Out With The Old, In With The New: A Nuanced Approach To Self-Stigma Among Veterans

• Main Authors: Derrick Alan Kranke, Shant Barmak, Aram Dobalian
• Format: Article
• Language: English
• Published: Virginia Tech Libraries 2019-03-01
• Series: Journal of Veterans Studies
• Subjects: veterans, self-stigma, mental illness, identity
• Online Access: https://journal-veterans-studies.org/articles/78

Self-stigma can generate significant public health costs because of its implications concerning underutilization of treatment. Veterans are particularly vulnerable to the effects of self-stigma because of the military’s value of self-reliance, which may compound negative feelings toward receiving treatment. Few intervention studies to reduce veteran self-stigma apply the Corrigan and Watson (2002) model of self-stigma to the veteran experience. Doing so may yield additional insight into factors driving self-stigma. Therefore, the purpose of this article is to apply this model to veterans from recent generations of war. The authors conducted a constant-comparative method between the Corrigan and Watson self-stigma model and a variation of a stigma model among military personnel with mental illness to identify similarities, differences, and any unaccounted factors. Contextual factors, such as identify development, military cultural values, and family concerns, that have been noted in recent research as factors impacting stigma, were not accounted for in the Corrigan and Watson model. The proposed model encompasses those nuanced contextual factors. The newly proposed model and underlying reasons for behaviors differed from the Corrigan and Watson self-stigma framework regarding the following components: 1) resistance; 2) differentiate; and 3) protect. The model is meant to disseminate novel issues that compound the effects of veteran self-stigma. Future research needs to assess the adequacy of this model, and if it is consistent with the lived experience of veterans, develop interventions that target the corresponding issues within each component.