Thesis title:
University peer support: Can non-professional interventions improve student mental health and wellbeing?
Abstract:
Student mental health and wellbeing are areas of concern in higher education. Universities report a 94% increase in demand for counselling services (Thorley, 2017) and are therefore searching for new approaches to support students. Non-professional, settings-based interventions that are embedded in the natural student lifecycle are of particular interest because of their accessibility for all students. Peer support in higher education is one such ‘organic’ approach; however, the research to understand its role in supporting students is lacking in university settings. The research project aims to address this by answering the research question: ‘University peer support: Can non-professional interventions improve student mental health and wellbeing?’ The following outlines the phases of this investigation:
To conduct a systematic review of the types of university peer support that affect student mental health and wellbeing.
To evaluate the operational feasibility of online student wellbeing peer support group in current COVID-19 landscape with mixed methods approach.
To understand university peer support approaches by interviewing staff who coordinate them independently or in partnership with Student Minds.
To Identify what university students understand peer support to be and the factors that influence if they access it through focus groups.
To co-create an evaluation framework with students for the Student Minds peer support programme through participatory action research methods.
To assess the impact of peer support on participants, comparing outcomes for students who complete the programme with students who start but do not complete and a matched group of students who do not participate using the co-created mixed methods evaluation framework.
To establish and model the relationship between factors that predict the efficacy of peer support; identifying when, why and for whom peer support is most effective through Structural Equation Modelling.
Social media:
https://www.linkedin.com/in/julia-haas-msc-68b617b1/ https://twitter.com/juliahaas07
First supervisor:
Prof. Juliet Foster
Pathway:
Pathway 2: Life Course, Psychology & Health
Cohort:
2020-21