Evaluating psychiatric hospital admission decisions for children in foster care: an optimal classification tree analysis.
This study explored clinical and nonclinical predictors of inpatient hospital admission decisions across a sample of children in foster care over 4 years (N = 13,245). Forty-eight percent of participants were female and the mean age was 13.4 (SD = 3.5 years). Optimal data analysis (Yarnold & Soltysik, 2005) was used to construct a nonlinear classification tree model for predicting admission decisions. As expected, clinical variables such as suicidality, psychoticism, and dangerousness predicted psychiatric admissions; however, several variables that are not direct indications of acute psychiatric distress, such as the presence of family problems and the location of the hospital screening, impacted decision making in a subsample of cases. Further analyses indicated that the model developed in Year 1 reliably and consistently predicted admission decisions (with 64%-68% overall accuracy) across the next 3 years. Policy, research, and clinical implications are discussed.
Snowden
Jessica A
JA
Department of Psychology, Loyola University Chicago, 6525 N. Sheridan, Chicago, IL 60626, USA. jsnowde@luc.edu
Leon
Scott C
SC
Bryant
Fred B
FB
Lyons
John S
JS
D000293:Adolescent; D002648:Child; D002675:Child, Preschool; D003657:Decision Making; D005260:Female; D005581:Foster Home Care; D006291:Health Policy; D006760:Hospitalization; D006778:Hospitals, Psychiatric; D006801:Humans; D008297:Male; D008403:Mass Screening / Q000379:methods; D001523:Mental Disorders / Q000175:diagnosis / Q000523:psychology / Q000534:rehabilitation*; D010343:Patient Admission; D012931:Social Environment; D011795:Surveys and Questionnaires