BackgroundSelf-report multiple choice questionnaires have been widely utilized to quantitatively measure one’s personality and psychological constructs. Despite several strengths, self-report multiple choice questionnaires have considerable limitations in nature. With the rise of machine learning and Natural language processing, researchers in the field of psychology are widely adopting NLP to assess psychological construct to predict human behaviors. However, there is a lack of connections between the work being performed in computer science and that of psychology due to small data sets and (...) unvalidated modeling practices.AimsThe current article introduces the study method and procedure of phase II which includes the interview questions for the five-factor model of personality developed in phase I. This study aims to develop the interview and open-ended questions for the FFM-based personality assessments, specifically designed with experts in the field of clinical and personality psychology, and to collect the personality-related text data using the interview questions and self-report measures on personality and psychological distress. The purpose of the study includes examining the relationship between natural language data obtained from the interview questions, measuring the FFM personality constructs, and psychological distress to demonstrate the validity of the natural language-based personality prediction.MethodsPhase I study was conducted to fifty-nine native Korean adults to acquire the personality-related text data from the interview and open-ended questions based on the FFM of personality. The interview questions were revised and finalized with the feedback from the external expert committee, consisting of personality and clinical psychologists. Based on the established interview questions, a total of 300 Korean adults will be recruited using a convenience sampling method via online survey. The text data collected from interviews will be analyzed using the natural language processing. The results of the online survey including demographic data, depression, anxiety, and personality inventories will be analyzed together in the model to predict individuals’ FFM of personality and the level of psychological distress. (shrink)

Generalized anxiety disorder can cause significant socioeconomic burden and daily life dysfunction; hence, therapeutic intervention through early detection is important. This study was the final stage of a 3-year anxiety screening tool development project that evaluated the psychometric properties and diagnostic screening utility of the Mental Health Screening Tool for Anxiety Disorders, which measures GAD. A total of 527 Koreans completed online and offline versions of the MHS: A, Beck Anxiety Inventory, Generalized Anxiety Disorder-7, and Penn State Worry Questionnaire. The (...) participants had an average age of 38.6 years and included 340 females. Participants were also administered the Mini-International Neuropsychiatric Interview. Internal consistency, convergent/criterion validity, item characteristics, and test information were assessed based on the item response theory, and a factor analysis and cut-off score analyses were conducted. The MHS: A had good internal consistency and good convergent validity with other anxiety scales. The two versions of the MHS: A were nearly identical. It had a one-factor structure and showed better diagnostic accuracy for GAD detection than the GAD-7 and BAI. The IRT analysis indicated that the MHS: A was most informative as a screening tool for GAD. The MHS: A can serve as a clinically useful screening tool for GAD in Korea. Furthermore, it can be administered both online and offline and can be flexibly used as a brief mental health screener, especially with the current rise in telehealth. (shrink)

Background: An association between gaming disorder and the symptoms of common mental disorders is unraveled yet. In this preregistered study, we quantitatively synthesized reliability, convergent and discriminant validity of GD scales to examine association between GD and other constructs.Methods: Five representative GD instruments were chosen based on recommendations by the previous systematic review study to conduct correlation meta-analyses and reliability generalization. A systematic literature search was conducted through Pubmed, Proquest, Embase, and Google Scholar to identify studies that reported information on (...) either reliability or correlation with related variables. 2,124 studies were full-text assessed as of October 2020, and 184 were quantitatively synthesized. Conventional Hedges two-level meta-analytic method was utilized.Results: The result of reliability generalization reported a mean coefficient alpha of 0.86 and a mean test-retest estimate of 0.86. Estimated effect sizes of correlation between GD and the variables were as follows: 0.33 with depression, 0.29 with anxiety, 0.30 with aggression, –0.22 with quality of life, 0.29 with loneliness, 0.56 with internet addiction, and 0.40 with game playtime, respectively. The result of moderator analyses, funnel and forest plots, and publication bias analyses were also presented.Discussion and Conclusion: All five GD instruments have good internal consistency and test-retest reliability. Relatively few studies reported the test-retest reliability. The result of correlation meta-analysis revealed that GD scores were only moderately associated with game playtime. Common psychological problems such as depression and anxiety were found to have a slightly smaller association with GD than the gaming behavior. GD scores were strongly correlated with internet addiction. Further studies should adopt a rigorous methodological procedure to unravel the bidirectional relationship between GD and other psychopathologies.Limitations: The current study did not include gray literature. The representativeness of the five tools included in the current study could be questioned. High heterogeneity is another limitation of the study.Systematic Review Registration: [https://www.crd.york.ac.uk/PROSPERO/], identifier [CRD42020219781]. (shrink)