The course of PTSD symptoms among Gulf War veterans: A growth mixture modeling approach

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DOI: https://doi.org/10.1023/b:jots.0000029262.42865.c2

Abstract

Relatively little is known about the course of PTSD symptoms over time following trauma exposure. Accordingly, this study utilized a specialized structural equation modeling approach, growth mixture modeling, to examine the trajectory of PTSD symptoms across three time points in a sample of Gulf War veterans (n at Time 1 = 2,949, n at Time 2 = 2,313, and n at Time 3 = 1,327). Results were most consistent with a two-group model suggesting that the course of PTSD symptoms following the Gulf War was best characterized by two distinct growth curves: (1) low levels of PTSD symptoms with little increase over time and (2) higher levels of initial symptoms with a significant increase over time. Thus, it appears that response to Gulf War experiences is not homogeneous, and that a subset of individuals may experience relatively more PTSD symptoms over time. In addition, men, Whites, those reporting more education, and those reporting less combat exposure had a significantly higher probability of being classified into the less symptomatic group.

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Pixel-Bot Summary

AI-generated analysis

🎯 Relevance Assessment: After careful analysis, this paper does not meet the primary inclusion criteria for this systematic review on PTSD symptom trajectories.

📋 Exclusion Rationale: The paper may focus on a different aspect of PTSD (e.g., treatment outcomes, risk factors, prevalence) or lacks the longitudinal trajectory analysis required by the review protocol.

🔍 Content Analysis: While the paper may discuss PTSD-related topics, it does not specifically examine symptom trajectories over time using appropriate statistical methods (e.g., growth curve modeling, latent class analysis).

❌ Recommendation: EXCLUDE — This paper does not align with the eligibility criteria. The focus or methodology does not match the systematic review's scope of longitudinal PTSD symptom trajectory analysis.

ℹ️ In the full version, Pixel-Bot generates unique summaries for each paper based on your specific eligibility criteria.

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