Background: Roughly 21% of youth in the United States meet criteria for a mental health diagnosis, but only one-fifth of those children obtain help. The Pediatric Behavioral Health Screen (PBHS) utilizes the Pediatric Symptom Checklist-17 (PSC-17) and functional impairment items to assess behavioral health concerns.

Methods: Data were obtained from a systematic chart review for children 6 to 16 years old. Descriptive analyses and a confirmatory factor analysis were used to evaluate the clinical performance and utility of the PBHS.

Results: A positive screen was endorsed for 26.7% of the sample, of whom 68% also experienced functional impairment. Clinicians appropriately administered the screen 73.5% of the time. The 3-factor model of the PSC-17 exhibited a good model fit.

Conclusions: Prevalence rates of psychosocial concerns and functional impairment affirm the need for routine behavioral health screening in the pediatric primary care setting. The PBHS exhibited good psychometric performance and clinical utility.

Download full-text PDF

Source
http://dx.doi.org/10.1177/0009922814527498DOI Listing

Publication Analysis

Top Keywords

behavioral health
16
functional impairment
16
pediatric behavioral
8
health screening
8
primary care
8
pediatric symptom
8
symptom checklist-17
8
impairment items
8
exhibited good
8
pediatric
5

Similar Publications

Objective: Asthma poses a significant health burden in South Asia, with increasing incidence and mortality despite a global decline in age-standardized prevalence rates. This study aims to analyze asthma trends from 1990 to 2021, focusing on prevalence, incidence, mortality, and disability-adjusted life years (DALYs) across South Asia. The study also assesses the impact of risk factors like high body mass index (BMI), smoking, and occupational exposures on asthma outcomes.

View Article and Find Full Text PDF

IntroductionAsthma attacks are set off by triggers such as pollutants from the environment, respiratory viruses, physical activity and allergens. The aim of this research is to create a machine learning model using data from mobile health technology to predict and appropriately warn a patient to avoid such triggers.MethodsLightweight machine learning models, XGBoost, Random Forest, and LightGBM were trained and tested on cleaned asthma data with a 70-30 train-test split.

View Article and Find Full Text PDF

Ovarian cancer (OC) ranks as the fifth leading cause of cancer-related deaths in the United States, posing a significant threat to female health. Late-stage diagnoses, driven by elusive symptoms often masquerading as gastrointestinal issues, contribute to a concerning 70% of cases being identified in advanced stages. While early-stage OC brags a 90% cure rate, progression involving pelvic organs or extending beyond the peritoneal cavity drastically diminishes it.

View Article and Find Full Text PDF

Sedentary lifestyles and prolonged physical inactivity are often linked to poor mental and physical health as well as an increased risk of a number of chronic illnesses, including cancer, obesity, type 2 diabetes, and cardiovascular problems. Metabolic Syndrome (MetS), as the new disease, has emerged as the world's leading cause of illness. Despite having its roots in the West, this issue has now completely globalized due to the development of the Western way of life throughout the world.

View Article and Find Full Text PDF

Behavioral management is essential to preventing recurrence after stroke, but its adherence is limited worldwide. We aimed to assess the impact of the behavior intervention based on the Recurrence risk perception and Behavioral decision Model for ischemic stroke patients' health behavior. This study was a single-blind, randomized, controlled trial with a 3-month follow-up.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!