An ecological model for university environment effects on life outcomes

Proposed ecological model for university environment effects on life outcomes

By Kieran Balloo

Ecological models for understanding health behaviour have proliferated in recent years. Ecological models move away from focusing on individual characteristics alone to consider the role that the environment plays in shaping behaviours. We are using this type of framework to understand impacts on life outcomes in the #StudentWellLives Project. In the figure above, we illustrate our proposed ecological model.

We suggest that mental health during adolescence is likely to shape long-term outcomes. However, whilst mental health problems may occur at an individual level, they may be influenced by individuals’ social identity characteristics, reciprocally and intersectionally, in how they predict these outcomes. We discussed in an earlier blog post, on modelling inequalities in an intersectional framework, how social identities are just proxies for systemic privilege and oppression. So fitting with an ecological model, these identities may also be relevant to think about being at a societal, rather than individual, level. We also propose that these aspects during adolescence will influence whether young people will go to university and the type of environment they choose to go to. During university, that environment may then influence their mental health at that point in time. The exact characteristics of the university environment we are exploring are discussed in our earlier blog posts on the type of environment data that universities should collect and belonging and wellbeing in higher education. For example, we noted that participating in sports or extra-curricular activities may be beneficial for certain students’ wellbeing, so the built environments of universities may act as protective factors (or risk factors) for students’ mental health. We anticipate that all of this will impact on life outcomes. Life outcomes have already been found to be influenced by adolescent mental health (which is at an individual level), in terms of impacts on mental health during young adulthood, education and employment outcomes. Therefore, the #StudentWellLives Project aims to draw on the above ecological framework to consider factors beyond the individual.

We would be interested to hear about how we might adapt the model further, so please leave a comment below or connect with us on Twitter: @uniwelllives #StudentWellLives.

Belonging and wellbeing in higher education

Photo by Eric Prouzet on Unsplash

By Kieran Balloo

A recent survey of nearly 1000 individuals found that a lack of belonging to the local community may be related to feelings of loneliness. This research found the issue to be particularly acute for individuals from a Black, Asian and Minority Ethnic (BAME) background, who may feel less welcome in their community than White individuals. Conceptualising belonging in higher education is therefore likely to be important too, but this may be difficult since belonging is unlikely to be a fixed state of being. The Enhancing Student Mental Wellbeing Handbook views a sense of belonging as being about having “strong connections with others who share your values”, and the authors note that it could be a protective factor that “promote[s] positive mental health and wellbeing”. Thus, whilst it may be difficult to quantify belonging, or the extent to which the university population consists of individuals who share a given student’s values, in this post we will give a short overview of how we are using quantitative data in the #StudentWellLives project to try and understand this situation a little more clearly.

In an earlier post, we discussed how we are using publicly available data from the UK Higher Education Statistical Agency (HESA) to understand how the cultural, social and built environments of different universities might impact on students’ mental health and wellbeing (both positively and negatively). For example, participating in sports or extra-curricular activities may be beneficial for certain students’ wellbeing, so the size of green spaces and sports fields at the university may act as proxies for some of the many protective factors that potentially exist in university environments. However, we are also using these data to gain a sense of whether the universities attended by respondents from the Next Steps Study included other students and staff who they might have viewed as being ‘like them’ – i.e. others with whom they could have felt more able to build strong connections.

In order to understand whether these students went to universities with ‘others like them’, we have used HESA data from the time the respondents were at university to determine the proportion of students and staff at their university who had similar characteristics to them. For example, if the respondent was a female commuter student at the time, we have calculated the proportion of female commuters who were also at their university. We will then examine whether going to a university with more individuals who have similar characteristics might act as a protective factor. Of course, going to a university with very different students might also be positive, so this is something we aim to explore with this exploratory approach too.

We would be interested to hear about other characteristics we should consider exploring, so please leave a comment below or connect with us on Twitter: @uniwelllives #StudentWellLives.

Modelling mental health inequalities within an intersectional framework

Photo by Benjamin Elliott on Unsplash

By Kieran Balloo and Anesa Hosein

One of the unique angles of the #StudentWellLives Project is that we are using an intersectional approach to investigate mental health inequalities. Lawyer and scholar, Kimberlé Crenshaw, was the first person to come up with the term intersectionality. She wanted to show how discrimination in employment occurred for Black women – and that the discrimination did not occur because they were Black or because that they were women, but because they were Black women.

We, therefore, expect that when it comes to mental health, the inequalities may be occurring because of what Leslie McCall calls intercategorical complexity – i.e. depending on the multiple social categories that a young person may hold because of, for example, their sex, ethnicity, sexuality, socio-economic class (SEC), and education background. We aim to look at interactions between multiple social categories (e.g. comparing Black male student experiences with white female non-student experiences).

However, in doing this we face a challenge; a large part of the literature on intersectionality is based on qualitative research, much of which subscribes to the anticategorical approach in which McCall explains that social life is seen as being too complicated to be boxed into discrete categories. Whilst we broadly agree with this argument, and that everyone is an individual, in large-scale quantitative datasets such as the Next Steps Study that we are using, patterns are sought about the population to inform policy. Thus, an intercategorical approach is the methodological compromise we need to make. We are also considering these categories in a similar way to Rita Dhamoon and Olena Hankivsky, who contend that these the social categories are really just proxies for systemic privilege and oppression, such as sexism, racism, homophobia, and classism. Hence, through the intercategorical approach we aim to show that social inequalities are a result of structural power hierarchies that shape individuals’ experiences, and intersectionality captures the “numerous interlocking systems of privilege and oppression”, as suggested by Clare Evans and colleagues.

Traditionally, the intercategorical approach would involve an examination of the main effects (i.e. the social categories) and their interactions (i.e. the intersectionalities). We do, however, face some challenges if we use this approach. Firstly, we need to make sure that there are reasonable sample sizes in all of the combinations of social categories. Secondly, this can create some difficulties with interpretation because of the large number of interactions.

However, we may have found an alternative that can get around these challenges. Evans and colleagues recently published a very influential methodological paper about the use of the intercategorical approach in quantitative analyses. In their paper, they propose using multilevel modelling (also known as hierarchical linear modelling) to reflect the power hierarchies embedded in the social categories. The advantages of this approach are that the sample size becomes less of an issue, and the interpretation of the data across social categories can be more easily understood through the use of graphs.

We are still exploring this approach by Evans and colleagues, but we would be interested to hear from other researchers who have adopted a quantitative approach to intersectionality analyses, or those who might be interested in using this novel multilevel approach. Please leave a comment below or connect with us on Twitter: @uniwelllives #StudentWellLives.

Relevant Readings:

  • Crenshaw, K. (1989). Demarginalizing the intersection of race and sex: A black feminist critique of antidiscrimination doctrine, feminist theory and antiracist politics. University of Chicago Legal Forum, 1, 139-167.
  • Dhamoon, R. K., & Hankivsky, O. (2011). Why the theory and practice of intersectionality matter to health research and policy. In O. Hankivsky (Ed.), Health inequities in Canada: Intersectional frameworks and practices (pp. 16–50). UBC Press.
  • Evans, C. R., Williams, D. R., Onnela, J. P., & Subramanian, S. V. (2018). A multilevel approach to modeling health inequalities at the intersection of multiple social identities. Social Science & Medicine203, 64-73.
  • McCall, L. (2005). The complexity of intersectionality. Signs: Journal of women in culture and society30(3), 1771-1800.

Differentiating between mental health and wellbeing


By Kieran Balloo

Currently, in the #StudentWellLives project we are grappling with how to define mental health and wellbeing. Often, the terms mental health and wellbeing are used interchangeably, but research suggests they should be viewed as being connected, but conceptually distinct (Keller, 2020). It might be useful to think of wellbeing as a broader concept that encompasses a range of factors experienced at an individual, community and national level.

Simply put, [wellbeing is] about ‘how we’re doing’ as individuals, communities and as a nation, and how sustainable that is for the future. It is sometimes referred to as social welfare or social value.

What Works Wellbeing (

Mental health refers to a full spectrum of experience ranging from good mental health to mental illness…. Good mental health means more than the absence of illness. It [is] a dynamic state of internal equilibrium in which an individual experiences regular enduring positive feelings, thoughts and behaviours, can respond appropriately to normal negative emotions and situations and is able to make a positive contribution to their community…. Mental illness [is] taken to mean a condition and experience, involving thoughts, feelings, symptoms and/or behaviours, that causes distress and reduces functioning, impacting negatively on an individual’s day to day experience, and which may receive or be eligible to receive a clinical diagnosis.

The University Mental Health Charter (

In the #StudentWellLives project, we believe the distinction is helpful because we will be analysing aspects of both wellbeing and mental health as separate constructs. The Office for National Statistics’ (ONS) Measuring National Well-being programme defines individual wellbeing as representing how people feel about:

  • personal wellbeing
  • health
  • relationships
  • education and skills
  • what they do
  • where they live
  • personal finances
  • the economy
  • governance
  • the environment

Using this conceptualisation, aspects of mental health, such as anxiety and depression, could fall under ‘personal wellbeing’ and ‘health’. ‘Personal wellbeing’ also includes life satisfaction, life worthwhileness, and emotions. Other aspects of wellbeing that are likely to be relevant to our project include: ‘relationships’, which covers personal relationships and feelings of loneliness; ‘education and skills’, which covers educational achievement and human capital; and ‘what they do’, which includes work and leisure activities.

In the Next Steps data we are utilising, there are measures of life satisfaction, health, and relationships with others. There are also measures related to young people’s education and employment outcomes (e.g. job satisfaction), which can be seen as aspects of their wellbeing under the ONS definition. Instead of conflating the two terms, we will be able to see how different aspects of mental health and wellbeing are interrelated, which might be valuable when using the findings to identify how best to support differing student needs. For example, supporting students with their feelings of loneliness when they start university might not mean automatically sending them to student counselling, which might be better for dealing with more profound mental health issues, such as anxiety and depression.

We would be interested to hear about other benefits or disadvantages to separating the two concepts, so please leave a comment below or connect with us on Twitter: @uniwelllives #StudentWellLives.

What environmental data should universities collect for student mental health and wellbeing?

Photo by Pixabay on

By Anesa Hosein

Over the last two months, we have been delving into the UK Higher Education Statistical Agency (HESA) publicly available datasets. And when I say, “we”, I mean Kieran. It is mind blowing how much data is available for each university. And it is such a rich dataset which researchers can use for any amount of secondary data analysis research.

In a recent research bid I was putting together for an Irish Higher Education context, I recognised the lack of data that is available for Higher Education Institutions (HEIs) in other countries in comparison to the UK. If we are to understand the university environment in different international contexts and how they impact on student wellbeing then we need governments to implement plans to collect sufficient and the right data for higher education.

What constitutes the appropriate/right data that HEIs and governments need to collect to understand student mental health and wellbeing is something that our project hopefully might be able to contribute to. We recognise that collecting this type of data requires a huge resource and infrastructure investment and hence we hope to provide clear guidelines in the future about what may be the best data to collect to understand how the university environment affects student mental health and wellbeing.

At the moment we are exploring from the HESA dataset, various cultural, social and built environment variables that contribute to the university environment, including investment in arts, proportion of Black, Asian and minority ethnic (BAME) students, and the size of green spaces and sport fields. We know from the work of WhatWorksWellbeing that the arts, a sense of belonging, and taking part in sports affect mental health and wellbeing. Hence we’re checking the extent to which these variables contribute to a university environment that supports mental health and wellbeing.

So, watch this space and if you can think of any variables that HESA should have for measuring mental health and wellbeing – let us know.

Project launch and start of phase 1

By Kieran Balloo

The Student Wellbeing & Life Outcomes Project launched at the beginning of July and we have got straight to work on the first phase of our four-phase project. As a secondary data analysis project, our main job at the moment involves preparing various data for analysis. During phase 1, which will last for the next few months, we will focus on preparing data to address the following research question:

  • What type of university environment works for having better life outcomes (particularly mental health and wellbeing) for graduates with different social characteristics?

Respondents’ university data from the Next Steps Study will be linked with university data held by the Higher Education Statistical Agency (HESA) and the Office for Students (OfS), and with National Student Survey (NSS) results. Some of the university data will be used to classify universities based on their environment. For example, universities will be clustered based on their financial investment in, and access to, sports facilities, and their perceived levels of academic and personal support.

We will also be meeting with members of our advisory board during this phase to discuss our project plans and ensure our research will be relevant to our beneficiaries.