. One application for which robots are already being deployed is in civilian security tasks, in which robots augment security and police forces. In this domain, robots will encounter individuals in crisis who may pose a threat to themselves, others, or personal property. In such interactions with human police and security officers, a key goal is to de-escalate the situation to resolve the interaction. This paper considers the task of utilizing mobile robots in de-escalation tasks, using the mechanisms developed for de-escalation in human–human interactions. What strategies should a robot follow in order to leverage existing de-escalation approaches? Given these strategies, what sensing and interaction capabilities should a robot be capable of in order to engage in de-escalation tasks with humans? First, we discuss the current understanding of de-escalation with individuals in crisis and present a working model of the de-escalation process and strategies. Next, we review the capabilities that an autonomous agent should demonstrate to be able to apply such strategies in robot-mediated crisis de-escalation. Finally, we explore data-driven approaches to training robots in de-escalation and the next steps in moving the field forward.

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