Predictive Power of Dynamic (vs. Static) Risk Factors in the Finnish Risk and Needs Assessment Form

We estimated the predictive power of the dynamic items in the Finnish Risk and Needs Assessment Form (Riski- ja tarvearvio [RITA]), assessed by caseworkers, for predicting recidivism. These 52 items were compared to static predictors including crime(s) committed, prison history, and age. We used two...

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Bibliographic Details
Authors: Salo, Benny (Author) ; Laaksonen, Toni (Author) ; Santtila, Pekka (Author)
Format: Electronic Article
Language:English
Published: 2019
In: Criminal justice and behavior
Year: 2019, Volume: 46, Issue: 7, Pages: 939-960
Online Access: Volltext (Resolving-System)
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Keywords:
Risk and needs assessment
Dynamic risk factors
Recidivism
Machine learning
RITA
Finland
Description
Summary:We estimated the predictive power of the dynamic items in the Finnish Risk and Needs Assessment Form (Riski- ja tarvearvio [RITA]), assessed by caseworkers, for predicting recidivism. These 52 items were compared to static predictors including crime(s) committed, prison history, and age. We used two machine learning methods (elastic net and random forest) for this purpose and compared them with logistic regression. Participants were 746 men who had and 746 who had not reoffended during matched follow-up periods from 0.5 to 5.8 years. Both RITA items and static predictors predicted general and violent recidivism well (area under the curve [AUC] = .74-.78), but to combine them increased discrimination only slightly over static predictors alone (ΔAUC = .01-.03). Calibration was good for all models. We argue that the results show strong potential for the RITA items, but that development is best focused on improving usability for identifying treatment targets and for updating risk assessments.
ISSN:1552-3594
DOI:10.1177/0093854819848793