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VetStar: Justice-Involved Veterans Successful Mental Health Diversion Cover

VetStar: Justice-Involved Veterans Successful Mental Health Diversion

Open Access
|Jan 2022

Abstract

Justice-involved veterans are a unique subpopulation with different mental health needs in pre-trial diversion than civilians. Veteran diversion can be challenging, creating evaluation issues at the program (i.e., development, implementation, outcomes), and individual (i.e., positive outcomes) levels. A case study of VetStar, a unified approach diversion for veterans spanning both the criminal justice and healthcare systems, is presented as a model for diversion of justice-involved veterans. The researchers obtained data from five publicly available sources: recorded radio interviews, VetStar published stakeholder reports, the VetStar Texas Veterans & Family Alliance 2018 grant submission, a photovoice project from the Lubbock Art Walk November 2019, and the Texas Veterans Commission Quarterly Reports and JMHCP Data Review. This case study found 10 major themes. VetStar is a non-profit organization using a modified version of assertive community treatment serving justice-involved veterans with mental health issues. We describe the progression of justice-involved veterans in the VetStar diversion program; 92% of the 571 veterans completing the VetStar program in five years did not re-offend for the same crime. VetStar’s approach could be performed in other subpopulations with distinct cultural identities. Modifications can be constructed and aligned within the construct of the subpopulation identity. Further research is needed to investigate VetStar’s FASTRR model, to help document a more detailed theory of the approach.

Language: English
Page range: 1 - 14
Submitted on: Feb 26, 2021
Accepted on: Aug 2, 2021
Published on: Jan 17, 2022
Published by: Virginia Tech Publishing
In partnership with: Paradigm Publishing Services

© 2022 Kevin B. Gittner, Lisaann S. Gittner, Jeff A. Dennis, Lauren M. Matheny, published by Virginia Tech Publishing
This work is licensed under the Creative Commons Attribution 4.0 License.