How do static and dynamic risk factors work together to predict violent behaviour among offenders with an intellectual disability?
Static risk factors alone predict violence in offenders with ID, so extra dynamic scales waste time.
01Research in Context
What this study did
Van Hanegem et al. (2014) asked a simple question. Do we need to measure both old, unchangeable risks and new, changeable risks when we predict violence in offenders with intellectual disability?
They tracked which kind of risk—static or dynamic—did the heavy lifting in forecasting violent acts.
What they found
Static risks won. The unchangeable facts, like past offenses, came first and soaked up most of the predictive power.
Dynamic risks, such as current mood or peer problems, mainly echoed the static signal instead of adding new information.
How this fits with other research
Neuringer et al. (2007) and Levin et al. (2014) mapped aggression types and personality clusters in the same adults-with-ID group. Their work set the stage for E et al. to test which risk domain really drives the violence.
Austin et al. (2015) looked at the HCR-20 tool one year later and found it predicts inpatient aggression in ID, but the clinical subscale underperforms. That result lines up with the 2014 finding—dynamic pieces add little once static items are known.
Taylor (2002) urged clinicians to favor behavioral or CBT plans over pills for aggression in this population. Van Hanegem et al. (2014) now hints we can trim assessment time by dropping extra dynamic scales and focus staff hours on those proven treatments instead.
Why it matters
If you write risk plans in forensic ID services, you can stop doubling up. Score the static items, lock in your safety level, then move your team’s energy to behavior support and skills teaching. Less paperwork, more treatment.
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02At a glance
03Original abstract
BACKGROUND: Research on risk assessment with offenders with an intellectual disability (ID) has largely focused on estimating the predictive accuracy of static or dynamic risk assessments, or a comparison of the two approaches. The aim of this study was to explore how static and dynamic risk variables may 'work together' to predict violent behaviour. METHODS: Data from 212 offenders with an ID were analysed. Risk assessment tools included one static measure (Violence Risk Appraisal Guide), and two dynamic measures (Emotional Problems Scale and the Short Dynamic Risk Scale). Six-month concurrent prediction data on violent behaviour were collected. A structured methodology was employed to explore putative relationships between static and dynamic factors. RESULTS: Static risk factors temporally preceded dynamic ones, and were shown to dominate both dynamic measures, while there was a non-zero relationship between the static and the two dynamic measures. According to Kraemer et al., these findings suggest that dynamic risk factors function as proxy risk factors for static risk. CONCLUSIONS: Dynamic and static risk factors appear to capture elements of the same underlying risk associated with violent behaviour in individuals with an ID. This is the first study to empirically explore risk interrelationships in the forensic ID field. We discuss the importance of the contribution of dynamic variables in the prediction and management of risk.
Journal of intellectual disability research : JIDR, 2014 · doi:10.1111/j.1365-2788.2012.01645.x