Factors associated with burnout among public hospital nurses in South Korea: A Job Demands-Resources perspective.

Major depressive disorder (MDD) is a leading cause of disability worldwide, accounting for approximately one-third of years lived with disability from mental disorders, and its incidence has increased by approximately 60% between 1990 and 2019 [,]. The clinical course and treatment response in MDD are highly variable, and many individuals experience incomplete remission or treatment resistance [,]. Capturing this variability in real-world settings is important to gain a more nuanced phenotypic understanding of depression to inform more personalized care. Although clinical trials routinely use validated symptom rating scales, outcome data in routine psychiatric care are inconsistently measured [], with less than 20% of mental health providers incorporating measurement-based care (MBC) into their practice and as few as 5% using it consistently at every session []. Although MBC has been shown to improve outcomes and is recommended in treatment guidelines [,], real-world uptake has been slow due to practical and organizational obstacles []. Implementation programs such as the National Network of Depression Centers Mood Outcomes Program have only recently demonstrated that adoption is feasible at scale when supported by coordinated infrastructure and system-level investment []. In this context, the Clinical Global Impression (CGI) scale [] represents a promising candidate for scalable, measurement-based assessment in real-world settings. The CGI is a brief, Likert-type scale rated by clinicians during patient encounters and includes both the CGI-Severity (CGI-S) scale, which rates cross-sectional illness severity from 1 (not at all ill) to 7 (among the most extremely ill patients), and the CGI-Improvement (CGI-I) scale, which assesses longitudinal change from 1 (very much improved since initiation of treatment) to 7 (very much worse since initiation of treatment). The CGI-S scale, the focus of the present work and hereafter referred to simply as CGI, has been validated in a wide range of psychiatric settings (eg, inpatient [], outpatient [,], and clinical trial contexts []) and psychiatric conditions including mood [-], anxiety [,], and psychotic disorders [-]. In inpatient care, CGI scores have shown strong convergent validity with established measures such as the Health of the Nation Outcome Scales (HoNOS), Mental Health Questionnaire–14 (MHQ-14), and Depression Anxiety Stress Scales–21 (DASS-21) [], underscoring their value as a brief and pragmatic outcome measure. In depression specifically, CGI ratings have demonstrated strong correlations with standard symptom scales (r≈0.6‐0.8), including the Beck Depression Inventory (BDI), Hamilton Depression Rating Scale (HAM-D), and Montgomery-Åsberg Depression Rating Scale (MADRS), and have shown comparable or greater sensitivity to treatment-related change across outpatient and clinical trial populations [,-]. Interrater reliability for CGI has generally been reported to be in the moderate-to-excellent range across psychiatric disorders, with Cohen κ or interrater coefficient values of approximately 0.7-0.8 [,-]. However, critiques have highlighted its relative lack of specificity and ambiguous response anchors, which may contribute to variability in ratings [-]. To address these concerns, Kadouri et al [] developed an improved CGI (iCGI) for depression, incorporating standardized case vignettes to guide rater calibration. This approach achieved excellent interrater reliability when averaging rater scores (intraclass correlation coefficient [ICC]≈0.9) and demonstrated greater sensitivity for detecting clinical change than with the HAM-D [].