Unraveling the Help-Seeking Process and Predictors of Mental Health Care Use among Individuals with Depressive Symptoms: A Machine Learning Approach

Abstract Purpose The goal of the study was to identify the most important influences on help-seeking from a wide range of factors. We incorporated findings from research areas of health behaviors, stigma, and motivation. Methods A sample of 1368 adults with untreated depressive symptoms participated in an online survey with three- and six-month follow-ups. We conducted multiple linear regressions for (a) help-seeking attitudes, (b) help-seeking intentions, and logistic regression for (c) help-seeking behavior with machine learning. Results While self-stigma and treatment experience are important for attitudes, complaint perception is relevant for intention. The best predictor for healthcare use remains the intention. Along the help-seeking process, we detected a shift of relevant factors from broader perceptions of mental illness and help-seeking, to concrete suffering, i.e. subjective symptom perception. Conclusion The results suggest a spectrum of influencing factors ranging from personal, self-determined factors to socially normalized factors. We discuss social influences on informal and professional help-seeking. [Clinical trials registration masked for review] Trial registration German Clinical Trials Register: [masked for review]. Registered 11 December 2020. World Health Organization, Universal Trial Number: [masked for review]. Registered 16 February 2021.