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The European Journal of Psychology Applied to Legal Context

On-line version ISSN 1989-4007Print version ISSN 1889-1861

Abstract

LILA, Marisol; OLIVER, Amparo; GALIANA, Laura  and  GRACIA, Enrique. Predicting success indicators of an intervention programme for convicted intimate-partner violence offenders: The Contexto Programme. The European Journal of Psychology Applied to Legal Context [online]. 2013, vol.5, n.1, pp.73-95. ISSN 1989-4007.

Recent legal changes in Spain have led to an important increase in the number of men court-mandated to community-based partner violence offender intervention programmes. However, just a few of those interventions have been systematically examined. This study aims to predict success indicators of an intervention programme for convicted intimate-partner violence offenders. The sample consisted of 212 convicted intimate-partner violence offenders who participated in the Contexto Programme. Three "intervention gains" or target criteria were established (increasing the perceived severity of violence, increasing the responsibility assumption for one's actions, and reducing the risk of recidivism). A structural equations model was tested, fitting data appropriately. Participants with major gain in recidivism risk were those who presented lower levels of alcohol consumption, shorter sentences, lower impulsivity, and a higher degree of life satisfaction. The largest gain in perceived severity was found in younger participants, participants with shorter sentences, lower alcohol consumption, higher life satisfaction, higher participation in their community, and higher self-esteem. And, finally, participants with the highest gains in responsibility assumption were older participants, participants who presented higher intimate support, higher anxiety, higher sexism, lower anger control, higher depression, higher impulsivity and higher self-esteem.

Keywords : partner-violence offenders; perceived severity of violence; responsibility assumption; risk of recidivism; structural equations model.

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