Protective + Risk Observations For Eliminating Sexual Offense Recidivism (PROFESOR): rationale for development and an initial investigation of reliability

References

• Ahrens, C. E., Stansell, J., & Jennings, A. (2010). To tell or not to tell: The impact of disclosure on sexual assault survivors’ recovery. Violence and Victims, 25(5), 631–648. https://doi.org/10.1891/0886-6708.25.5.631
   PubMed Web of Science ®Google Scholar
• Andrews, D. A., Bonta, J., & Hoge, R. D. (1990). Classification for effective rehabilitation: Rediscovering psychology. Criminal Justice and Behavior, 17(1), 19–52. https://doi.org/10.1177/0093854890017001004
   Web of Science ®Google Scholar
• Arnett, J. J. (2000). Emerging adulthood: A theory of development from the late teens through the twenties. American Psychologist, 55(5), 469–480. https://doi.org/10.1037/0003-066X.55.5.469
   PubMed Web of Science ®Google Scholar
• Association for the Treatment of Sexual Abusers (ATSA). (2017). Practice guidelines for assessment, treatment, and intervention with adolescents who have engaged in sexually abusive behavior. Author.
   Google Scholar
• Baker, A. J. L., Tabacoff, R., Tornusciolo, G., & Eisenstadt, M. (2001). Calculating number of offenses and victims of juvenile sexual offending: The role of posttreatment disclosures. Sexual Abuse, 13(2), 79–90. https://doi.org/10.1177/107906320101300202
   PubMedGoogle Scholar
• Barraclough, P., af Wåhlberg, A., Freeman, J., Watson, B., & Watson, A. (2016). Predicting crashes using traffic offences. A meta-analysis that examines potential bias between self-report and archival data. PLoS One, 11(4), e0153390. https://doi.org/10.1371/journal.pone.0153390
   PubMed Web of Science ®Google Scholar
• Beech, A. R., & Ward, T. (2004). The integration of etiology and risk in sexual offenders: A theoretical framework. Aggression and Violent Behavior, 10(1), 31–63. https://doi.org/10.1016/j.avb.2003.08.002
   Web of Science ®Google Scholar
• Blasko, B. L., Jeglic, E. L., & Mercado, C. C. (2011). Are actuarial risk data used to make determinations of sex offender risk classification?: An examination of sex offenders selected for enhanced registration and notification. International Journal of Offender Therapy and Comparative Criminology, 55(5), 676–692. https://doi.org/10.1177/0306624X10372784
   PubMed Web of Science ®Google Scholar
• Caldwell, M. F. (2016). Quantifying the decline in juvenile sexual recidivism rates. Psychology, Public Policy, and Law, 22(4), 414. https://doi.org/10.1037/law0000094
   Web of Science ®Google Scholar
• Carpentier, J., Leclerc, B., & Proulx, J. (2011). Juvenile sexual offenders: Correlates of onset, variety, and desistance of criminal behavior. Criminal Justice and Behavior, 38(8), 854–873. https://doi.org/10.1177/0093854811407730
   Web of Science ®Google Scholar
• Carter, G., Milner, A., McGill, K., Pirkis, J., Kapur, N., & Spittal, M. J. (2017). Predicting suicidal behaviours using clinical instruments: Systematic review and meta-analysis of positive predictive values for risk scales. The British Journal of Psychiatry, 210(6), 387–395. https://doi.org/10.1192/bjp.bp.116.182717
   PubMed Web of Science ®Google Scholar
• Cicchetti, D. V. (1994). Guidelines, criteria, and rules of thumb for evaluating normed and standardized assessment instruments in psychology. Psychological Assessment, 6(4), 284–290. http://doi.org/10.1037/1040-3590.6.4.284
   Google Scholar
• Craig, L. A., & Rettenberger, M. (2016). A brief history of sexual offender risk assessment. In D. Laws & W. O'Donohue (Eds.), Treatment of sex offenders (pp. 19–44). Springer. https://doi.org/10.1007/978-3-319-25868-3_2.
   Google Scholar
• de Vries Robbé, M., Mann, R. E., Maruna, S., & Thornton, D. (2015). An exploration of protective factors supporting desistance from sexual offending. Sexual Abuse, 27(1), 16–33. https://doi.org/10.1177/1079063214547582
   PubMed Web of Science ®Google Scholar
• Fazel, S., Singh, J. P., Doll, H., & Grann, M. (2012). Use of risk assessment instruments to predict violence and antisocial behaviour in 73 samples involving 24,827 people: Systematic review and meta-analysis. British Medical Journal, 345(jul24 2), e4692. https://doi.org/10.1136/bmj.e4692
   PubMedGoogle Scholar
• Feldt, L. S., Woodruff, D. J., & Salih, F. A. (1987). Statistical inference for coefficient alpha. Applied Psychological Measurement, 11(1), 93–103. https://doi.org/10.1177/014662168701100107
   Web of Science ®Google Scholar
• Fish, J. N., Baams, L., Wojciak, A. S., & Russell, S. T. (2019). Are sexual minority youth overrepresented in foster care, child welfare, and out-of-home placement? Findings from nationally representative data. Child Abuse & Neglect, 89, 203–211. http://dx.doi.org/10.1016/j.chiabu.2019.01.005
   PubMed Web of Science ®Google Scholar
• Griffin, H. L., Beech, A., Print, B., Bradshaw, H., & Quayle, J. (2008). The development and initial testing of the AIM2 framework to assess risk and strengths in young people who sexually offend. Journal of Sexual Aggression, 14(3), 211–225. http://dx.doi.org/10.1080/13552600802366593
   Google Scholar
• Hanson, R. K., & Bussière, M. T. (1998). Predicting relapse: A meta-analysis of sexual offender recidivism studies. Journal of Consulting and Clinical Psychology, 66(2), 348–362. https://doi.org/10.1037/0022-006X.66.2.348
   PubMed Web of Science ®Google Scholar
• Hanson, R. K., & Morton-Bourgon, K. E. (2009). The accuracy of recidivism risk assessments for sexual offenders: A meta-analysis of 118 prediction studies. Psychological Assessment, 21(1), 1–21. https://doi.org/10.1037/a0014421
   PubMed Web of Science ®Google Scholar
• Harris, A. J., Walfield, S. M., Shields, R. T., & Letourneau, E. J. (2016). Collateral consequences of juvenile sex offender registration and notification: Results from a survey of treatment providers. Sexual Abuse, 28(8), 770–790. https://doi.org/10.1177/1079063215574004
   PubMedGoogle Scholar
• Helmus, L., Thornton, D., Hanson, R. K., & Babchishin, K. M. (2012). Improving the predictive accuracy of Static-99 and Static-2002 with older sex offenders: Revised age weights. Sexual Abuse, 24(1), 64–101. http://doi.org/10.1177/1079063211409951
   PubMed Web of Science ®Google Scholar
• Helmus, L. M., Kelley, S. M., Frazier, A., Fernandez, Y. M., Lee, S. C., Rettenberger, M., & Boccaccini, M. T. (2022). Static-99R: Strengths, limitations, predictive accuracy meta-analysis, and legal admissibility review. Psychology, Public Policy, and Law, 28(3), 307–331. https://doi.org/10.1037/law0000351
   Web of Science ®Google Scholar
• Hoge, R. D., & Andrews, D. A. (2011). Youth level of service/case management inventory 2.0. Multihealth Systems.
   Google Scholar
• Jadhav, R., Achutan, C., Haynatzki, G., Rajaram, S., & Rautiainen, R. (2015). Risk factors for agricultural injury: A systematic review and meta-analysis. Journal of Agromedicine, 20(4), 434–449. https://doi.org/10.1080/1059924X.2015.1075450
   PubMed Web of Science ®Google Scholar
• Jensen, M., Askeland, I. R., & Bjørknes, R. (2022). Interrater reliability and experiences of assessment, intervention, and moving-on 3 assessment model in a multidisciplinary Norwegian sample. Frontiers in Psychology, 13, 1019739. http://doi.org/10.3389/fpsyg.2022.1019739
   PubMed Web of Science ®Google Scholar
• Kansagara, D., Englander, H., Salanitro, A., Kagen, D., Theobald, C., Freeman, M., & Kripalani, S. (2011). Risk prediction models for hospital readmission: A systematic review. JAMA, 306(15), 1688–1698. https://doi.org/10.1001/jama.2011.1515
   PubMed Web of Science ®Google Scholar
• Kim, K., Duwe, G., Tiry, E., Oglesby-Neal, A., Hu, C., Shields, R., Letourneau, E., & Caldwell, M. (2019). Development and validation of an actuarial risk assessment tool for juveniles with a history of sexual offending. National Criminal Justice Reference Service, Office of Justice Programs. https://www.ojp.gov/sites/g/files/xyckuh241/files/media/document/253444.pdf.
   Google Scholar
• King, C. K., & Rings, J. A. (2022). Adolescent sexting: Ethical and legal implications for psychologists. Ethics & Behavior, 32(6), 469–479. http://doi.org/10.1080/10508422.2021.1983818
   Web of Science ®Google Scholar
• Krause, C., Roth, A., Landolt, M. A., Bessler, C., & Aebi, M. (2021). Validity of risk assessment instruments among juveniles who sexually offended: Victim age matters. Sexual Abuse, 33(4), 379–405. http://dx.doi.org/10.1177/1079063220910719
   PubMed Web of Science ®Google Scholar
• Langton, C. M., Awrey, M. J., & Worling, J. R. (2023). Protective factors in the prediction of criminal outcomes for youth with sexual offenses using tools developed for adults and adolescents: Tests of direct effects and moderation of risk. Psychological Assessment, 35 (6), 497–509. https://doi.org/10.1037/pas0001227
   PubMed Web of Science ®Google Scholar
• Langton, C. M., Betteridge, M., & Worling, J. R. (2024). Promotive, mixed, and risk effects of individual items comprising the SAPROF assessment tool with justice-involved youth. Assessment, 31(2), 418–430. http://dx.doi.org/10.1177/10731911231163617
   PubMed Web of Science ®Google Scholar
• Langton, C. M., Ranjit, J. A., & Worling, J. R. (2023). A proof of concept study of promotive, mixed, and risk effects using the SAVRY assessment tool items with youth with sexual offenses. Psychological Assessment, 35(10), 856–867. https://doi.org/10.1037/pas0001272
   PubMed Web of Science ®Google Scholar
• Langton, C. M., & Worling, J. R. (2015). Introduction to the special issue on factors positively associated with desistance for adolescents and adults who have sexually offended. Sexual Abuse, 27(1), 3–15. https://doi.org/10.1177/1079063214568423
   PubMed Web of Science ®Google Scholar
• Leonard, M., & Hackett, S. (2019). The AIM3 assessment model: Assessment of adolescents and harmful sexual behaviour. AIM Project.
   Google Scholar
• Lyons, J. S. (2009). Communimetrics: A theory of measurement for human service enterprises. Springer.
   Google Scholar
• Mann, R. E., Hanson, R. K., & Thornton, D. (2010). Assessing risk for sexual recidivism: Some proposals on the nature of psychologically meaningful risk factors. Sexual Abuse, 22(2), 191–217. https://doi.org/10.1177/1079063210366039
   PubMed Web of Science ®Google Scholar
• McCann, K., & Lussier, P. (2008). Antisociality, sexual deviance, and sexual reoffending in juvenile sex offenders: A meta-analytical investigation. Youth Violence and Juvenile Justice, 6(4), 363–385. https://doi.org/10.1177/1541204008320260
   Web of Science ®Google Scholar
• McGrath, R. J., Cumming, G. F., Burchard, B. L., Zeoli, S., & Ellerby, L. (2010). Current practices and emerging trends in sexual abuser management: The Safer Society 2009 North American Survey. Safer Society Press.
   Google Scholar
• McGraw, K. O., & Wong, S. P. (1996). Forming inferences about some intraclass correlation coefficients. Psychological Methods, 1(1), 30–46. http://doi.org/10.1037/1082-989X.1.1.30
   Web of Science ®Google Scholar
• Olver, M. E., Stockdale, K. C., & Wormith, J. S. (2009). Risk assessment with young offenders: A meta-analysis of three assessment measures. Criminal Justice and Behavior, 36(4), 329–353. https://doi.org/10.1177/0093854809331457
   Web of Science ®Google Scholar
• Phenix, A., Fernandez, Y., Harris, A. J. R., Helmus, M., Hanson, R. K., & Thornton, D. (2016). Static-99R Coding Rules Revised-2016. static99.org.
   Google Scholar
• Prentky, R., & Righthand, S. (2003). Juvenile Sex Offender Assessment Protocol—II (J-SOAP-II): Manual. Unpublished document.
   Google Scholar
• Prentky, R. A., & Righthand, S.. (2021). The juvenile sex offender assessment protocol-II (JSOAP-II). In K. S. Douglas & R. K. Otto (Eds.), Handbook of violence risk assessment (2nd. ed., pp. 294–321). Routledge/Taylor Francis.
   Google Scholar
• Print, B., Morrison, T., & Henniker, J. (2001). An inter-agency assessment framework for young people who sexually abuse: Principles, processes and practicalities. In M. C. Calder (Ed.), Juveniles and children who sexually abuse: Frameworks for assessment (pp. 271–281). Russell House.
   Google Scholar
• Rasmussen, L. (2018). Comparing predictive validity of JSORRAT-II and MEGA♪ with sexually abusive youth in long-term residential custody. International Journal of Offender Therapy and Comparative Criminology, 62(10), 2937–2953. https://doi.org/10.1177/0306624X17726550
   PubMed Web of Science ®Google Scholar
• Rich, A., Brandes, K., Mullan, B., & Hagger, M. S. (2015). Theory of planned behavior and adherence in chronic illness: A meta-analysis. Journal of Behavioral Medicine, 38(4), 673–688. https://doi.org/10.1007/s10865-015-9644-3
   PubMed Web of Science ®Google Scholar
• Rojas, E. Y., & Olver, M. E. (2019). Validity and reliability of the violence risk scale–youth sexual offense version. Sexual Abuse, 32(7), 826–849. https://doi.org/10.1177/1079063219858064
   PubMed Web of Science ®Google Scholar
• Schwalbe, C. S. (2007). Risk assessment for juvenile justice: A meta-analysis. Law and Human Behavior, 31(5), 449–462. https://doi.org/10.1007/s10979-006-9071-7
   PubMed Web of Science ®Google Scholar
• Thornton, D. (2013). Implications of our developing understanding of risk and protective factors in the treatment of adult male sexual offenders. International Journal of Behavioral Consultation and Therapy, 8(3–4), 62–65. http://doi.org/10.1037/h0100985
   Google Scholar
• van den Berg, J. W., Smid, W., Schepers, K., Wever, E., van Beek, D., Janssen, E., & Gijs, L. (2018). The predictive properties of dynamic sex offender risk assessment instruments: A meta-analysis. Psychological Assessment, 30(2), 179–191. https://doi.org/10.1037/pas0000454
   PubMed Web of Science ®Google Scholar
• van der Put, C. E., & Asscher, J. J. (2015). Protective factors in male adolescents with a history of sexual and/or violent offending: A comparison between three subgroups. Sexual Abuse, 27(1), 109–126. https://doi.org/10.1177/1079063214549259
   PubMed Web of Science ®Google Scholar
• van der Put, C. E., Assink, M., & Boekhout van Solinge, N. F. (2017). Predicting child maltreatment: A meta-analysis of the predictive validity of risk assessment instruments. Child Abuse & Neglect, 73, 71–88. https://doi.org/10.1016/j.chiabu.2017.09.016
   PubMed Web of Science ®Google Scholar
• van der Put, C. E., Gubbels, J., & Assink, M. (2019). Predicting domestic violence: A meta-analysis on the predictive validity of risk assessment tools. Aggression and Violent Behavior, 47, 100–116. https://doi.org/10.1016/j.avb.2019.03.008
   Web of Science ®Google Scholar
• Viljoen, J. L., Mordell, S., & Beneteau, J. L. (2012). Prediction of adolescent sexual reoffending: A meta-analysis of the J-SOAP-II, ERASOR, J-SORRAT-II, and Static-99. Law and Human Behavior, 36(5), 423–438. https://doi.org/10.1037/h0093938
   PubMed Web of Science ®Google Scholar
• Vivar, L. A., Muñoz, M. S., Cárcamo, Y. B., & Arenas, R. P.-L. (2021). Factores protectores en adolescentes que han desarrollado conductas sexuales abusivas: Propiedades psicométricas del DASH-13 en Chile [Protective factors in adolescents who have engaged in abusive sexual behaviors: Psychometric properties of DASH-13 in Chile]. Revista Señales, 04, 60–75. https://www.sename.cl/web/wp-content/uploads/2022/01/A4-Revista_Se%C3%B1ales_web_n%C2%BA25_25_01.pdf.
   Google Scholar
• Williams, D. J., Thomas, J. N., & Prior, E. E. (2015). Moving full-speed ahead in the wrong direction? A critical examination of US sex-offender policy from a positive sexuality model. Critical Criminology, 23(3), 277–294. https://doi.org/10.1007/s10612-015-9270-y
   Web of Science ®Google Scholar
• Worling, J. R. (2013). Desistance for Adolescents who Sexual Harm (DASH-13). Unpublished document. www.drjamesworling.com.
   Google Scholar
• Worling, J. R. (2017). Protective + Risk Observations For Eliminating Sexual Offense Recidivism (PROFESOR). Unpublished document. www.profesor.ca.
   Google Scholar
• Worling, J. R., & Curwen, T. (2001). Estimate of Risk of Adolescent Sexual Offense Recidivism (ERASOR; Version 2.0). In M. C. Calder (Ed.), Juveniles and children who sexually abuse: Frameworks for assessment (pp. 372–397). Russell House.
   Google Scholar
• Worling, J. R., & Långström, N. (2003). Assessment of criminal recidivism risk with adolescents who have offended sexually: A review. Trauma, Violence, & Abuse, 4(4), 341–362. https://doi.org/10.1177/1524838003256562
   PubMedGoogle Scholar
• Worling, J. R., & Langton, C. M. (2022). Factors related to desistance from sexual recidivism. In C. M. Langton & J. R. Worling (Eds.), Facilitating desistance from aggression and crime: Theory, research, and strength-based practices (pp. 211–229). John Wiley & Sons. https://doi.org/10.1002/9781119166504.ch8.
   Google Scholar
• Zeng, G., Chu, C. M., & Lee, Y. (2015). Assessing protective factors of youth who sexually offended in Singapore: Preliminary evidence on the utility of the DASH-13 and the SAPROF. Sexual Abuse, 27(1), 91–108. https://doi.org/10.1177/1079063214561684
   PubMed Web of Science ®Google Scholar