Using Network Analysis to Uncover the Complexities of Mental Wellbeing among Diverse Student Groups

The UCL Diversity Forum has been actively engaged in using network analysis to gain a deeper understanding of the mental wellbeing (MeW) of first-year students from diverse backgrounds. Combining insights from two separate studies conducted over the academic years 2021-2022 and 2022-2023, we aim to shed light on the complex interplay of gender, ethnicity, and mental health in shaping students’ university experiences.

Network analysis allows researchers to explore the intricate connections between various factors influencing MeW, such as personal traits, contextual factors, and severe mental illness (SMI). By comparing network structures across gender and ethnicity groups, the UCL Diversity Forum seeks to identify patterns that could inform tailored support and interventions for diverse student populations.

Findings from both years revealed interesting insights about the density of MeW networks for different groups. Female students consistently had more densely connected MeW networks than their male counterparts. This suggests that females’ mental wellbeing may be more sensitive to campus intervention and support, while male students may be less impacted due to their potentially higher self-efficacy.

NA on 21/22 Diversity Forum data.
NA on 22/23 Diversity Forum data.

In terms of ethnicity, the results were more nuanced. While Asian and White students displayed similar network patterns, Black and other minority students exhibited sparser, more distinct networks. These groups had fewer MeW predictors than the majority White students, with these predictors remaining stable over time.

When examining changes in network structures over time, notable differences emerged across genders, but not across ethnicities. For both genders, the predictability of MeW by subjective status and belongingness decreased over time. Among female students, extraversion became a less accurate predictor of MeW, while prestige emerged as a better indicator. For male students, all personal factors maintained a consistent level of predictability for MeW over time.

The use of network analysis in the UCL Diversity Forum’s research has proven invaluable for uncovering the complexities of mental wellbeing among diverse student groups. Future research should delve further into separate ethnic minority groups, investigate the role of severe mental illness, and explore intersectional effects of gender and race on students’ mental health. Armed with these insights, higher education institutions can devise targeted, effective interventions to support the mental wellbeing of all students, fostering a more inclusive and nurturing environment for everyone.

We are actively exploring ways data science (network analysis, social networks, machine learning) can help unfold the experience of diverse university students to serve as bottom-up feedback to universities’ EDI policy. If you are interested in collaboration, please contact We look forward to working together to promote a more inclusive and supportive academic environment for students from all backgrounds.


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