Comparing the mental health of Australian university students with that of young people who are not in higher education.

MENTAL health; COLLEGE students; YOUNG adults; PSYCHOLOGICAL resilience; WELL-being; SOCIOECONOMIC status; HIGHER education; PSYCHOLOGICAL distress

Compared with their non-student counterparts, young adults engaged in higher education are often reported to be at greater risk for mental health concerns. However, research directly comparing university student and non-student populations is limited. This study uses a community sample to investigate differences in the mental health and wellbeing of young Australians (17–25 years) engaged in university study (n = 828) compared with their age-matched non-studying peers (n = 880). Comparisons are made on mean levels of psychological distress, wellbeing, resilience, and loneliness, as well as the proportion of individuals meeting commonly used classifications for mental health and wellbeing. We then examine the role of employment on reported distress and wellbeing. While a substantial proportion of the sample reported high to very high distress, our study found no overall substantive difference in levels of distress or wellbeing between students and non-students. An examination of employment status revealed a more nuanced result. Full-time employment was associated with greater psychological distress in students, where otherwise students reported better wellbeing and lower distress than their non-student peers. These findings challenge the common discourse of tertiary students as having a greater risk of distress than the general community. However, attempting to combine full-time work with full-time study is challenging, and the pressures of such work can increase distress. Increased financial assistance for tertiary students is needed to ease work pressures, along with greater understanding of the flexibility needed to balance work and study commitments. [ABSTRACT FROM AUTHOR]

Copyright of Higher Education (00181560) is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)