Use of Mental Health Services By Youths Who Have Sexually Offended
R. Gregg Dwyer and Jeanette M. Jerrell
Affiliation: Forensic Psychiatry Program and Sexual Behaviors Clinic and Lab, Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, 29-C Leinbach Drive, Charleston, SC 29407, USA.
Objectives: To examine differences in personal characteristics, types of services, and outcomes of care in a state mental health system among youths identified as having committed sex offenses.
Methods: A cohort of 188 youths identified with sexual offending behaviors served during one fiscal year was compiled and their existing personal, service, and functioning data from medical and computer files were used in retrospective comparisons employing analysis of variance, logistic regression, and random effects regression modeling.
Results: A diagnosis of conduct disorder or oppositional defiant disorder significantly distinguished between males and females in the cohort. The primary form of intervention was 24-hour residential or inpatient treatment, with males having longer lengths of stay than females. In community-based outpatient care, males received significantly more crisis services, group therapy, and medication monitoring. Child Global Assessment of Functioning (CGAF) ratings improved over time, but not to a statistically significant extent.
Conclusions: Given the potential for untreated adolescents with sexual offense histories becoming adult clients in mental health systems, evidence-based interventions should be employed as early as possible. This requires a matching of needs with interventions of proven efficacy, thus necessitating periodic reviews and monitoring of the demographics of the adolescents (and children) identified as having engaged in sexual offending behaviors and their outcomes (functional improvement) over time.
Keywords: Juveniles, sexual offending behaviors, treatment services, Child Global Assessment of Functioning, CGAF, sex offenses, retrospective, analysis of variance, logistic regression, random effects regression modeling
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