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ORIGINAL RESEARCH article

Front. Public Health
Sec. Public Mental Health
Volume 12 - 2024 | doi: 10.3389/fpubh.2024.1504720

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

Provisionally accepted
Lina-Jolien Peter Lina-Jolien Peter 1*Vanessa Juergensen Vanessa Juergensen 1David Steyrl David Steyrl 2Cindy, Sumaly Lor Cindy, Sumaly Lor 2Anh Phi Bui Anh Phi Bui 1Thomas McLaren Thomas McLaren 3Holger Muehlan Holger Muehlan 3,4Samuel Tomczyk Samuel Tomczyk 3Silke Schmidt Silke Schmidt 3Georg Schomerus Georg Schomerus 1,5
  • 1 Department of Psychiatry and Psychotherapy, Medical Faculty, University Leipzig, Leipzig, Lower Saxony, Germany
  • 2 Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria
  • 3 Department of Health and Prevention, Institute of Psychology, University of Greifswald, Greifswald, Mecklenburg-Vorpommern, Germany
  • 4 Health and Medical University Erfurt, Erfurt, Thuringia, Germany
  • 5 Department of Psychiatry and Psychotherapy, University Hospital Leipzig, Leipzig, Lower Saxony, Germany

The final, formatted version of the article will be published soon.

    Purpose: The goal of the study was to identify the most important influences on professional healthcare use of people with depressive symptoms. We incorporated findings from research areas of health behaviors, stigma, and motivation to predict the help-seeking process variables from a wide range of personal factors and attitudes. 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, and (b) help-seeking intentions, and logistic regression for (c) help-seeking behavior with machine learning methods. Results: While self-stigma and treatment experience are important influences on help-seeking 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 professional help-seeking and the use of combined public health programs and tailored help-seeking interventions.

    Keywords: Help-seeking, depressive symptoms, Mental illness stigma, machine learning, healthcare use

    Received: 01 Oct 2024; Accepted: 06 Nov 2024.

    Copyright: © 2024 Peter, Juergensen, Steyrl, Sumaly Lor, Phi Bui, McLaren, Muehlan, Tomczyk, Schmidt and Schomerus. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Lina-Jolien Peter, Department of Psychiatry and Psychotherapy, Medical Faculty, University Leipzig, Leipzig, Lower Saxony, Germany

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.