Exploring the Acceptability and Feasibility of Interventions to Signpost Students to University Mental Health and Wellbeing Support: A Mixed-Methods Approach

Abstract

Background: Many university students experiencing distress struggle to access support or engage only at a point of crisis. Consequently, there have been calls to use data to proactively identify students at-risk using predictive analytics and intervene earlier. However, there are limited data on students’ and academics’ views on this approach.
Aim: To explore students’ and academics’ views on the acceptability of aligning predictive analytics with student mental health and wellbeing and explore the feasibility of predictive analytics-based interventions to encourage students to engage with university support.
Methods: A systematic review and three primary research studies were conducted. The review identified six institutional barrier and five facilitator themes that impacted students’ professional mental health help-seeking behaviour. Mixed-methods online surveys explored students’ (N = 181) and academics’ (N = 141) views on predictive analytics, roles/responsibilities, and the determinants of signposting behaviour (Studies 1 and 2, respectively). Study 3 (N = 16) used focus groups to explore students’ views on the feasibility of using email interventions to signpost and encourage help-seeking behaviour.
Findings: Students indicated they would opt-in and both students and academics were relatively accepting of predictive analytics-based interventions at a high-level. However, both groups had doubts regarding effectiveness, using email as the mode of delivery, and that these interventions did not address what they perceived as key challenges facing university mental health and wellbeing support provision. Clarification of academics’ pastoral roles is required, as many are unprepared to respond to students experiencing distress.
Contributions: Guiding principles were developed to communicate the key objectives and distinctive features required of signposting emails and wider-university mental health and wellbeing-related communications. This thesis offers timely insights for higher education decision-makers regarding policy and practice, outlines areas for future research in this area, and the planning and deployment of future iterations of predictive analytics.

Date of Award	28 Nov 2024
Original language	English
Awarding Institution	Northumbria University
Supervisor	Peter Francis (Supervisor), James Newham (Supervisor), Peter Francis (Supervisor) & Alyson Dodd (Supervisor)