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Latent profile analysis of veterans' psychological risk and protective factors: Associations with nonroutine military
Nicholas Barr1, Andrea Corral Rodríguez1
1School of Social Work, University of Nevada, Las Vegas.
Objective:
Veterans with nonroutine military discharge (NRD) often have worse psychosocial outcomes than their routinely discharged peers. However, little is known about how subgroups of veterans vary regarding risk and protective factors like PTSD, depression, self-stigma of mental illness, mindfulness, and self-efficacy, and how subgroup characteristics relate to discharge status. We applied person-centered models to detect latent profiles and associations with NRD.
Method:
A total of 485 post-9/11 era veterans completed online surveys, and a sequence of latent profile (LPA) models were fitted to the data and examined for parsimony, profile separation, and substantive utility. Following LPA model selection, we applied a series of models to analyze demographic predictors of latent profile membership and associations between latent profiles and the outcome NRD.
Results:
LPA model comparison supported a 5-profile solution for the data. We identified a Self-Stigmatized (SS) profile comprising 26% of the sample with mindfulness and self-efficacy below full sample averages and self-stigma, PTSD, and depressive symptoms above sample averages. Those in the SS profile were significantly more likely than a profile approximating full sample averages on indicators to report nonroutine discharge (OR = 2.42, 95% CI [1.15, 5.10]).
Conclusions:
Meaningful subgroups were present in this sample of post-9/11 service-era military veterans with respect to psychological risk and protective factors. The SS profile had over 10 times the odds of nonroutine discharge compared to the Average profile. Findings suggest that veterans most in need of mental health treatment face external barriers from nonroutine discharge and an internal stigma barrier to obtaining care. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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