Pediatric mental health is a growing concern around the world, affecting children’s social-emotional development and increasing the risk of poor behavioral outcomes later in life. However, obtaining a behavioral diagnosis in early childhood is challenging due to lack of access to resources, low parental mental health literacy, and children’s dependence on several stakeholders to coordinate care for them. While app-based, at-home screening tools could offer a scalable and convenient diagnostic solution for families, stakeholder perspectives on their utility and usability remain to be examined. This work reports on a survey of child mental health practitioners and interviews with parents to illustrate existing barriers to care that stakeholders encounter, the perceived benefits of app-based screening tools in meeting their needs, and the challenges in scaling up these tools. We identify where stakeholders agree or disagree, delineate key design tensions, and provide recommendations for the development of future screening technologies.

The diagnosis of psychological disorders in early childhood is of utmost importance given their severe impact on children's academic and social skills as well as general adaptive functioning. Wearable and video-based systems have the potential to collect important diagnostic information in the form of neurophysiological and behavioral signals. However, accurate prediction of psychological disorder status from multimodal data streams necessitates their combination into meaningful features for classification models. In this work, we present a multitask supervised contrastive learning approach to learn useful multimodal embeddings from functional Near-Infrared Spectroscopy, galvanic skin response, and facial video data collected during a frustration-inducing task. The generated embeddings are able to accurately infer emotion regulation-related psychological disorders with an F1 score of 0.91, having significant implications for early-childhood mental health diagnoses.