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PSYCH 3CC3 (101)

Assessment of Violence Risk.docx

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McMaster University
Richard B Day

November 11 , 2013 Psych 3CC3: Forensic Psychology Assessment of Violence Risk Assessment of Violence Risk - After NCRMD, institutional options Baxtrom vs Herold (1966) – U.S. Supreme Court - Baxtrom committed crime close to end of sentence - And committed to criminal hospital - Baxtrom’s rights violated – hospitalized beyond prison term without dangerousness assessment - ~1,000 prisoners moved to low security hospitals  20% subsequently violent  18% discharged within a year  1% later readmitted to secure hospitals - Over 4.5 years:  50% of patients released  <3% returned to secure hospital - Could not be held without assessment of behaviour November 12 , 2013 Assessing Risk of Violence - Sentencing decisions  How long before parole?  What treatments recommended or required? - Parole eligibility - Eligibility for release from psychiatric custody - Support provided after release - Risk management:  What factors increase risk?  What factors reduce risk? Thorberry & Jacoby (1979) - 586 patients released from Pennsylvania institution (Dixon v. Pennsylvania) - 3-year return/recidivisim rate = 23.7% (less than half) - 4-year violent arrest record = 15% (much smaller than predicted) Quinsey & Ambtman (1979) - Accuracy of future risk assessment was coming under question - Quinsey, Canadian - Trying to determine the reliability of risk assessment in forensic psychiatrists - Reliability of forensic psychiatrists compared to untrained high school teachers - 9 high school teachers and forensic psychiatrists - Evaluate recidivism risk, release, for 30 patients:  11 property offenders  9 child molesters  11 serious adult offenders (murder) - 3 types of patient data:  Offender description  Patient history  Psychiatric assessments (IQ, MMPI, Rorschach, etc.) - Read through the material and come up with a probability risk - To what extend did the psychiatrist agree in any given case on whether or not the individual would reoffend - Inter-rater reliabilities (agreement between) was low in both cases - Inter-rater reliabilities low for both psychiatrists and teachers - Inter-rate reliabilities similar for psychiatrists and teachers - Psychiatrists rate offenders as more likely to commit offense than do teachers - Little use of psychiatric assessments made by either group, relied primarily on the offense that had been committed and the patients history - Courts have mandated that patients must receive psychiatric assessments yet even psychiatrist do not rely on these information - These data question the usefulness of psychiatric examinations in the prediction of dangerousness. Perhaps psychiatric examinations would be best restricted to determining whether an offender is treatable and should not address the issue of dangerousness Violence Assessment in the 1980s - Monahan (1981): psychiatrists and psychologists are accurate in no more than one out of three predictions of violent behaviour - Barefoot v. Estelle (1983): we are [not] convicted that the view of the APA should be converted into a constitutional rule barring an entire category of export testimony…neither petitioner nor the Association suggests that psychiatrists are always wrong with respect to future dangerousness, only most of the time  Barefoot denied release based on psychiatric assessment on his likelihood to reoffend  APA agrees with Barefoot and support the plaint  Court reject the APA statement Violence Risk Assessment Research - Actuarial methods: ignore the inaccurate clinician, and rely on statistics data  Aspects of individuals (age, religion, race) that predict better than psychiatric evaluation if an individual will be likely to reoffend - Structured professional judgment: increase clinician’s accuracy by standardizing the assessment process  Quantify the judgment and use to make decision Measures of Accuracy - Four common measures to describe the accuracy - Positive predictive power:  What is the probability that if the test identifies someone having a problem who actually does?  PPP = H/(H+FA)  Critically depends on the base rate, what proportion of individuals actually do have the condition  Accuracy depends on base rate  The smaller the base rate (the less people have the condition), the worse the test will be at correctly identifying  Assume 95% accuracy (H and R)  False alarms and misses 5%  Assume 10,00 people, disorder has 50% prevalence  5,000 with disorder and 5,000 without disorder  4750 have disorder, miss 250 people  4750 don’t have disorder, false alarms 250  PPP gives 95% accuracy rate  If disorder only 5% prevalence, 500 people have disorder, 475 have disorder, 25 missed, 475 false alarm, 9025 correct rejection; PPP = 475/(475+475) = 50%  Half of the people we identify as having the disorder do not  Fundamental problem for any test of a condition with low base rate - Negative Predictive Power  NPP = R/(R+M)  Of all the people your tests says do not have disorders, how many actually don’t - Positive and negative predictive power: given your test outcomes, how accurate are they - Sensitivity: S = H/(H+M): of all people who have disorder how many did you catch - Specificity: Sp = R/(R + FA): of al the people who don’t, how many did you correctly identify - Overall accuracy: Accuracy = R+H/(R+H+FA+M) - Negative predictive power, sensitivity and specificity do not change much with changing base rate - PPP at its maximum when the prevalence of the population is 50% - Because of the problems with PPP, we use areas under the curve (AUC – ROC) ROC Measures of Accuracy - Maps the ratios of hits to false alarms - ROC = Sensitivity/(1 – Specificity) = (H/H+M)/(1 – (R/(R+FA)) = H/FA - If you have a criterion based measure, and you have to choose a decision criterion - You generate a series of points as you shift the criteria - Area under the curve, the higher the better the ratio of hits to false alarms - No area under the curve (0) = no predictive ability - Want 70% or higher November 14 , 2013 ROC Curve - Hits vs. false alarms - Choosing a cut off point - The higher we set the criteria, the higher will be the ratio of hits to false alarms AUC Measure of Accuracy - Given test and assorted in violent and no violence based on the predictions - If we took a random sample from an individual in both groups, what is the probability that the individual that we predicted to be violent would have a higher score on the test than the individual that is not - AUC – p(V>NV) - Looking for approximately 70% probability or higher Violence Risk Appraisal Guide (VRAG) - Developed in the mid 90s - Ontario study - Predict violence among offenders with prior violent episodes - 685 violent or sexually violent Oak Ridge offenders - Insanity acquittals and matched sample in Oak Ridge for one day between 1975 and 1978 - Measure: any criminal charge, or chargeable behaviour (independent variable) VRAG Predictor Variables - Childhood history - Adult adjustment - Index offense – offense for which the individual was committed - Assessment results - Variables chosen for VRAG:  12 variables that were most closely correlated with reoffending  Psychopathy checklist .34  The best indicator f violent reoffending  Elementray school maladjustment .31  DSM-III personality disorder .26  Age at index offense -.26  The older you were, the lower the probability of reoffending  Separated from parents under age 16 .25  Failure on prior conditional release .24  Property offense history .20  Never married .18  DSM-III Schizophrenia -.17  Victim injury in index offense -.16  Alcohol abuse history .13  Female victim in index offense -.11 SORAG - Extension of VRAG to assess risk of violence among past sexual offend
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