Evaluating risk of violence…are we safe?

Angelica Restrepo
Psyc 406–2015
Published in
3 min readMar 11, 2015

Different scales have been developed throughout the years to manage aggression and predict offense or re-offense in clinical patients, populations and risk, and inmates.
The Historical, Clinical, Risk Management-20 (HCR-20; Webste et al., 1997) was designed to evaluate, predict, and manage the risk of violence in mentally ill patients or inmates. The authors selected 20 risk factors and divided them in the three subscales (historic, clinical, and risk).
The Violence Risk Appraisal Guide (VRAG; Webster et al., 1994) was developed as an instrument to evaluate dangerous criminality in high-risk males. The evaluation tool studies variables such as early academic maladjustment, the age of first infraction, psychiatric diagnosis, whether parents divorced before age 16, and history violent crimes. The VRAG is one of the most used and validated scales for the prediction of violence.
The Offender Group Reconviction Scale (OGRS) was designed to predict re-offense based on age, gender, and criminal history. The measure has been used by probation staff and correction facilities to predict future offenses in youth.

Considering that these are just some of the many tests existing in forensic psychiatry (the intersection of psychology/psychiatry and the law), one would think that the ground has been covered in terms of us being able to determine who would pose a problem for society. However, the problem remains that each of these tests is not measuring “criminality” or “psychopathy”, but related variables. So far, most tests in psychology serve this purpose, as the majority of psychological traits cannot be directly measured. However, psychological testing, specifically this kind that affects the safety and well being of society at large, becomes a problem when the tests are not valid

In a recent study, Coid, Ullrich and Kallis (2013) found that precisely these three tests, measuring inmates’ likelihood to re-offend, cannot predict such event more than 50% of the time. Therefore, by the same logic, if a judge wanted to know whether or not a sexual offender is likely to recommit such violent crime, he could simply toss a coin and make his decision on such grounds. The researchers applied the tests on 1396 inmates that were about to be released within the following 6 to 12 months. The results showed that the measures were able to predict (about 75% of the time) the behaviors of individuals when they had not received any psychiatric diagnosis. However, when it came to those diagnosed with psychiatric disorders, the measures could not predict the likelihood of re-offense more than 60% of the time, and 50% of the time for those with a diagnosis of psychopathy.

These extremely low rates of predictability definitely make me question the value of such measures. In fact, even if one would argued for their utility on the grounds that they did predict future offense for mentally sane inmates, the argument would be dismissed. That is, jail time inevitably affects an individual’s life in such a way that most likely, the majority of the released inmates will show some sort of psychological distress.
More valid measures need to be found, or revised, soon. Otherwise, disastrous consequences will occur (or perhaps have already occurred) from the release into society of Hannibal Lecter or Dexter.

Wakefield, H. & Underwager, R. (1998). Assessing violent recidivism: Issues for forensic psychologists. Retrieved from http://ipt-forensics.com/Library/ACFP98.ht

Monahan, J. (1981). The clinical prediction of violent behaviour. Rockville, MD: National Institute of Mental Health.

Webster, C.D., Harris, G.T., Rice, M., Cormier, C., & Quinsey, V.L. (1994). The Violence Prediction Scheme: Assessing Dangerousness in High Risk Men. IPT Journal, 10.

Coid, J. W., Ullrich, S., & Kallis, C. (2013). Predicting future violence among individuals with psychopathy. Retrieved from http://bjp.rcpsych.org/content/early/2013/09/14/bjp.bp.112.118471.abstract?sid=57005307-803a-42b0-b375-8e7a4fccc218

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