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A novel multi-component online intervention to improve the mental health of university students: Randomised controlled trial of the Uni Virtual Clinic. | LitMetric

AI Article Synopsis

Article Abstract

Background: Of the millions of students enrolled in university, up to 50% will experience a mental disorder. Many of these students do not seek help, and for those who do, university-based services are often over-burdened. Anonymous, evidence-based, online interventions can improve students' access to mental health support. The Uni Virtual Clinic (UVC) is a transdiagnostic online mental health program designed specifically for university students. This paper reports on a randomised controlled trial examining the effectiveness of the UVC in a sample of Australian university students.

Methods: University students with elevated psychological distress (K10>15;  = 200) were randomised to the UVC intervention or a waitlist control condition for a period of 6 weeks. Baseline, post-intervention, and 3-month follow-up surveys assessed depression, anxiety, self-efficacy, quality of life, adherence, and satisfaction with the UVC intervention.

Results: Mixed models analysis demonstrated that use of the UVC was associated with small significant reductions in social anxiety and small improvements in academic self-efficacy. The program was not effective in reducing symptoms of depression, anxiety, or psychological distress compared to a control group. The majority of participants in the intervention condition who were retained at follow-up engaged with the program, and most of these participants reported satisfaction with the UVC.

Discussion: The results suggest that multi-component online interventions such as the UVC have utility in a university environment. Future trials of the UVC should examine the impact of guidance and/or tailoring on treatment efficacy, and the potential role of the UVC in a stepped care model incorporating on-campus services.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6926241PMC
http://dx.doi.org/10.1016/j.invent.2019.100276DOI Listing

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