Intolerance of uncertainty on distress and impairment: The mediating role of repetitive negative thinking

Abstract: Repetitive negative thinking and intolerance of uncertainty are risk and maintenance factors for emotional disorders. Although emerging evidence suggests that intolerance of uncertainty predicts increases in distress through repetitive negative thinking, these relationships have yet to be investigated among veterans. The present study examines if repetitive negative thinking mediates the relationships of intolerance of uncertainty with stress, disordered symptoms and impairment among a mixed clinical sample of veterans. Two hundred and forty-four treatment-seeking veterans with diagnoses of major depressive disorder, panic disorder, or posttraumatic stress disorder completed measures of intolerance of uncertainty, repetitive negative thinking, stress, impairment, depression, panic, and posttraumatic stress prior to receiving treatment. Mediation models revealed indirect effects of intolerance of uncertainty through repetitive negative thinking on stress and impairment in the full sample, and on disordered symptoms in subsamples with major depressive disorder and posttraumatic stress disorder. Conversely, intolerance of uncertainty did not have direct or indirect effects on disordered symptoms in a panic disorder subsample. Findings suggest that repetitive negative thinking and intolerance of uncertainty uniquely contribute to stress, impairment, and disordered symptoms, but repetitive negative thinking, may, in part, drive intolerance of uncertainty's contribution to emotional disorders. Interventions for repetitive negative thinking might improve the efficacy of existing transdiagnostic treatment protocols. Cross-sectional data is a limitation of the present study. Prospective designs in civilian samples can better establish the temporality of these relationships and if they are generalizable to the larger population.

Read the full article
Report a problem with this article

Related articles

  • More for Researchers

    Identifying opioid relapse during COVID-19 using natural language processing of nationwide Veterans Health Administration electronic medical record data

    Abstract: Novel and automated means of opioid use and relapse risk detection are needed. Unstructured electronic medical record data, including written progress notes, can be mined for clinically relevant information, including the presence of substance use and relapse-critical markers of risk and recovery from opioid use disorder (OUD). In this study, we used natural language processing (NLP) to automate the extraction of opioid relapses, and the timing of these occurrences, from veteran patients' electronic medical record. We then demonstrated the utility of our NLP tool via analysis of pre-/post-COVID-19 opioid relapse trends among veterans with OUD. For this demonstration, we analyzed data from 107,606 veterans OUD enrolled in Veterans Health Administration, comparing a pandemic-exposed cohort (n = 53,803; January 2019-March 2021) to a matched prepandemic cohort (n = 53,803; October 2017-December 2019). The recall of our NLP tool was 75% and our precision was 94%, demonstrating moderate sensitivity and excellent specificity. Using the NLP tool, we found that the odds of opioid relapse postpandemic onset were proportionally higher compared to prepandemic trends, despite patients having fewer mental health encounters from which to derive instances of relapse postpandemic onset. In this research application of the tool, and as hypothesized, we found that opioid relapse risk was elevated postpandemic. The application of NLP Methods: to identify and monitor relapse risk holds promise for future surveillance, risk prevention, and clinical outcome research.