Predicting symptom clusters of post-traumatic stress disorder among combatants of the Ukrainian Air Assault Forces

Author:

Mokrousova A.A.1ORCID,Yatsenko N.V.1ORCID,Hrytsai D.V.2ORCID

Affiliation:

1. Municipal Non Commercial Enterprise of Kyiv Oblast Council «Kyiv Regional Center for Mental Health», Vorzel, Ukraine; Taras Shevchenko National University of Kyiv of the Ministry of Education and Science of Ukraine, Kyiv, Ukraine

2. Municipal Non Commercial Enterprise of Kyiv Oblast Council «Kyiv Regional Center for Mental Health», Vorzel, Ukraine

Abstract

Background. This study addresses the urgent need for refined diagnostic models for post-traumatic stress disorder (PTSD), particularly tailored to the experiences of the Ukrainian Air Assault Forces (AAFU). Authors hypothesise that integrating contextual factors into PTSD assessments could significantly improve the accuracy of predicting symptom clusters, thereby optimising rehabilitation programmes planning. Purpose. This study aimed to develop a predictive model of PTSD symptom clusters among AAFU combatants, focusing on the influence of personal contextual factors on mental health outcomes. Materials and Methods. A cross-sectional study involving 216 male AAFU combatants at the Kyiv Regional Center for Mental Health was conducted. Participants underwent pre-diagnostic examinations including psychological questioning and clinical interviews. Data analysis was performed using Structural Equation Modelling (SEM) with the Diagonally Weighted Least Squares (DWLS) estimator in R software (version 4.3.2). Statistical significance was set at p < 0.05, with model fit indices set at CFI and TLI > 0.95, RMSEA < 0.08 and SRMR < 0.05. Results. The SEM models revealed significant predictive value of personal and combat-related factors for the intensity of PTSD symptom clusters such as avoidant, depressive, vigilant, and intrusive. Specifically, factors like deployment duration and combat-related brain injuries had a moderate effect on clusters related to avoidance, negative cognition and mood, and arousal, with less impact on re-experiencing symptoms. Social support emerged as a protective factor in the model. The model exhibited robust fit, evidenced by CFI = 0.999, TLI = 0.997, RMSEA = 0.033 and SRMR = 0.048. Conclusions. Incorporating contextual factors into the diagnostic models of PTSD underlines the value of pre-diagnostic evaluations at mental health services. This methodological shift could lead to the creation of timely and appropriately tailored treatment plans, addressing both the limited durations of rehabilitation programs and the specific needs of combatants based on their personal and combat experiences.

Publisher

V. N. Karazin Kharkiv National University

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