Enhancing the Identification of Commercial Sexual Exploitation Among a Population of High-Risk Youths Using Predictive Regularization Models. (August 2020)

• Record Type: Journal Article
• Authors: de Vries, Ieke; Kafafian, Matthew; Goggin, Kelly; Bouchard, Elizabeth; Goldfarb, Susan; Farrell, Amy
• Is Part Of: Child maltreatment. Volume 25:Number 3(2020)
• Page Start: 318
• Page End: 327
• Publication Date: 2020-08
• Subjects: methodology -- child abuse -- child maltreatment -- child welfare -- services/child protection -- risk assessment; Child abuse -- Periodicals; Abused children -- Periodicals; 362.76
• DOI: 10.1177/1077559519889178
• Languages: English
• ISSNs: 1077-5595

Abstract

Despite an increasing awareness about the existence and harms of commercial sexual exploitation of children (CSEC), the identification of victims remains a challenge for practitioners, hindering their ability to provide appropriate services. Tools that gauge risk of CSEC support the identification of victims but are underdeveloped because most tools assess risk of CSEC within a general youth population. An understanding of what predicts actual CSEC victimizations among youths at higher risk of CSEC due to experiences of childhood adversities has been left unassessed. Research in this area is limited in part because traditional methods do not allow for an assessment of the unique impact of childhood adversities that tend to co-occur. To address these difficulties, the current study applied predictive regularization methods to identify the most decisive risk items for CSEC. Proximal risk of CSEC was assessed among 317 youths who were referred to a specialized program in the Northeast of the United States due to suspicion of CSEC. With an innovative methodological approach, this study seeks to prompt other scholars to examine risk utilizing novel techniques and provides a foundation for the development of concise tools that assess risk of CSEC among populations of youths at higher levels of risk.