The Veteran's Identity Journey: A Qualitative Exploration Through Social Identity Model of Identity Change.
Published In: Journal of Community & Applied Social Psychology, 2025, v. 35, n. 2. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Sharma, Aditi; Hussain, Dilwar 3 of 3
Abstract
The current study delves into the transition faced by military veterans upon their retirement from the armed forces. Retirees encounter various difficulties, primarily revolving around shifts in group dynamics, alterations in roles and responsibilities and adapting to civilian life. Rooted in the Social Identity Model of Identity Change (SIMIC), which posits that group identification can mitigate threats to well‐being during life transitions, we explored the relevance of this model to the context of military retirement. Through semi‐structured interviews with 17 retired veterans, we employed reflexive thematic analysis to investigate SIMIC's pathways. Our findings underscored the significance of identity continuity and gain pathways, which either posed challenges to veterans' sense of identity or facilitated their adjustment process. The compatibility between the two pathways also played a crucial role in facilitating the adjustment process. This qualitative validation of the SIMIC model sheds light on the unique experiences of veterans transitioning from military to civilian life. Please refer to the Supporting Information section to find this article's Community and Social Impact Statement. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Journal of Community & Applied Social Psychology. 2025/03, Vol. 35, Issue 2, p1
- Document Type:Article
- Subject Area:Political Science
- Publication Date:2025
- ISSN:1052-9284
- DOI:10.1002/casp.70062
- Accession Number:184045290
- Copyright Statement:Copyright of Journal of Community & Applied Social Psychology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Looking to go deeper into this topic? Look for more articles on EBSCOhost.