scholarly journals Initial validation of brief measures of suicide risk factors: Common data elements used by the Military Suicide Research Consortium.

2018 ◽  
Vol 30 (6) ◽  
pp. 767-778 ◽  
Author(s):  
Fallon B. Ringer ◽  
Kelly A. Soberay ◽  
Megan L. Rogers ◽  
Christopher R. Hagan ◽  
Carol Chu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Suicide Risk ◽  
Common Data Elements ◽  
Initial Validation ◽  
Suicide Risk Factors ◽  
Research Consortium ◽  
Data Elements ◽  
Military Suicide ◽  
The Military

scholarly journals The Military Suicide Research Consortium Common Data Elements: An Examination of Measurement Invariance Across Current Service Members and Veterans

Assessment ◽  
2018 ◽  
Vol 26 (6) ◽  
pp. 963-975 ◽  
Author(s):  
Ian H. Stanley ◽  
Jennifer M. Buchman-Schmitt ◽  
Carol Chu ◽  
Megan L. Rogers ◽  
Anna R. Gai ◽  
...  
Keyword(s):  
Measurement Invariance ◽  
Model Fit ◽  
Service Members ◽  
Common Data Elements ◽  
Adequate Model ◽  
Increased Risk ◽  
Research Consortium ◽  
Data Elements ◽  
Military Suicide ◽  
The Military

Suicide rates within the U.S. military are elevated, necessitating greater efforts to identify those at increased risk. This study utilized a multigroup confirmatory factor analysis to examine measurement invariance of the Military Suicide Research Consortium Common Data Elements (CDEs) across current service members ( n = 2,015), younger veterans (<35 years; n = 377), and older veterans (≥35 years; n = 1,001). Strong factorial invariance was supported with adequate model fit observed for current service members, younger veterans, and older veterans. The structures of all models were generally comparable with few exceptions. The Military Suicide Research Consortium CDEs demonstrate at least adequate model fit for current military service members and veterans, regardless of age. Thus, the CDEs can be validly used across military and veteran populations. Given similar latent structures, research findings in one group may inform clinical and policy decision making for the other.

The Nature and Structure of the Military Suicide Research Consortium’s Common Data Elements

2020 ◽  
pp. 1-10
Author(s):  
Anna R. Gai ◽  
Fallon Ringer ◽  
Katherine Schafer ◽  
Sean Dougherty ◽  
Matthew Schneider ◽  
...  
Keyword(s):  
Common Data Elements ◽  
Data Elements ◽  
Military Suicide ◽  
The Military

Machine Learning to Classify Suicidal Thoughts and Behaviors: Implementation Within the Common Data Elements Used by the Military Suicide Research Consortium

2021 ◽  
pp. 216770262096106
Author(s):  
Andrew K. Littlefield ◽  
Jeffrey T. Cooke ◽  
Courtney L. Bagge ◽  
Catherine R. Glenn ◽  
Evan M. Kleiman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Approaches ◽  
Classification Models ◽  
Linear Modeling ◽  
Common Data Elements ◽  
And Behaviors ◽  
Data Elements ◽  
Military Suicide ◽  
The Military ◽  
Suicidal Thoughts And Behaviors

Suicide rates among military-connected populations have increased over the past 15 years. Meta-analytic studies indicate prediction of suicide outcomes is lacking. Machine-learning approaches have been promoted to enhance classification models for suicide-related outcomes. In the present study, we compared the performance of three primary machine-learning approaches (i.e., elastic net, random forests, stacked ensembles) and a traditional statistical approach, generalized linear modeling (i.e., logistic regression), to classify suicide thoughts and behaviors using data from the Military Suicide Research Consortium’s Common Data Elements (CDE; n = 5,977–6,058 across outcomes). Models were informed by (a) selected items from the CDE or (b) factor scores based on exploratory and confirmatory factor analyses on the selected CDE items. Results indicated similar classification performance across models and sets of features. In this study, we suggest the need for robust evidence before adopting more complex classification models and identify measures that are particularly relevant in classifying suicide-related outcomes.

Sign in / Sign up

Export Citation Format

Share Document