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Exploring resilience models in a sample of combat-exposed military service members and veterans: a comparison and commentary

Background: The term resilience is applied in numerous ways in the mental health field, leading to different perspectives of what constitutes a resilient response and disparate findings regarding its prevalence following trauma. Objective: illustrate the impact of various definitions on our understa...

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Detalles Bibliográficos
Autores principales: Sheerin, Christina M., Stratton, Kelcey J., Amstadter, Ananda B., Education, Clinical Center (MIRECC) Workgroup, The VA Mid-Atlantic Mental Illness Research, McDonald, Scott D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Taylor & Francis 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6032017/
https://www.ncbi.nlm.nih.gov/pubmed/29988781
http://dx.doi.org/10.1080/20008198.2018.1486121
Descripción
Sumario:Background: The term resilience is applied in numerous ways in the mental health field, leading to different perspectives of what constitutes a resilient response and disparate findings regarding its prevalence following trauma. Objective: illustrate the impact of various definitions on our understanding and prevalence of resilience, we compared various resilience definitions (absence of PTSD, absence of current mental health diagnosis, absence of generalized psychological distress, and an alternative trauma load–resilience discrepancy model of the difference between actual and predicted distress given lifetime trauma exposure) within a combat-exposed military personnel and veteran sample. Method: In this combat-trauma exposed sample (N = 849), of which approximately half were treatment seeking, rates of resilience were determined across all models, the kappa statistic was used to determine the concordance and strength of association across models, and t-tests examined the models in relation to a self-reported resilience measure. Results: Prevalence rates were 43.7%, 30.7%, 87.4%, and 50.1% in each of the four models. Concordance analyses identified 25.7% (n = 218) considered resilient by all four models (kappa = .40, p < .001). Correlations between models and self-reported resilience were strong, but did not fully overlap. Conclusions:The discussion highlights theoretical considerations regarding the impact of various definitions and methodologies on resilience classifications, links current findings to a systems-based perspective, and ends with suggestions for future research approaches on resilience.