Generalized anxiety disorder (GAD) is a common but serious form of anxiety disorder. Despite this, the rate of GAD recognition in primary care remains low in both Western and Eastern countries. The GAD-7 and GAD-2 were developed to efficiently identify people with GAD, and their reliability and validity have been well-documented in Western countries. The GAD-7 and GAD-2 have also been widely utilized to screen for other anxiety disorders; however, their diagnostic utility has not been fully justified with empirical support, especially in East Asian samples. In this study, we examined the diagnostic sensitivity and specificity of these screening tools for identifying individuals with GAD or other anxiety disorders, and recommended screening cutoff scores for GAD and other anxiety disorders for use in Korea. Based on the rigorous standard suggested by the Quality Assessment of Diagnostic Accuracy Studies-2, a total of 1,157 participants randomly recruited from the community completed the GAD-7, GAD-2, and other anxiety and depression measures in a counter-balanced order. All participants were assessed, and their psychiatric diagnosis confirmed through a structured clinical interview conducted by independent clinicians blinded to the results of the self-report questionnaires. The GAD-7 and GAD-2 both showed excellent reliability and validity. Notably, both the GAD-7 and GAD-2 demonstrated acceptable diagnostic accuracy in detecting GAD with similar recommended cut-off scores as those reported in Western countries, but unacceptable diagnostic accuracy for other anxiety disorders. We conclude that given their brevity, the GAD-7 and GAD-2 can be well-utilized to identify people with GAD for preventative evaluation and treatment in Korea. Use of the GAD-7 and GAD-2 for screening other anxiety disorders should be cautioned.

The Beck Depression Inventory-II (BDI-II) is one of the most widely used depression assessment tools in Korea. However, the psychometric properties and diagnostic cut-off point of the official Korean version of the BDI-II have not yet been reported. This study aims to clarify the psychometric properties and diagnostic utility of the Korean BDI-II. A total of 1,145 clinical and non-clinical Korean adults participated in this study. The BDI-II showed a high level of internal consistency and high correlations with other depression-related measures. Confirmatory factor analysis (CFA) was performed, and a 3-factor model showed the best model fit. To identify the diagnostic utility of the BDI-II, the Quality Assessment of Diagnostic Accuracy Studies 2nd Edition (QUADAS-2) methodology was applied in participant recruitment and research design. Results of ROC curve analysis suggested two optimal cut-off scores, 23 points for detecting major depressive disorder (MDD) (83.3% sensitivity, 86.8% specificity) and 17 points for depressive-related disorder (80.9% sensitivity, 76.4% specificity). To identify the usefulness of the BDI-II as a severity assessment tool or screening tool, a test information curve (TIC) was generated with an Item Response Theory (IRT) analysis. The TIC was flat and plateau-like, indicating its appropriateness as a severity rating tool. Research data supports the BDI-II as a reliable and valid screening tool as well as a severity rating tool in the Korean adult population.

Acknowledging separable factors underlying negative symptoms may lead to better understanding and treatment of negative symptoms in individuals with schizophrenia. The current study aimed to test whether the negative symptoms factor (NSF) of the Positive and Negative Syndrome Scale (PANSS) would be better represented by expressive and experiential deficit factors, rather than by a single factor model, using confirmatory factor analysis (CFA). Two hundred and twenty individuals with schizophrenia spectrum disorders completed the PANSS; subsamples additionally completed the Brief Negative Symptom Scale (BNSS) and the Motivation and Pleasure Scale—Self-Report (MAP-SR). CFA results indicated that the two-factor model fit the data better than the one-factor model; however, latent variables were closely correlated. The two-factor model’s fit was significantly improved by accounting for correlated residuals between N2 (emotional withdrawal) and N6 (lack of spontaneity and flow of conversation), and between N4 (passive social withdrawal) and G16 (active social avoidance), possibly reflecting common method variance. The two NSF factors exhibited differential patterns of correlation with subdomains of the BNSS and MAP-SR. These results suggest that the PANSS NSF would be better represented by a two-factor model than by a single-factor one, and support the two-factor model’s adequate criterion-related validity. Common method variance among several items may be a potential source of measurement error under a two-factor model of the PANSS NSF.