Abstract
The risk-need-responsivity (RNR) framework has great utility to the field of corrections and public policy about how best to punish and treat those involved in the justice system. The basic premise is that the decision should be grounded in responsivity—the response that will generate the most desired positive outcomes, particularly if one is interested in reducing recidivism. We have explored the RNR framework and have presented an updated RNR framework with empirically and clinically based principles. In this final chapter, we highlight six key conclusions: (1) there is an expansive body of literature supporting an RNR framework of treatment and program delivery; (2) offender risk and need assessment instruments can, with some adjustments, be used to identify primary offender risks and needs; (3) a significant treatment gap in services currently exists to address offender’s primary needs, and this gap contributes to the current high rates of negative outcomes; (4) meta-analyses of correctional treatment programs can be used to identify programs that result in significant reductions in recidivism; (5) simulation models that test RNR implementation scenarios on a large scale illustrate substantial reductions in recidivism; and (6) RNR programming can be integrated into a system of treatment delivery designed for particular jurisdictions. Future research is needed in the area of substance use disorders, measurement of criminogenic needs, identifying dosage levels, testing treatment matching strategies, and understanding how offender-level demographics should be integrated into the RNR model. Together, these will advance the next generation on the RNR framework.
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Taxman, F.S., Caudy, M.S., Pattavina, A. (2013). Risk-Need-Responsivity (RNR): Leading Towards Another Generation of the Model. In: Taxman, F., Pattavina, A. (eds) Simulation Strategies to Reduce Recidivism. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6188-3_11
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DOI: https://doi.org/10.1007/978-1-4614-6188-3_11
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