JOURNAL ARTICLE

B - 15 Symptom Presentation on the Neurobehavioral Symptom Inventory and Post Traumatic Stress Disorder Checklist for DSM-5 in a Cohort of Explosive Ordnance Disposal Veterans.

  • Published In: Archives of Clinical Neuropsychology, 2024, v. 39, n. 7. P. 1104 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Tschida, Sherri; Summers, Angela; Chacko, Thomas; Breneman, Charity; Brewster, Ryan; Reck, Lily; Killy, Owen; Allen, Nathanial; Costanzo, Michelle; Reinhard, Matthew 3 of 3

Abstract

This article focuses on symptom differences between Explosive Ordnance Disposal (EOD) veterans and non-EOD veterans as measured by the Neurobehavioral Symptom Inventory (NSI) and the Post Traumatic Stress Disorder Checklist for DSM-5 (PCL-5) at the Washington DC War Related Illness and Injury Study Center (DC-WRIISC) Veterans Administration Medical Center. The study found that while overall symptom severity on the NSI correlated with PTSD symptoms on the PCL-5 in both groups, distinct patterns emerged: cognitive symptoms correlated with specific PTSD symptom clusters differently for EOD and non-EOD veterans. Additionally, stress and resilience factors, measured by the Holmes-Rahe Life Stress Inventory, may contribute to these group differences. These findings suggest unique symptom presentations linked to occupational exposures in EOD veterans, underscoring the importance of tailored diagnostic and treatment approaches.

Additional Information

  • Source:Archives of Clinical Neuropsychology. 2024/10, Vol. 39, Issue 7, p1104
  • Document Type:Abstract
  • Subject Area:Literature and Writing
  • Publication Date:2024
  • ISSN:0887-6177
  • DOI:10.1093/arclin/acae067.176
  • Accession Number:184163467
  • Copyright Statement:Copyright of Archives of Clinical Neuropsychology is the property of Oxford University Press / USA 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.)

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