Risk Assesment
Risk Assessment
Examples
A police officer escorts a young man acting in a threatening manner to hospital emergency
A 15 year old girl has allegedly attacked and seriously injured a classmate and a crown prosecutor now petitions the court to raise her to adult court
A person suffering from schizophrenia murdered his parents was found criminally responsible and is now requesting a transfer to a lower security facility
Assessment
Assessment = an evaluation of the person’s cognitive, emotional, and behavioural functioning
Goal = to obtain information that provides a better understanding of the individual
This information is used to guide decisions, interventions, and management of offenders
Risk Assessment
Considers the information used
Risk Prediction
Uses this information to assess the risk that people will commit a crime in the future
Risk Management
Develop effective intervention strategies to manage that risk
Assessment Measures
Clinical interviews
Self-reports
Rating scales
Peer ratings
Direct observations
Actuarial instruments
Physical exams
Lab tests
Psychological instruments
Why Do Assessments?
Civil settings
Fitness to stand trial
Sentencing decisions
Young offenders, dangerous offenders
Suicidal ideation
Psychological disorders
Classification
Treatment intervention strategies
Parole decision-making
Pre-release
Sources of Information
Structured interview
Self reports
Collateral contacts (family, friends)
Police reports, prior criminal justice reports
Court transcripts, Judge’s Reasons for Sentencing, Pre-Sentence Reports, Victim Impact Statements
Prior mental health reports, psychological tests, actuarial measures
What is Being Assessed?
Criminogenic Risk Factors
Factors that are static
Criminogenic needs
Factors that are dynamic and place an offender at greater risk of reoffending
Stable vs. acute dynamic factors
Treatment intervention strategies
Identified needs become treatment targets
Noncriminogenic needs may also be treated
Assessment Challenges?
Informed consent (voluntary, informed, and understood)
Limits of confidentiality (researcher vs. clinician)
Rapport
Earning trust
Evaluation of honesty
How are these challenges overcome?
Collateral sources of information
Use standardized actuarial instruments
Compare information from these sources with clinical judgements
Civil Setting: Duty to Warn/Protect
Liable for failing to protect a potential victim
Canadian Code of Ethics for psychologists requires psychologists to “do everything possible to prevent serious physical harm or death of others…may include reporting to the appropriate authorities or an intended victim”
Approaches
Unstructured clinical judgement
1st generation
Selection and combination of information are not guided by any explicit rules
Decision rules unclear
Informal, subjective
No specific risk factors, low accuracy
Problems:
Illusory correlations (believe an association occurs between predictor and outcome)
Base rates, reliance on salient cues, overconfidence, gender
Dr. “DEATH”
Grigson in Texas Capital Sentencing Proceedings
Doctor...do you have an opinion within reasonable psychiatric certainty whether or not there is a probability that the defendant will commit criminal acts of violence in the future?
Yes, he most certainly would.
Would you state whether or not that would be true regardless of where he is?
It wouldn’t matter whether he is in the penitentiary or whether he was free. Wherever he is he will continue commit violence.
Would you state whether or not, Doctor, you have an opinion within reasonable psychiatric certainty as to the degree of that probability that you have just expressed to this jury?
Well, yes sir, I would put it at one hundred percent and absolute.
Predictions of Recidivism
Predictions of future dangerousness has proven to be a difficult task for professionals.
Meta analyses have found actuarial instruments to be 10-13% more accurate than clinical judgments for general recidivism.
Average effect size for the prediction of violence around.30 and .46 for sexual offenders
Approaches
Actuarial
2nd generation
Coollect pre-specified risk factors and enter them into a statistical moodel that combines and weights them
Formal, objective
Empirically derived factors
Same factors used for each case
Speecific cutoffs for decisions
Focus on static measures
Actuarial more accurate than clinical
SIR-R
Type of current offinse
Age at admissioon
Previous incarcerations
Revocation of conditional release
Escape from custody
Security classification
Age first adult conviction
Previous conviction for assault
Marital status at admission
Interval at risk since last conviction
Number of dependents
Current length of sentence
Previous conviction for sex offences
Previous conviction for break and enter
Employment status at arrest
VRAG
Actuarial instrument designed to predict violent recidivism in serious offenders
Empirically-derived actuarial risk assessment device
Developed on a single large sample of forensic psychiatric patients (n = 618)
12 static items
scores range between -27 and +35
scores categorized into 9 risk bins
higher scores/bins = higher risk
PCL-R score (+)
Elementary school problems (+)
Personality disorder (+)
Separated from parents (+)
Failure on prior release (+)
Alcohol abuse (+)
Non violent offense history (+)
Never married (+)
Schizophrenia (-)
Victim injury (-)
Female victim (-)
Age (-)
Actuarial Disadvantages
Focus on static factors
Risk level cannot change
Provides little information about treatment needs
Must cross-validate risk factors on different samples
Approaches
Structured Professional Judgement (SPJ)
3rd generation
Specific risk factors
Derived from literature
Includes static and dynamic
Includes case critical factors
Selection of items is guided by explicit rules but combination of these items is not
Additional items may be considered
Rater makes final decision about risk level
Fewer predictive studies
LSI-R
Designed to predict general recidivism
54 risk and criminogenic needs items (i.e., both static and dynamic)
10 subcomponents
scores categorized into 5 risk/need levels
Higher scores = higher risk
Criminal history
Education/Employment
Financial
Family/Marital
Accommodation
Leisure/Recreation
Companions
Alcohol/Drug problem
Emotional/Personal
Attitudes/Orientation
HCR-20
Structured professional judgment instrument
Designed for violence risk assessment in criminal and psychiatric populations
Items selected on basis of lit review and clinical experience
Historical items
Past violent behavior
Young age at first violence
Relationship instability
Employment problems
Substance use problems
Major mental illness
Psychopathy
Early maladjustment
Personality disorder
Prior supervision failure
Clinical items
Lack of insight › Little insight into mental disorder, treatment needs, triggers
Negative attitudes › Procriminal, supportive of violence
Active symptoms of major mental illness › Specific threat delusions, sadistic fantasies
Impulsivity › Affective instability, behavioral acting out
Unresponsiveness to Treatment › Respond poorly to treatment, non-compliant, refuse treatment
Risk Management Items
Plans lack feasibility › No plans or unsuited to individual’s needs
Exposure to destablizers › Antisocial peers, victims, substance use
Lack of personal support
Noncompliance › Refuse to take medication, fail to comply with discharge plans
Stress › Ability to cope with stress, association between stress and violence
HCR-20: Risk Ratings
Low risk - monitor and intervene with low priority and intensity
Mod risk - monitor and intervene with some priority and intensity
High risk - monitor and intervene with high priority and intensity
SPJ: Strengths
Predicts likelihood, monitors change, and suggests intervention and management strategies
Simple, reliable
Greater flexibility because case-specific info and interactions can be considered
Predictive and dynamic validity with variety of samples
SPJ Weaknesses
“Human” judgment may reduce accuracy
Requires clinical training
Which Approach Better?
Actuarial is more accurate than unstructured clinical judgment
Structured professional judgment appears to be similar to actuarial in accuracy
Predicting Recidivism: Dynamic
Many risk scales have been created to predict various types of outcome and in general are reliable measures of risk.
One of the major limiting factors is that many of them do not contain dynamic factors and as such are not able to inform risk management.
Gendreau et al. (1996), in a meta analysis of prospective studies with a minimum follow up of 6 months, found that dynamic risk factors were equally, if not better, at predicting general recidivism (.12 for static and .15 for dynamic).
More recent meta-analysis with similar criteria predicting violence found a similar pattern of results (.22 for static and .25 for dynamic; Campbell et al., 2007)
For the most part, the risk management process is subjective, may vary from one clinician/officer to the next, and may be vulnerable to the same limitations as risk assessments based on unstructured clinical judgments.
Obstacles for the Investigation of Dynamic Risk
Lack of confidence in predictive ability
Concerns regarding measurement
Challenges in analyzing the data
General Recidivism
Substance AbuseAssociates | |
Attitudes | Social support |
Interpersonal Conflict | Difficulties with Family/Poor |
EmploymentProblems/Dissatisfaction | Single/Unsupportive Partner/Marital Problems |
Emotional Instability (e.g., depression, loneliness, negative affect, anger, worries) | Unstable Accommodations Perceived problem level Expected positive outcomes of crime |
Deficient Cognitive Skills | Financial Difficulties |
Barriers to Treatment | Social Achievement |
Research on dynamic predictors of violence in its infancy.
Most research is disjointed.
Summary papers have been useful at consolidating the research. › (e.g., Loza & Dhaliwal, 2005; Douglas & Skeem, 2005)
Violent Recidivism
Victim AccessPoor mechanisms for addressing stressors | |
General self regulation, impulsivity | Treatment alliance, adherence, motivation |
Attitudes | Availability/Means to commit violence |
Substance abuse | Employment instability |
Negative affect | Relationship instability |
Negative social ties | Victim empathy |
Acceptance of responsibility |
Sexual Recidivism
APD*Distorted attitudes | |
Negative social influences | Emotional collapse |
Hostility towards women | Collapse of social supports |
Rejection/Loneliness | Substance abuse |
Lack of concern for others | General self regulation* |
Lack of cooperation with supervision | Employment instability* |
Impulsive acts | Exposure to high risk situations |
Poor cognitive problem solving | PCL-R |
Relationship stability | Justification |
Sexual preoccupation*, sex as coping, negative emotion/hostility, deviant sexual preference* | Victim access, hostility, sexual preoccupation, rejection of supervision |
Dynamic Risk: Limitations
Single point measures
Pre/post measures
Large domains/scales
Findings disjointed
Long term predictions
Frequency of reassessment
Ecological validity
Weak statistical procedures
Lack of consideration for protective factors
Protective Factors
Those characteristics or assets of an individual that buffer risk.
Literature abundant in youth mental health.
Very few efforts have been made to extend that literature into understanding adult criminal behavior.
Among serious group of youth, positive peer relations, good school performance, participation in organized leisure activity, positive response to authority correlated with lower recidivism and better compliance (Hoge et al., 1996).
Some preliminary research found that protective factors added incrementally to the prediction of general recidivism
Structured activities and strong family relations potentially important factors in understanding protection from criminal behavior (DeMatteo et al., 2005).
Outcome Statistics
Correlations
Between risk measure and outcome
Range from + 1.00 to - 1.00
r = .30
Odds ratio
Take scores above and below median
OR = 2.50
One group is 2.5 times more likely than other group to possess some criterion
Analysis of variance
Time to reoffend, number of offenses
Compare 2 or more groups
Regression
Determine the proportion of variance accounted for by the risk measure
Input one set of variables and determine if risk measure accounts for any additional variance
Survival curve analysis
Takes into account length of follow-up
Evaluate how quickly participants recidivated
Decision Outcomes
TP = person correctly predicted to be violent
TN = person correctly predicted not violent
FP = person predicted to be violent but is not
FN = person predicted to be nonviolent but is
Errors have different consequences › FP – individual
Example 1
Baxstrom study (Steadman & Cocozza, 1974) › 1966 – US supreme court › “dangerous” mentally ill patients released into the community › Follow-up 4.5 years › 98 patients followed › Used age and previous criminal history to classify into low and high risk
Decision Outcomes
Clinical judgments › AUC = 0.55
Actuarial tools › AUC = 0.80
Structured clinical guidelines › AUC = 0.75
Methodological Issues
Definition of violence › Violence is the actual, attempted, or threatened harm to a person or persons › Type, severity, target of violence
Length of follow-up period › Longer the follow-up = higher rate of violence
Most studies use a limited number of predictors › Need to use multiple predictors across domains
Historical, neurological, situational, psychological
Most use Static vs. dynamic › static = historical, factors that do not change › dynamic = factors that fluctuate or can change
Written Reports
Questions to Consider:
What is the likelihood that the individual will engage in future violence?
risk and protective factors
probabilistic statement
time period
relative to some specific comparison group
What is the probable context, victim, severity and frequency of any future violence?
What steps need to be taken to manage the individual’s risk?
What circumstances might exacerbate the individual’s risk?
Framing the Prediction
this person is dangerous
If [the following risk factors are present] then there is a [high, moderate, low] probability that the person will engage in [some specific] behavior within [specific period of time] that may place [specify victims] at risk for [specify type and severity of harm]
Communicating Risk
Low violence risk
Few risk factors present
No further assessment/preventive actions
E.g., 60-year-old depressed man with no violent history and no threats of violence
Moderate violence risk
Several risk factors present
Gather more information/monitor person
E.g. 25-year-old woman who is abusing alcohol, with a history of assaults, but without a recent violent act
High violence risk
Numerous risk factors present
Priority given to gathering additional information and close monitoring
Make preparations for preventive actions should condition deteriorate
E.g. 30-year-old woman who is using street drugs, with a history of assaults, making recent vague threats
Extreme High Violence Risk
Numerous risk factors present
Enough information to make a decision
Take preventative action (e.g. intensive case management, involuntary hospitalization, warn potential victim)
E.g. 35-year-old man who is using street drugs, has a history of recent violence, is threatening his spouse, and has recently purchased a gun
Treatment
EFFECTIVE CORRECTIONAL TREATMENT SHOULD BE BASED ON THE PRINCIPLES OF RISK, NEED, RESPONSIVITY (Andrews & Bonta, 2010)
Risk Principle (”Who”)
Assess risk
Offenders deemed higher risk of reoffending should be the focus of institutional intervention programs
Match risk level to treatment services level
Low risk offenders are unlikely to reoffend and may actually increase in risk when exposed to treatment
Need Principle (”What”)
Target criminogenic needs primarily rather than non-criminogenic needs to decrease recidivismThese are criminogenic needs known to contribute to reoffending
Antisocial attitudes
Substance abuse
Antisocial peers
Non criminogenic needs: Self-esteem, anxiety
Responsivity Principle (”How”)
Correctional intervention should match the learning styles of offenders
General: Use structured cognitive behavioural interventions
Specific: Match treatment delivery to offender’s ability and learning style
Treatment
Treatment programs that adhere to the RNR model (particularly cognitive-behavior based programs) have been demonstrated to effectively reduce recidivism in various settings and with various types of offending