Full text
1,961 characters
· extracted from
oa-pdf
· click to expand
Posted on 14 Aug 2025 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.175517115.57730020/v1 — This is a preprint and has not been peer-reviewed. Data may be preliminary.
Digital tools to support post-secondary student mental health and
wellbeing
Haley LaMonica M 1, Ian Hickie1, William Capon 1, Maya Ahia2, Lexi Ewing 3, Wendy Lee4,
Frank Iorfino1, Yun Ju Song1, Sarah Mckenna1, and Kristin Cleverley 3
1The University of Sydney Brain and Mind Centre
2University of Toronto
3Centre for Addiction and Mental Health
4The University of Sydney School of Education and Social Work
August 14, 2025
Abstract
Post-secondary students are confronted by multiple factors that may impact their mental health, including heightened academic
demands, financial burdens, new living circumstances, social isolation, and an increased need to be self-reliant. While student
mental health is a priority for post-secondary institutions, there is a marked gap in evidence regarding what supports and
interventions are most effective, for whom, and in what contexts. Digital technologies can improve the accessibility of mental
health care and facilitate comprehensive data collection; however, we argue that there is an urgent need for these tools to be
co-designed with students to ensure they are relevant, useable, and responsive to their real-world experiences, implemented
with human support to optimise outcomes, and championed by organisational leadership to promote adoption. Further, it is
essential that clinical and service usage data is tracked alongside academic performance to prioritise both student mental health
and academic success in alignment within institutional priorities.
Hosted file
Digital tools for post-sec students_12_08_2025_FINAL.docx available at https://authorea.com/
users/956169/articles/1325162-digital-tools-to-support-post-secondary-student-mental-
health-and-wellbeing
1
Text is read by the "Ask this paper" AI Q&A widget below.
Extraction quality varies by source — PMC NXML preserves structure
cleanly, OA-HTML may include some navigation residue, and OA-PDF can
have broken hyphenation. The publisher copy
(via DOI)
is the canonical version.