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Rocky Mountain MIRECC for Veteran Suicide Prevention - Lauren Borges, PhD

Rocky Mountain MIRECC for Veteran Suicide Prevention

Updated: 6 December 2018

Biography

Lauren M. Borges, PhD
Title: Clinical/Research Psychologist
Contact:
720-723-4865
lauren.borges2@va.gov
 
Lauren Borges earned her Ph.D. in Clinical Psychology from Western Michigan University in 2016 following the completion of a clinical internship at the VA Maryland Healthcare System/University of Maryland School of Medicine as a VA trauma track intern. Her research focuses on the role of emotion regulation in PTSD, suicidal behavior, and personality psychopathology. She is specifically interested in applying functional contextual interventions to the treatment of shame in these individuals. Dr. Borges joined the Rocky Mountain MIRECC as a Postdoctoral Fellow in September of 2016.

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Publications

Barnes SM, Monteith LL, Forster JE, Nazem S, Borges LM, Stearns-Yoder KA, Bahraini NH. Developing Predictive Models to Enhance Clinician Prediction of Suicide Attempts Among Veterans With and Without PTSD. Suicide Life Threat Behav. 2018 Sep 11. doi: 10.1111/sltb.12511. [Epub ahead of print] PubMed PMID: 30206955.
The limitations of self-report confine clinicians' ability to accurately predict suicides and suicide attempts (SAs). Behavioral assessments (e.g., Death Implicit Association Test [IAT]) may be a means of supplementing self-report and clinician prediction. OBJECTIVE: The authors aimed to build and test a predictive model of SAs that included established risk factors and measures of suicide risk, and Death IAT scores. The authors also sought to test the predictive validity of the SA model among subgroups of Veterans with and without PTSD. METHOD: Participants included 166 psychiatrically hospitalized Veterans. RESULTS: A model that included patient prediction, age, and Death IAT scores improved upon clinician prediction of SAs during the six-month follow-up (C-statistic for clinician prediction = 73.6, 95% CI [62.9, 84.4] and C-statistic for model = 82.8, 95% CI [74.6, 91.0]). The model was tested in subgroups of Veterans with and without PTSD. Among Veterans without PTSD, the Death IAT and patient prediction predicted SAs above and beyond clinician prediction, while these variables did not significantly improve prediction among Veterans with PTSD (C-statistic for no-PTSD = 91.3, 95% CI [80.6, 1.00]; C-statistic for PTSD = 86.8, 95% CI [76.8, 96.8]). Building a separate model for Veterans with PTSD did not improve upon clinician prediction. CONCLUSIONS: Findings indicate that predictive models may bolster clinician prediction of SAs and that predictors may differ for Veterans with PTSD. Published 2018. This article is a U.S. Government work and is in the public domain in the USA.
Barnes, S.M., Monteith, L.L., Forster, J.E., Nazem, S., Borges, L.M., Stearns-Yoder, K.A., & Bahraini, N.H. (2018). Developing predictive models to enhance clinician prediction of suicide attempts among veterans with and without PTSD. Suicide and Life-Threatening Behavior. https://doi.org/10.1111/sltb.12511
The limitations of self-report confine clinicians' ability to accurately predict suicides and suicide attempts (SAs). Behavioral assessments (e.g., Death Implicit Association Test [IAT]) may be a means of supplementing self-report and clinician prediction. OBJECTIVE: The authors aimed to build and test a predictive model of SAs that included established risk factors and measures of suicide risk, and Death IAT scores. The authors also sought to test the predictive validity of the SA model among subgroups of Veterans with and without PTSD. METHOD: Participants included 166 psychiatrically hospitalized Veterans. RESULTS: A model that included patient prediction, age, and Death IAT scores improved upon clinician prediction of SAs during the six-month follow-up (C-statistic for clinician prediction = 73.6, 95% CI [62.9, 84.4] and C-statistic for model = 82.8, 95% CI [74.6, 91.0]). The model was tested in subgroups of Veterans with and without PTSD. Among Veterans without PTSD, the Death IAT and patient prediction predicted SAs above and beyond clinician prediction, while these variables did not significantly improve prediction among Veterans with PTSD (C-statistic for no-PTSD = 91.3, 95% CI [80.6, 1.00]; C-statistic for PTSD = 86.8, 95% CI [76.8, 96.8]). Building a separate model for Veterans with PTSD did not improve upon clinician prediction. CONCLUSIONS: Findings indicate that predictive models may bolster clinician prediction of SAs and that predictors may differ for Veterans with PTSD. Published 2018. This article is a U.S. Government work and is in the public domain in the USA.
 
Borges, L. M., and Naugle, A. E. (2017) The role of emotion regulation in predicting personality dimensions. Personality and Mental Health, doi: 10.1002/pmh.1390.
Dimensional models of personality have been widely acknowledged in the field as alternatives to a trait-based system of nomenclature. While the importance of dimensional models has been established, less is known about the constructs underlying these personality dimensions. Emotion regulation is one such potential construct. The goal of the current study was to examine the relationship between personality dimensions and emotion regulation. More specifically, the predictive capacity of emotion regulation in accounting for personality dimensions and symptoms on the Schedule for Nonadaptive and Adaptive Personality-2 above and beyond a measure of general distress was evaluated. Emotion regulation was found to be predictive of most personality dimensions and symptoms of most personality disorders. Consistent with hypotheses, emotion regulation variables associated with undercontrol of emotions were most predictive of traits associated with Cluster B personality disorders whereas Cluster A and C traits were most associated with emotion regulation related to overcontrol of emotions. These findings provide preliminary evidence that some personality dimensions never assessed in relation to emotion regulation are strongly predicted by emotion regulation variables. Thus, the present study facilitates an initial step in understanding the relationship between personality dimensions and a multidimensional model of emotion regulation. Copyright © 2017 John Wiley & Sons, Ltd.
 
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