The help-seeking process and predictors of mental health care use among individuals with depressive symptoms: a machine learning approach

Vanessa Juergensen; Lina-Jolien Peter; David Steyrl; Cindy Sumaly Lor; Anh Phi Bui; Thomas McLaren; Holger Muehlan; Samuel Tomczyk; Silke Schmidt; Georg Schomerus

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

Front Public Health. 2024 Nov 20:12:1504720. doi: 10.3389/fpubh.2024.1504720. eCollection 2024.

Authors

Affiliations

¹ Department of Psychiatry and Psychotherapy, Medical Faculty, University Leipzig, Leipzig, 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, Germany.
⁴ Department of Medicine, Health & Medical University Erfurt, Erfurt, Germany.
⁵ Department of Psychiatry and Psychotherapy, University of Leipzig Medical Center, Leipzig, Germany.

^# Contributed equally.

Abstract

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 1,368 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.

Clinical trial registration: German Clinical Trials Register (https://drks.de/search/en): Identifier DRKS00023557.

Keywords: depressive symptoms; healthcare use; help-seeking; machine learning; mental illness stigma.

Publication types

Randomized Controlled Trial

MeSH terms

Adult
Depression* / psychology
Female
Germany
Help-Seeking Behavior*
Humans
Intention
Machine Learning*
Male
Mental Health Services / statistics & numerical data
Middle Aged
Patient Acceptance of Health Care* / psychology
Patient Acceptance of Health Care* / statistics & numerical data
Social Stigma
Surveys and Questionnaires

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study has been funded by DFG Deutsche Forschungsgemeinschaft (SCHO 1337/4-2, SCHM 2683/4-2). The funding body is neither involved in the design of the study, the preparation, collection, analysis, and interpretation of data, nor in the writing of this article and deciding to submit it for publication.