Literature DB >> 31302515

Predicting mental health help seeking orientations among diverse Undergraduates: An ordinal logistic regression analysis.

Genéa Stewart1, Akihito Kamata2, Rona Miles3, Evan Grandoit3, Faigy Mandelbaum3, Crystal Quinn3, Laura Rabin3.   

Abstract

BACKGROUND: Recent years have seen a steady increase in college students reporting mental health issues, though only approximately one-third of these students seek treatment. The present study examines: a) students' perceptions of access to campus provided mental health care; b) student stigma attitudes based on social distance and willingness to disclose mental health issues to campus members who might support help-seeking efforts; and c) the predictive value of five factors (aged older than 22, female gender, completed two or more psychology courses, low stigma, and high perception of access) on help-seeking orientation (HSO).
METHODS: We performed an ordinal logistic regression (OLR) on data from a diverse sample of undergraduates (n = 1,272). The OLR statistical model is more appropriate for measurement of Likert style data than commonly employed statistical models, which may oversimplify attitudinal data by assuming equal intervals between response categories.
RESULTS: Most students did not know that campus-provided counseling was free or confidential, and almost half did not perceive these services as timely or adequate. Students reported more stigma related to disclosing their own problems than to supporting someone else. All five study predictors retained positive and statistically significant slope associations with a positive HSO. Unexpectedly, we found a statistically significant gender interaction with psychology coursework. LIMITATIONS: Data were obtained through self-report measures.
CONCLUSIONS: Results are discussed in relation to the possibility that campus-based mental health interventions may remove roadblocks to healthy help-seeking behaviors, particularly for male students.
Copyright © 2019 Elsevier B.V. All rights reserved.

Keywords:  Help-seeking orientation; Mental health; Perceptions of access; Stigma

Mesh:

Year:  2019        PMID: 31302515     DOI: 10.1016/j.jad.2019.07.058

Source DB:  PubMed          Journal:  J Affect Disord        ISSN: 0165-0327            Impact factor:   4.839


  1 in total

Review 1.  Educational Anomaly Analytics: Features, Methods, and Challenges.

Authors:  Teng Guo; Xiaomei Bai; Xue Tian; Selena Firmin; Feng Xia
Journal:  Front Big Data       Date:  2022-01-14
  1 in total

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