Development of Violence Across the Lifespan: A Preliminary Model
<p>Violence has serious implications for both the victim and the wider community. The current adult rehabilitation programmes accept violent offenders ranged from 20 years and older. This age range could have serious rehabilitation consequences, as a twenty year olds violence and violence related goals may differ substantially to a 70 year old. For this reason an understanding of the development of violence and violence related goals can aide rehabilitation and punitive policies. A review of recent research highlights there are many methodological and empirical gaps in the development of violence whereby the current research aimed to assuage this issue. The current research used grounded theory to develop a model on the development of violence over the life-course. For this research twelve men currently incarcerated at Rimutaka Prison in a violence rehabilitation unit were interviewed. This method developed two models. The “Influences on violence development” model outlines how environment and personal choices had an impact on the development of violence. The “development of violence” model outlines the increasing severity and frequency of violence over time, and the increasing complexity of violence related goals. This model is nested within the influences on violence development model. Comparing the current models to Loeber et al's (1993) pathways model, and Sampson and Laub's life-course perspective on offending, has found support for both models. Thus this model's theoretical value lies within its ability to draw together other areas of research and provide a holistic understanding of both how and why violence develops. One implication of these models is the understanding of the varying influences of environment on violence, upon both different individuals and different ages. This implies that rehabilitation should perhaps follow a more individual based focus. There are many limitations to the research, the most salient one being lack of saturation in the model and low sample size.</p>