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Offender reintegration : an RNR-GLM framework

Casey, Sharon 2009, Offender reintegration : an RNR-GLM framework, in Abstracts of the XXXIst international congress on Law and Mental Health, International Academy of Law and Mental Health, Montreal, Quebec, pp. 282-282.

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Title Offender reintegration : an RNR-GLM framework
Author(s) Casey, Sharon
Conference name International Congress on Law and Mental Health (31st : 2009 : New York, N.Y.)
Conference location New York, N.Y.
Conference dates 28 Jun.-3 Jul. 2009
Title of proceedings Abstracts of the XXXIst international congress on Law and Mental Health
Publication date 2009
Start page 282
End page 282
Publisher International Academy of Law and Mental Health
Place of publication Montreal, Quebec
Summary According to the Good Lives Model, the inability to meet human needs in an adaptive manner - through lack of suitable circumstances, abilities, or opportunity - compels the individual to address this deficiency through other (maladaptive) means available to them. Thus the GLM contends that offending behavior serves a specific function and that different behaviors (crimes) are used to meet different needs. This presentation will discuss how the Good Lives Model can be used, in conjunction with that of the Risk-Needs Model, in a prison service. The combined model develops and implements programming for offenders prior to release into the community by mapping the offender's offence, social and psychological history against the secondary goods described in the Good Lives Model (i.e., the concrete means by which primary goods or human needs can be achieved), with identified deficits in secondary goods being the focus of intervention.
Language eng
Field of Research 160202 Correctional Theory, Offender Treatment and Rehabilitation
Socio Economic Objective 940402 Crime Prevention
HERDC Research category E3.1 Extract of paper
Persistent URL http://hdl.handle.net/10536/DRO/DU:30032175

Document type: Conference Paper
Collection: School of Psychology
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Created: Mon, 10 Jan 2011, 11:14:56 EST

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