Restricted accessResearch articleFirst published online 2022-8
Predicting Risk of Reoffending in Persons with Child Sexual Exploitation Material Offense Histories: The Use of Child Pornography Offender Risk Tool in a Scottish Population
In the past decade, there has been an increase in child sexual exploitation material (CSEM) offenses and convictions. Although research shows that individuals with CSEM offence histories generally are at low risk of reoffending, certain factors do increase in CSEM convictions, in order to assist with case prioritization, management and supervision, risk assessment is helpful across agencies. The Child Pornography Offender Risk Tool (CPORT) was created specifically for this population and shows significant predictive validity for various outcomes. This study aimed to validate the use of the CPORT in a Scottish sample of 141 adult males who were convicted of CSEM offenses. Receiver Operating Characteristic (ROC) and logistic regression analyses indicated that the CPORT significantly predicted any recidivism (Area Under the Curve = .81), any sexual recidivism (AUC = .78) and CSEM recidivism (AUC = .74), suggesting that it is a valid risk assessment tool for Scottish populations. Recommendations for further research and clinical implications are discussed.
BabchishinK. M.HansonR. K.VanZuylenH. (2015). Online child pornography offenders are different: A meta-analysis of the characteristics of online and offline sex offenders against children. Archives of Sexual Behavior, 44(1), 45-66. https://doi.org/10.1007/s10508-014-0270-x
2.
BabchishinK. M.MerdianH. L.BartelsR. M.PerkinsD. (2018). Child sexual exploitation materials offenders. European Psychologist, 23(2), 130-143. https://doi.org/10.1027/1016-9040/a000326
3.
BarnettG. D.WakelingH. C.HowardP. D. (2010). An examination of the predictive validity of the Risk Matrix 2000 in England and Wales. Sexual Abuse: A Journal of Research and Treatment, 22(4), 443-470. https://doi.org/10.1177/1079063210384274.
BlackC. (2018). Predicting recidivism among an adult male child sexual abuse imagery offender population with the child pornography offender risk tool short version (CPORT-SV): A New Zealand validation study. [Unpublished master’s thesis. University of Canterbury].
6.
BrankleyA. E.BabchishinK. M.HansonR. K. (2019). STABLE-2007 Demonstrates predictive and incremental validity in assessing risk-relevant propensities for sexual offending: A meta-analysis. Sexual Abuse, 33, 34-62. https://doi.org/10.1177/1079063219871572
7.
CicchettiD. V. (1994). Guidelines, criteria, and rules of thumb for evaluating normed and standardized assessment instruments in psychology. Psychological Assessment, 6(4), 284-290. https://doi.org/10.1037/1040-3590.6.4.284
8.
DalinsJ.WilsonC.CarmanM. (2018). Criminal motivation on the dark web: A categorisation model for law enforcement. Digital Investigation, 24, 62-71. https://doi.org/10.1016/j.diin.2017.12.003
EkeA. W.HelmusL. M.SetoM. C. (2019). A validation study of the child pornography offender risk tool (CPORT). Sexual Abuse, 31(4), 456-476. https://doi.org/10.1177/1079063218762434.
12.
EkeA. W.SetoM. C.WilliamsJ. (2011). Examining the criminal history and future offending of child pornography offenders: An extended prospective follow-up study. Law and Human Behavior, 35(6), 466-478. https://doi.org/10.1007/s10979-010-9252-2
13.
ElliottI. A.Mandeville-NordenR.Rakestrow-DickensJ.BeechA. R. (2019). Reoffending rates in a U.K. community sample of individuals with convictions for indecent images of children. Law and Human Behavior, 43(4), 369-382. https://doi.org/10.1037/lhb0000328
14.
EndrassJ.UrbaniokF.HammermeisterL. C.BenzC.ElbertT.LaubacherA.RosseggerA. (2009). The consumption of Internet child pornography and violent and sex offending. BMC Psychiatry, 9, 43-7. https://doi.org/10.1186/1471-244X-9-43.
15.
GarringtonC.ChamberlainP.RickwoodD.BoerD. P. (2018). Risk assessment of online child abuse material (CAM) offenders: a review of existing tools. Journal of Criminal Psychology, 8(2), 150-161. https://doi.org/10.1108/JCP-05-2017-0022
16.
GreijerS.DoekJ.The Interagency Working Group. (2016). Terminology guidelines for the protection of children from sexual exploitation and sexual abuse. ECPAT International. http://luxembourgguidelines.org/english-version/.
HansonR. K.BussièreM. T. (1998). Predicting relapse: A meta-analysis of sexual offender recidivism studies. Journal of Consulting and Clinical Psychology, 66(2), 348-362.
20.
HirschtrittMETuckerDBinderRL (2019). Risk assessment of online child sexual exploitation offenders. The Journal of the American Academy of Psychiatry and the Law, 47(2), 155-164. https://doi.org/10.29158/JAAPL.003830-19
21.
IBMCorp (2016). IBM SPSS statistics for windows: IBM Corp[Computer software]. (Version 24.0).
22.
LandisJ. R.KochG. G. (1977). The measurement of observer agreement for categorical data published by : International biometric society stable. Biometrics, 33(1), 159-174. https://doi.org/10.2307/2529310
23.
LongM.AlisonL.TejeiroR.HendricksE.GilesS. (2016). KIRAT: Law enforcement's prioritization tool for investigating indecent image offenders. Psychology, Public Policy, and Law, 22(1), 12-21. https://doi.org/10.1037/law0000069.
PilonA. J. M. (2016). The predictive validity of general and offence-specific risk assessment tools for child pornography offenders’ reoffending. [Unpublished master’s thesis. Univeristy of Saskatchewan].
26.
QuayleE.CooperK. (2015). The role of child sexual abuse images in coercive and non-coercive relationships with adolescents: A thematic review of the literature. Child & Youth Services, 36(4), 312-328. https://doi.org/10.1080/0145935X.2015.1092840
27.
RiceM. E.HarrisG. T. (2005). Comparing effect sizes in follow-up studies: ROC Area, Cohen’s d, and r. Law and Human Behavior, 29(5), 615-620. https://doi.org/10.1007/s10979-005-6832-7
28.
RiceM. E.HarrisG. T.LangC.CormierC. (2006). Violent sex offenses: How are they best measured from official records?. Law and Human Behavior, 30(4), 525-541. https://doi.org/10.1007/s10979-006-9022-3
ScurichN.JohnR. S. (2019). The dark figure of sexual recidivism. Behavioral Sciences & the Law, 37(2), 158-175. https://doi.org/10.1002/bsl.2400
31.
SetoM. C.EkeA. W. (2015). Predicting recidivism among adult male child pornography offenders: Development of the child pornography offender risk tool (CPORT). Law and Human Behavior, 39(4), 416-429. https://doi.org/10.1037/lhb0000128
32.
SetoM. C.EkeA. W. (2017). Correlates of admitted sexual interest in children among individuals convicted of child pornography offenses. Law and Human Behavior, 41(3), 305-313. https://doi.org/10.1037/lhb0000240
33.
SetoM. C.Karl HansonR.BabchishinK. M. (2011). Contact sexual offending by men with online sexual offenses. Sexual Abuse, 23(1), 124-145. https://doi.org/10.1177/1079063210369013
34.
SoldinoV.Carbonell-VayáE. J.Seigfried-SpellarK. C. (2020). Spanish validation of the child pornography offender risk tool. Sexual Abuse, 1-26. https://doi.org/10.1177/1079063220928958
35.
SteelC. M. S.NewmanE.O’RourkeS.QuayleE. (2020). An integrative review of historical technology and countermeasure usage trends in online child sexual exploitation material offenders. Forensic Science International: Digital Investigation, 33, 1-17. https://doi.org/10.1016/j.fsidi.2020.300971
36.
TaylorM.HollandG.QuayleE. (2001). Typology of Paedophile Picture Collections. The Police Journal: Theory, Practice and Principles, 74(2), 97-107. https://doi.org/10.1177/0032258x0107400202
VergouweY.SteyerbergE. W.EijkemansM. J. C.HabbemaJ. D. F. (2005). Substantial effective sample sizes were required for external validation studies of predictive logistic regression models. Journal of Clinical Epidemiology, 58(5), 475-483. https://doi.org/10.1016/j.jclinepi.2004.06.017
40.
WagerN.ArmitageR.ChristmannK.GallagherB.IoannouM.ParkinsonS.ReevesC.RogersonM.SynnottJ. (2018). RAPID Evidence Assessment : Quantifying The Extent Of Online-Facilitated Child Sexual ABUSE : Report for the Independent Inquiry into Child Sexual Abuse. HuddersfieldThe Extent Of Online Facilitated Child Sexual Abuse.pdf. Evidence Assessment Quantifying.
41.
WakelingH. C.HowardP.BarnettG. (2011). Comparing the validity of the RM2000 scales and OGRS3 for predicting recidivism by internet sexual offenders. Sexual Abuse, 23(1), 146-168. https://doi.org/10.1177/1079063210375974
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
