Evaluation of the Predictive Validity of a Risk-Need-Responsivity Assessment Tool (RNR-A) in the Swedish Prison and Probation Service

AuthorMaria Danielsson,Dan Andersson,Peter Johansson Bäckström,Louise C. Starfelt Sutton
Published date01 June 2022
Date01 June 2022
Subject MatterArticles
Evaluation of the
Predictive Validity of a
Assessment Tool
(RNR-A) in the Swedish
Prison and Probation
Peter Johansson Bäckström,
Maria Danielsson, Louise
C. Starfelt Sutton,
and Dan Andersson
To appraise the real-worldimplementation of the risk principle, this study
examined the predictive validity of a Risk-Need-Responsivity assessment in
the Swedish Prison and Probation Service. Reconviction rates at 24 months
follow-up in a cohort of 2,442 offenders were used to assess calibration and
discrimination indices. Results indicated acceptable predictive accuracy
(AUC =.68.74), with scope for improvement among young adult offenders.
The tools utility was supported foremost by its ability to screen out low-risk
offenders, while over-prediction of recidivism among medium- and high-risk
offenders calls for more comprehensive assessment to inform the effective
planning of rehabilitative service intensity.
risk assessment, Risk-Need-Responsivity, predictive validity, Sweden, recidivism
The Swedish Prison and Probation Service, Norrkoping, Sweden
Corresponding Author:
Louise C. Starfelt Sutton, The Swedish Prison and Probation Service, Slottsgatan 78, 601 80,
Norrkoping, Sweden.
Email: louise.starfeltsutton@kriminalvarden.se
The Prison Journal
2022, Vol. 102(3) 367390
© 2022 SAGE Publications
Article reuse guidelines:
DOI: 10.1177/00328855221095561
Since the introduction of assessment tools for predicting recidivism, they have
become an integrated part of prison and probation services as well as youth
care (e.g., Andrews et al., 1990, 2006). Historically, the assessment of recid-
ivism risk has relied solely on clinical judgment. The f‌irst generation of risk
assessments thus typically involved correctional staff (i.e., probation off‌icers
or prison staff) or clinical professionals (i.e., psychologists, psychiatrists, or
social workers) conducting unstructured assessments. One of the main advan-
tages of this type of assessment is f‌lexibility, which is benef‌icial in unique
cases. Given a lack of transparency, however, it is diff‌icult, if not impossible,
to describe the decision-making process to measure the agreement between
different assessments or assessors (Brown & Singh, 2014). Unstructured
risk assessments are therefore hard to replicate and have shown evidence of
personal biases (e.g., Bonta & Andrews, 2007; Van Voorhis & Brown,
1996) and limited predictive accuracy (Mills, 2017).
In the 1970s, there was a growing recognition of the benef‌its of evidence-
based actuarial risk assessment tools in place of professional judgment. As a
consequence, a second generation of assessment tools was developed. These
tools provided quantitative methods for assessing future risk, using static risk
factors that were linked to recidivism. Such factors were identif‌ied through
research on criminal populations mostly white males (Hannah-Moffat,
2013). The utility of these tools has been questioned due to a lack of f‌lexibil-
ity, despite increased accuracy in prediction when compared to unstructured
assessments (e.g., Ægisdóttir et al., 2006). Further, the exclusive reliance on
static factors obscures the complexity and variability of individual function-
ing and undercuts any possibility to identify intervention targets (Andrews
et al., 1990; Campbell et al., 2009).
With a growing interest in treatment and risk-reducing interventions and as
a reaction against a widespread treatment pessimism (nothing works;
Martinson, 1974), the research focus shifted in the late 1970s and early
1980s. This shift involved a change from a one-sided emphasis on static
risk factors to extensive empirical enquiry into risk factors that were
dynamic in nature to measure changes in the likelihood of violent or criminal
behavior. There is nothing inherently different with actuarial tools that justif‌iy
the exclusion of dynamic factors (Skeem & Monahan, 2011). Criminal history
has thus continued to be an important factor in third-generation risk assess-
ment tools, combined with dynamic factors that are sensitive to changes in
an offenders circumstances (Bonta & Andrews, 2007).
Afourthgenerationofriskassessmentinstruments has also been described in
the literature. These instruments are designed as an integrated part of risk
368 The Prison Journal 102(3)

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