SAFEGUARD: Transforming Military Suicide Prevention Through Predictive Analytics and Targeted Interventions

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Authors: Thomas H. Nassif, Amy B. Adler, Robert D. Atkinson, Kate H. Bentley, Bonomi Robin, Jenny D’Olympia, Emily R. Edwards, Rebecca G. Fortgang, Brandon A. Gaudiano, Joseph C. Geraci, Marianne S. Goodman, Daniel B. Herman
+10 more Evan M. Kleiman, Sarah Maggio, Allison McDaniel, James A. Naifeh, Matthew K. Nock, Christopher M. Paine, Timothy Regan, John Torous, Vincent F. Capaldi, Ronald C. Kessler
Cited by: 1
References: 79

Abstract

PURPOSE OF REVIEW: Machine learning predictive modeling can support scalable prevention of suicide-related behavior (SRB). SAFEGUARD is a three-pronged universal, indicated, and clinical SRB-prevention intervention system focused on key military career touchpoints. RECENT FINDINGS: The targeted SAFEGUARD interventions are designed to improve on the mixed results of universal interventions. Level Up uses digital tools, personalized messaging, and remote booster sessions to deliver customized universal military-focused cognitive behavioral therapy skills training designed to reduce SRBs during first duty assignments. Operation Life Force delivers remote group dialectical behavior therapy skills training with a mental toughness focus to soldiers identified during annual physicals as high-risk for SRBs. Pathfinding delivers remote wrap-around case management after psychiatric inpatient discharge to soldiers identified as high-risk for SRBs. SAFEGUARD is a data-driven system for SRB prevention that delivers targeted best-practice interventions at critical points to optimize impact and efficiently use mental health resources across the military.

Fields of Study & Subjects

Suicide and Self-Harm Studies Mental Health via Writing Digital Mental Health Interventions

Publication details

Journal / source
Current Psychiatry Reports
Publisher
Springer Science and Business Media LLC
Volume
28
Issue
1
Publication date
27 February 2026
Language
EN