The Occupational Depression Inventory: A new tool for clinicians and epidemiologists

Abstract

BackgroundDepressive symptoms induced by insurmountable job stress and sick leave for mental health reasons have become a focal concern among occupational health specialists. The present study introduces the Occupational Depression Inventory (ODI), a measure designed to quantify the severity of work-attributed depressive symptoms and establish provisional diagnoses of job-ascribed depression. The ODI comprises nine symptom items and a subsidiary question assessing turnover intention. MethodsA total of 2254 employed individuals were recruited in the U.S., New Zealand, and France. We examined the psychometric and structural properties of the ODI as well as the nomological network of work-attributed depressive symptoms. We adopted an approach centered on exploratory structural equation modeling (ESEM) bifactor analysis. We developed a diagnostic algorithm for identifying likely cases of job-ascribed depression (SPSS syntax provided). ResultsThe ODI showed strong reliability and high factorial validity. ESEM bifactor analysis indicated that, as intended, the ODI can be used as a unidimensional measure (Explained Common Variance = 0.891). Work-attributed depressive symptoms correlated in the expected direction with our other variables of interest―e.g., job satisfaction, general health status―and were markedly associated with turnover intention. Of our 2254 participants, 7.6% (n = 172) met the criteria for a provisional diagnosis of job-ascribed depression. ConclusionsThis study suggests that the ODI constitutes a sound measure of work-attributed depressive symptoms. The ODI may help occupational health researchers and practitioners identify, track, and treat job-ascribed depression more effectively. ODI-based research may contribute to informing occupational health policies and regulations in the future.