Mental health disorders affect 1 in 5 children; however, the majority of affected children do not receive appropriate services, leading to adverse adult outcomes. To meet the needs of children, pediatricians need to take on a larger role in addressing mental health problems. The accompanying policy statement, "Mental Health Competencies for Pediatric Practice," articulates mental health competencies pediatricians could achieve to improve the mental health care of children; yet, the majority of pediatricians do not feel prepared to do so. In this technical report, we summarize current initiatives and resources that exist for trainees and practicing pediatricians across the training continuum. We also identify gaps in mental health clinical experience and training and suggest areas in which education can be strengthened. With this report, we aim to stimulate efforts to address gaps by summarizing educational strategies that have been applied and could be applied to undergraduate medical education, residency and fellowship training, continuing medical education, maintenance of certification, and practice quality improvement activities to achieve the pediatric mental health competencies. In this report, we also articulate the research questions important to the future of pediatric mental health training and practice.
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http://dx.doi.org/10.1542/peds.2019-2758 | DOI Listing |
J Neural Transm (Vienna)
January 2025
Laboratory of Biological Psychiatry, Institute of Mental Health, Tianjin Anding Hospital, Mental Health Center of Tianjin Medical University, 13 Liulin Road, Tianjin, 300222, China.
Bipolar disorder (BD) frequently coexists with anxiety disorders, creating complex challenges in clinical therapy and management. This study investigates the prevalence, prognostic implications, and treatment strategies for comorbid BD and anxiety disorders. High comorbidity rates, particularly with generalized anxiety disorder, underscore the necessity of thorough clinical assessments to guide effective management.
View Article and Find Full Text PDFJ Immigr Minor Health
January 2025
Watson Institute for International and Public Affairs and the School of Public Health, Brown University, Providence, USA.
Eur J Hum Genet
January 2025
Institute of Bioinformatics, International Technology Park, Bangalore, 560066, India.
Mitochondrial membrane protein-associated neurodegeneration (MPAN) is a rare neurodegenerative disorder characterized by spastic paraplegia, parkinsonism and psychiatric and/or behavioral symptoms caused by variants in gene encoding chromosome-19 open reading frame-12 (C19orf12). We present here seven patients from six unrelated families with detailed clinical, radiological, and genetic investigations. Childhood-onset patients predominantly had a spastic ataxic phenotype with optic atrophy, while adult-onset patients were presented with cognitive, behavioral, and parkinsonian symptoms.
View Article and Find Full Text PDFJ Occup Rehabil
January 2025
Faculty of Health Sciences, Curtin School of Allied Health, Curtin University, Perth, WA, Australia.
Purpose: Workers' compensation claims can negatively affect the wellbeing of injured workers. For some, these negative effects continue beyond finalisation of the workers' compensation claim. It is unclear what factors influence wellbeing following finalisation of a workers' compensation claim.
View Article and Find Full Text PDFCommun Psychol
January 2025
Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
How do people model the world's dynamics to guide mental simulation and evaluate choices? One prominent approach, the Successor Representation (SR), takes advantage of temporal abstraction of future states: by aggregating trajectory predictions over multiple timesteps, the brain can avoid the costs of iterative, multi-step mental simulation. Human behavior broadly shows signatures of such temporal abstraction, but finer-grained characterization of individuals' strategies and their dynamic adjustment remains an open question. We developed a task to measure SR usage during dynamic, trial-by-trial learning.
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