Extending the general aggression model: contributions of DSM-5 maladaptive personality facets and schema modes

This study sought to determine whether the addition of DSM-5 personality facets and schema modes improved the prediction of aggression history over and above General Aggression Model (GAM) cognitive knowledge structures (aggressive script rehearsal and normative beliefs supportive of aggression) and...

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Published in:Psychology, crime & law
Main Author: Dunne, Ashley L. (Author)
Other Authors: Lee, Stuart D. (Author); Daffern, Michael
Format: Electronic Article
Language:English
Published: 2019
In:Psychology, crime & law
Year: 2019, Volume: 25, Issue: 9, Pages: 875-895
Online Access: Volltext (Resolving-System)
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Summary:This study sought to determine whether the addition of DSM-5 personality facets and schema modes improved the prediction of aggression history over and above General Aggression Model (GAM) cognitive knowledge structures (aggressive script rehearsal and normative beliefs supportive of aggression) and anger. Participants were 208 incarcerated adult male prisoners who completed a battery of self-report psychological tests assessing the aforementioned constructs. Results indicated that aggression-related cognitive knowledge structures, anger, and various personality facets and schema modes were significantly associated with aggression. However, only anger, the personality facet Risk Taking, and the Enraged Child schema mode uniquely accounted for variance in multivariable modelling. This suggests that consideration of maladaptive personality facets, specifically risk taking, and schema modes characterised by intense anger and destructive coping behaviour, alongside established aggression-related cognitive and affective constructs, can (a) extend theoretical knowledge of the specific contents and processes involved in increasing aggression propensity, and (b) improve GAM’s application in clinical settings, particularly in regards to assessment and treatment of violent offenders.
ISSN:1477-2744
DOI:10.1080/1068316X.2019.1597089