Background: Nonaccidental trauma (NAT), or child abuse, is a leading cause of childhood injury and death in the US. Studies demonstrate that military-affiliated individuals are at greater risk of mental health complication and family violence, including child maltreatment. There is limited information about the outcomes of military children who experience NAT.
View Article and Find Full Text PDFPurpose: We explored the application of a machine learning algorithm for the timely detection of potential abusive head trauma (AHT) using the first free-text note of an encounter and demographic information.
Methods: First free-text physician notes and demographic information were collected for children under 5 years of age at a Level 1 Trauma Center. The control group, which included patients with head/neck injury, was compared to those with AHT diagnosed by the Child Protective Team.
School health programs are united by their desire to promote health and health-related outcomes among youth. They are also united by the fact that their expected effects are contingent on successful program implementation, which is often impeded by a multitude of real-world barriers. Techniques used in management science may help optimize school-based programs by accounting for implementation barriers.
View Article and Find Full Text PDFThis study investigated mental health indicators, substance use, and their relationships, by race/ethnicity. A probability sample of 1,053 students at two California universities self-reported their frequency of substance use and rated their experience with indicators of mental health. One-way analysis of variance (ANOVA), chi-square tests, and multivariate censored regression models were estimated to examine which indicators of mental health were associated with each substance use form by race/ethnicity.
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