Investigating the psychometric properties and measurement invariance of the forms of self-criticizing/attacking and self-reassuring scale in Veteran men and women

Abstract: Self-criticism, or negative self-evaluation characterized by often unrealistic personal standards and a harsh self-view, is a relevant transdiagnostic construct for mental health. Yet, the psychometric properties of scales assessing self-criticism have not been examined in military veterans, a population with a high burden of psychiatric symptoms. The present study tested the factor structure of the full and short-form versions of the Forms of Self-Criticizing/Attacking and Self-Reassuring Scale (FSCRS and FSCRS-SF), a measure of self-criticism, in a sample of 1,161 United States veterans who completed this and other measures as part of a larger study. Measurement invariance across men and women and associations with mental health symptoms, disordered eating, substance abuse, and self-directed violence were also examined. We found adequate support for two- and three-factor models and improved model fit with correlated residuals. Given theoretical and model fit considerations, measurement invariance was tested with the three-factor model. Both the FSCRS and FSCRS-SF were invariant across men and women. Women scored higher than men on the Hated Self (HS) and Inadequate Self (IS) subscales. HS and IS subscales scores were positively correlated with mental health constructs, whereas Reassured Self (RS) subscale scores were negatively correlated with these constructs. Overall, both the FSCRS and FSCRS-SF, when a briefer scale is preferred, appear appropriate for use in veterans. There are important gender differences in self-criticism that warrant further investigation; however, scores on this measure can be meaningfully compared across men and women. Validation of the FSCRS in veterans may encourage additional research on self-criticism in this 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.