Hostname: page-component-78c5997874-s2hrs Total loading time: 0 Render date: 2024-11-16T13:26:54.226Z Has data issue: false hasContentIssue false
  • English
  • Français

The evolution of violence risk assessment

Published online by Cambridge University Press:  28 March 2014

John Monahan  and
Jennifer L. Skeem
John Monahan*
Affiliation:
School of Law, University of Virginia, Charlottesville, Virginia, USA
Jennifer L. Skeem
Affiliation:
School of Social Welfare & Goldman School of Public Policy, University of California–Berkeley, Berkeley, California, USA
*
*Address for correspondence: John Monahan, School of Law, University of Virginia, 580 Massie Road, Charlottesville, VA 22903-1738, USA. (Email: [email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

Many instruments have been published in recent years to improve the ability of mental health clinicians to estimate the likelihood that an individual will behave violently toward others. Increasingly, these instruments are being applied in response to laws that require specialized risk assessments. In this review, we present a framework that goes beyond the “clinical” and “actuarial” dichotomy to describe a continuum of structured approaches to risk assessment. Despite differences among them, there is little evidence that one instrument predicts violence better than another. We believe that these group-based instruments are useful for assessing an individual's risk, and that the instrument should be chosen based on the purpose of the assessment.

Keywords

Risk assessmentviolencedangerousness
Type
Review Articles
Information
CNS Spectrums , Volume 19 , Issue 5 , October 2014 , pp. 419 - 424
Copyright
Copyright © Cambridge University Press 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Kraemer, H, Kazdin, A, Offord, D, etal. Coming to terms with the terms of risk. Arch Gen Psychiatry. 1997; 54: 337–343.Google Scholar
2. Desmarais, S, Van Dorn, R, Johnson, K, etal. Community violence perpetration and victimization among adults with mental illness. Am J Public Health. Published online ahead of print, February 13, 2014: e1–e8. doi:10.2105/AJPH.2013.301680.Google Scholar
3. Monahan, J. The inclusion of biological risk factors in violence risk assessments. In: Singh I, Sinnott-Armstrong W, Savulescu J, eds. BioPrediction, Biomarkers, and Bad Behavior: Scientific, Legal, and Ethical Implications. New York: Oxford University Press; 2013: 57–76.Google Scholar
4. Cohen, B, Bonnie, R, Monahan, J. Understanding and applying Virginia's new statutory civil commitment criteria. Developments in Mental Health Law. 2009; 28(1): 127–139.Google Scholar
5. Meehl, P. Clinical Versus Statistical Prediction: A Theoretical Analysis and a Review of the Evidence. Minneapolis, MN: University of Minnesota; 1954.Google Scholar
6. Ægisdóttir, S, White, M, Spengler, P, etal. The meta-analysis of clinical judgment project: fifty-six years of accumulated research on clinical versus statistical prediction. The Counseling Psychologist. 2006; 34(3): 341–382.Google Scholar
7. Skeem, J, Monahan, J. Current directions in violence risk assessment. Current Directions in Psychological Science. 2011; 20: 38–42.Google Scholar
8. Tardiff, K. Clinical risk assessment of violence. In: Simon R, Tardiff K, eds. Textbook of Violence Assessment and Management. Washington, DC: American Psychiatric Press; 2008: 3–16.Google Scholar
9. Webster, C, Douglas, K, Eaves, D, Hart, S. HCR-20: Assessing Risk for Violence (Version 2). Vancouver: Simon Fraser University; 2007.Google Scholar
10. Douglas, K, Hart, , Webster, C, Belfrage, H. HCR-20 (Version 3): Assessing Risk for Violence. Burnaby, BC, Canada: Mental Health, Law, and Policy Institute, Simon Fraser University; 2013.Google Scholar
11. Douglas, K, Hart, S, Groscup, J, Litwack, T. Assessing violence risk. In: Weiner I, Otto R, eds. The Handbook of Forensic Psychology, 4th ed. Hoboken, NJ: John Wiley; 2014: 385–442.Google Scholar
12. Monahan, J, Steadman, H, Silver, E, etal. Rethinking Risk Assessment: The MacArthur Study of Mental Disorder and Violence. New York: Oxford University Press; 2001.Google Scholar
13. Monahan, J. The classification of violence risk. In: R. Otto R, Douglas K, eds. Handbook of Violence Risk Assessment. New York: Routledge; 2010: 187–198.Google Scholar
14. Sturup, J, Kristiansson, M, Monahan, J. Gender and violent behavior in Swedish general psychiatric patients: a prospective clinical study. Psychiatr Serv. 2013; 64(7): 688–693.Google Scholar
15. Quinsey, V, Harris, G, Rice, M, Cormier, C. Violent Offenders: Appraising and Managing Risk, 2nd ed. Washington, DC: American Psychological Association; 2006.Google Scholar
16. Rice, M, Harris, G, Lang, C. Validation of and revision to the VRAG and SORAG: the Violence Risk Appraisal Guide—Revised (VRAG-R). Psychol Assess. 2013; 25(3): 951–965.CrossRefGoogle Scholar
17. Lidz, C, Mulvey, E, Gardner, W. The accuracy of predictions of violence to others. JAMA. 1993; 269(8): 1007–1011.CrossRefGoogle ScholarPubMed
18. Yang, M, Wong, S, Coid, J. The efficacy of violence prediction: a meta-analytic comparison of nine risk assessment tools. Psychol Bull. 2010; 136(5): 740–767.Google Scholar
19. Kroner, D, Mills, J, Morgan, B. A coffee can, factor analysis, and prediction of antisocial behavior: the structure of criminal risk. Int J Law Psychiatry. 2005; 28(4): 360–374.Google Scholar
20. Skeem, J, Manchak, S, Peterson, J. Correctional policy for offenders with mental disorder: creating a new paradigm for recidivism reduction. Law Hum Behav. 2011; 35(2): 110–126.Google Scholar
21. Hart, S, Michie, C, Cooke, D. Precision of actuarial risk assessment instruments: evaluating the ‘margins of error’ of group v. individual predictions of violence. Br J Psychiatry Suppl. 2007; 49: s60–s65.Google Scholar
22. Cooke, D, Michie, C. Limitations of diagnostic precision and predictive utility in the individual case: a challenge for forensic practice. Law HumBehav. 2010; 34(4): 259–274.Google Scholar
23. Hanson, R, Howard, P. Individual confidence intervals do not inform decision-makers about the accuracy of risk assessment evaluations. Law Hum Behav. 2010; 34(4): 275–281.Google Scholar
24. Scurich, N, Monahan, J, John, R. Innumeracy and unpacking: bridging the nomothetic/idiographic divide in violence risk assessment. Law Hum Behav. 2012; 36(6): 548–554.Google Scholar
25. Abraham, K. Distributing Risk: Insurance, Legal Theory, and Public Policy. New Haven, CT: Yale University Press; 1986.Google Scholar
26. National Research Council. Improving Risk Communication. Washington, DC: National Academy Press; 1989.Google Scholar
27. Faigman, D, Monahan, J, Slobogin, C. Group to individual (G2i) inference in scientific expert testimony. University of Chicago Law Review. In press.Google Scholar
28. Baird, C. A question of evidence: A critique of risk assessment models used in the justice system. National Council on Crime & Delinquency Web site. http://www.nccdglobal.org/sites/default/files/publication_pdf/special-report-evidence.pdf. Published February 2009. Accessed November 25, 2013.Google Scholar
29. Andrews, D. The risk-need-responsivity (RNR) model of correctional assessment and treatment. In: Dvoskin J, Skeem J, Novaco R, Douglas K. Using Social Science to Reduce Violent Offending. New York: Oxford University Press; 2012: 127–156.Google Scholar
30. Monahan, J, Skeem, J. Risk redux: The resurgence of risk assessment in criminal sanctioning. Federal Sentencing Reporter. In press.Google Scholar
31. Heilbrun, K. Prediction versus management models relevant to risk assessment: the importance of legal decision-making context. Law Hum Behav. 1997; 21(4): 347–359.Google Scholar
32. Howard, P, Dixon, L. Identifying change in the likelihood of violent recidivism: causal dynamic risk factors in the OASys violence predictor. Law Hum Behav. 2013; 37(3): 163–174.Google Scholar
33. Kroner, D, Yessine, A. Changing risk factors that impact recidivism: in search of mechanisms of change. Law Hum Behav. 2013; 37(5): 321–336.Google Scholar
34. Menzies, R, Webster, C, Sepejak, D. Hitting the forensic sound barrier: predictions of dangerousness in a pre-trial psychiatric clinic. In: Webster C, Ben-Aron M, Hucker S, eds. Dangerousness: Probability and Prediction, Psychiatry and Public Policy. New York: Cambridge University Press; 1985: 115–143.Google Scholar
35. Appelbaum, P. Reference guide on mental health evidence. In: Federal Judicial Center. Reference Manual on Scientific Evidence. Washington, DC: National Academies Press; 2011: 813–896.Google Scholar
36. Singh, J, Petrila, J. Measuring and interpreting the predictive validity of violence risk assessments. Behav Sci Law. 2013; 31(1): 1–7.CrossRefGoogle ScholarPubMed
37. Sadeh, N, Binder, R, McNiel, D. Recent victimization increases risk for violence in justice-involved persons with mental illness. Law Hum Behav. Advance online publication July 15, 2013. doi:10.1037/lhb0000043.Google Scholar
38. Knock, M, Park, J, Finn, C, etal. Measuring the suicidal mind: Implicit cognition predicts suicidal behavior. Psychol Sci. 2010; 21(4): 511–517.Google Scholar
39. Skeem, J, Manchak, S, Lidz, C, Mulvey, M. The utility of patients’ self- perceptions of violence risk: consider asking the person who may know best. Psychiatr Serv. 2013; 64(5): 410–415.Google Scholar
40. Aharonia, E, Vincent, GM, Harenskia, CL, etal. Neuroprediction of future rearrest. Proc Natl Acad Sci U S A. 2013; 110(15): 6223–6228.Google Scholar