5 research outputs found

    Using computational models of learning to advance cognitive behavioral therapy

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    Many psychotherapy interventions have a large evidence base and can help a substantial number of people with psychiatric symptoms. However, we still have little understanding of why treatments work. Early advances in psychotherapy, such as the development of exposure therapy, built on theoretical and experimental evidence from Pavlovian and instrumental conditioning. More generally, all psychotherapy achieves change through learning. The past 25 years have seen substantial developments in computational models of learning, with increased computational precision and a focus on multiple learning mechanisms and their interaction. Now might be a good time to formalize psychotherapy interventions as computational models of learning to improve our understanding of mechanisms of change in psychotherapy. To advance research and help bring together a new joint field of theory-driven computational psychotherapy, we first review literature on cognitive behavioral therapy (exposure therapy and cognitive restructuring) and introduce computational models of reinforcement learning and representation learning. We then suggest a mapping of these learning algorithms on change processes presumably underlying the effects of exposure therapy and cognitive restructuring. Finally, we outline how the understanding of interventions through the lens of learning algorithms can inform intervention research

    Pediatric psychiatric emergency rooms during COVID-19: a multi-center study

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    Abstract Background The COVID-19 (SARS-CoV-2) pandemic has been a major stressor for the mental health and well-being of children and adolescents. Surveys and reports from hotlines indicate a significant rise in mental health problems. As the psychiatric emergency room (ER) is a first-line free-of-charge facility for psychiatric emergencies, we expected to see a significant increase in visits, specifically of new patients suffering from anxiety, depression, or stress-related disorders. Methods Data from two psychiatric hospital ERs and one general hospital were included. All visits of children and adolescents from the computerized files between March and December of 2019 were analyzed anonymously and compared to the same months in 2020, using multilevel linear modeling. Results There was a significant decline in the total number of visits (p = .017), specifically among those diagnosed as suffering from stress-related, anxiety, and mood disorder groups (p = .017), and an incline in the proportion of visits of severe mental disorders (p = .029). Discussion The limited use of child and adolescent psychiatric emergency facilities during the pandemic highlights the importance of tele-psychiatry as part of emergency services. It also suggests the importance of the timeline of the emergence of clinically relevant new psychiatric diagnoses related to the pandemic. Future studies are needed to establish the long-term effects of the pandemic and the expeditious use of tele-psychiatry
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