title: Gender differences in patterns of risk factors among children receiving mental health services: latent class analyses.
abstract: Latent class analyses were used to analyze data from a sample of children participating in the national evaluation of the Comprehensive Communities Mental Health Services for Children and Their Families Program (N = 6786). Lifetime risk experiences of the child were analyzed to identify 4 classes of boys and girls with similar risk patterns. While low-risk, status-offense, abuse, and high-risk classes were identified for both boys and girls, there were nearly half the number of girls in the low-risk class, almost as many in the status-offense class, twice as many in the abuse class, and more than 3 three times as many in the high-risk class as there were boys. These findings suggest that there are specific groups of children entering services who differ as a function of their lifetime risk exposure. In addition, the relationship between class membership and child functioning, and class membership and family lifetime risk experiences. Understanding these differences provides critical information to the service planning process. In addition, it may result in immediate improvement in the triage of children into services and a better understanding of their behaviors during and after treatment.
authors.affiliation: ORC Macro, 116 John St, Suite 800, New York, NY 10038, USA. Christine.M.Walrath@orcmacro.com
authors.forename: Christine M
authors.initials: CM
authors.lastname: Walrath
authors.forename: Hanno
authors.initials: H
authors.lastname: Petras
authors.forename: David S
authors.initials: DS
authors.lastname: Mandell
authors.forename: Robert L
authors.initials: RL
authors.lastname: Stephens
authors.forename: E Wayne
authors.initials: EW
authors.lastname: Holden
authors.forename: Philip J
authors.initials: PJ
authors.lastname: Leaf
mesh_terms: D002648:Child; D002675:Child, Preschool; D005260:Female; D006801:Humans; D008297:Male; D008605:Mental Health Services / Q000706:statistics & numerical data; D012307:Risk Factors; D012737:Sex Factors; D014481:United States