Traumatic brain injury and suicidality among military veterans: The mediating role of social integration

Schafer, Katherine Musacchio; Wilson, Emma; Joiner, Thomas E.

doi:10.1016/j.jad.2023.06.047

Cited by 3 publications

(2 citation statements)

References 69 publications

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“…Third, nine of the 15 judgment variables were involved in mediation or moderation with contextual variables. Age, employment, and perceived loneliness mediated or moderated relationships with distinct judgment variables to model anxiety scores, consistent with other reports regarding the relationship of cognitive science measures and contextual variables [65][66][67][71][72][73][74][75] . Fourth, all contextual variables exhibited significant differences in anxiety scores, and 11 of the 15 judgment variables differed when assessed for median shifts across 'higher' and 'lower' anxiety groups, indicating that a constellation of judgment alterations are predictive of anxiety levels.…”

Section: Article Discussionsupporting

confidence: 88%

“…From a psychological perspective, these findings demonstrate how context (e.g., age, employment status, perceived loneliness) modulates or interacts with judgment variables to model anxiety, and how these relationships between judgment and context may aide the assessment of anxiety and ultimately, other mental health conditions. Others have noted that psychological processes occur in a context, and this study supports their work [65][66][67][71][72][73][74][75] .…”

Section: Article Discussionsupporting

confidence: 88%

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A novel approach to anxiety level prediction using small sets of judgment and survey variables

Bari,

Kim,

Vike

et al. 2024

npj Mental Health Res

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Anxiety, a condition characterized by intense fear and persistent worry, affects millions each year and, when severe, is distressing and functionally impairing. Numerous machine learning frameworks have been developed and tested to predict features of anxiety and anxiety traits. This study extended these approaches by using a small set of interpretable judgment variables (n = 15) and contextual variables (demographics, perceived loneliness, COVID-19 history) to (1) understand the relationships between these variables and (2) develop a framework to predict anxiety levels [derived from the State Trait Anxiety Inventory (STAI)]. This set of 15 judgment variables, including loss aversion and risk aversion, models biases in reward/aversion judgments extracted from an unsupervised, short (2–3 min) picture rating task (using the International Affective Picture System) that can be completed on a smartphone. The study cohort consisted of 3476 de-identified adult participants from across the United States who were recruited using an email survey database. Using a balanced Random Forest approach with these judgment and contextual variables, STAI-derived anxiety levels were predicted with up to 81% accuracy and 0.71 AUC ROC. Normalized Gini scores showed that the most important predictors (age, loneliness, household income, employment status) contributed a total of 29–31% of the cumulative relative importance and up to 61% was contributed by judgment variables. Mediation/moderation statistics revealed that the interactions between judgment and contextual variables appears to be important for accurately predicting anxiety levels. Median shifts in judgment variables described a behavioral profile for individuals with higher anxiety levels that was characterized by less resilience, more avoidance, and more indifference behavior. This study supports the hypothesis that distinct constellations of 15 interpretable judgment variables, along with contextual variables, could yield an efficient and highly scalable system for mental health assessment. These results contribute to our understanding of underlying psychological processes that are necessary to characterize what causes variance in anxiety conditions and its behaviors, which can impact treatment development and efficacy.

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Section: Article Discussionsupporting

confidence: 88%

Section: Article Discussionsupporting

confidence: 88%

A novel approach to anxiety level prediction using small sets of judgment and survey variables

Bari,

Kim,

Vike

et al. 2024

npj Mental Health Res

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Predicting suicidality with small sets of interpretable reward behavior and survey variables

Lalvani,

Bari,

Vike

et al. 2024

Nat. Mental Health

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The prediction of suicidal thought and behavior has met with mixed results. This study of 3,476 de-identified participants (4,019 before data exclusion) quantified the prediction of four suicidal thought and behavior (STB) variables using a short reward/aversion judgment task and a limited set of demographic and mental health surveys. The focus was to produce a simple, quick and objective framework for assessing STB that might be automatable, without the use of big data. A balanced random forest classifier performed better than a Gaussian mixture model and four standard machine learning classifiers for predicting passive suicide ideation, active suicide ideation, suicide planning and planning for safety. Accuracies ranged from 78% to 92% (optimal area under the curve between 0.80 and 0.95) without overfitting, and peak performance was observed for predicting suicide planning. The relative importance of features for prediction showed distinct weighting across judgment variables, contributing between 40% and 64% to prediction per Gini scores. Mediation/moderation analyses showed that depression, anxiety, loneliness and age variables moderated the judgment variables, indicating that the interaction of judgment with mental health and demographic indices is fundamental for the high-accuracy prediction of STB. These findings suggest the feasibility of an efficient and highly scalable system for suicide assessment, without requiring psychiatric records or neural measures. The findings suggest that STB might be understood within a cognitive framework for judgment with quantitative variables whose unique constellation separates passive and active suicidal thought (ideation) from suicide planning and planning for safety.

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Loneliness partially mediates the relation between substance use and suicidality in Veterans

Schafer,

Wilson-Lemoine,

Campione

et al. 2024

Military Psychology

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Traumatic brain injury and suicidality among military veterans: The mediating role of social integration

Cited by 3 publications

References 69 publications

A novel approach to anxiety level prediction using small sets of judgment and survey variables

A novel approach to anxiety level prediction using small sets of judgment and survey variables

Predicting suicidality with small sets of interpretable reward behavior and survey variables

Loneliness partially mediates the relation between substance use and suicidality in Veterans

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