A Mixed-Methods Exploration of the Utilisation of Embedded University Wellbeing Services by Ethnic Minority Students in London

Jones Nielsen, Jessica D.; Thomas, Rachel; Fett, Anne-Kathrin

doi:10.3390/educsci15091263

Open AccessArticle

A Mixed-Methods Exploration of the Utilisation of Embedded University Wellbeing Services by Ethnic Minority Students in London

by

Jessica D. Jones Nielsen

^1,†

,

Rachel Thomas

^1,† and

Anne-Kathrin Fett

^1,2,*

¹

Department of Psychology and Neuroscience, School of Health and Medical Sciences, City St George’s, University of London, London EC1V 0HB, UK

²

Department of Psychosis Studies, Institute of Psychology, Psychiatry and Neuroscience, King’s College London, London SE5 8AF, UK

^*

Author to whom correspondence should be addressed.

^†

These authors contributed equally to this work.

Educ. Sci. 2025, 15(9), 1263; https://doi.org/10.3390/educsci15091263

Submission received: 25 July 2025 / Revised: 15 September 2025 / Accepted: 18 September 2025 / Published: 22 September 2025

(This article belongs to the Special Issue Addressing Mental Health and Well-Being in Higher Education: Collaborative Insights from Education, Psychology, and the Learning Sciences)

Download Versions Notes

Abstract

The current study aimed to explore the incidence and severity of the mental health difficulties of ethnically diverse students at University of London institutions. Student mental health is a growing concern in UK higher education, yet little research has explored how ethnically diverse students experience embedded university wellbeing services. Despite persistent inequalities, embedded university wellbeing services remain under researched for ethnic minority students in the UK. A sequential explanatory mixed-methods design was essential to illuminate the mechanisms underlying observed quantitative patterns amongst this group of students, with qualitative accounts providing depth and context to statistical trends. A sequential explanatory mixed-methods approach was employed, combining data from 203 ethnically diverse students at University of London institutions with data from open-text and semi-structured interviews. A mixed-methods design was critical in capturing both the absence of broad group differences and the specific barriers shaping ethnic minority student experiences. Results showed few ethnic differences in distress, help-seeking, or dropout. However, ethnic minority students reported greater in-group stigma and cultural barriers, which were negatively associated with help-seeking. Qualitative themes further illustrated the importance of the therapeutic relationship, concerns about privacy, perceptions of undeservingness, and time constraints. Findings highlight the importance of culturally responsive approaches to embedded university wellbeing services in higher education. Targeted outreach, ethnic matching of therapists, and a more diverse counselling workforce may improve engagement and reduce inequalities.

Keywords:

student mental health; ethnicity; student wellbeing services; dropout; utilisation; stigma

1. Introduction

Rising rates of mental health difficulties among university students have become a pressing concern worldwide. International studies suggest that students experience higher rates of depression and anxiety than their non-student peers, with implications for academic performance, retention, and long-term wellbeing (Auerbach et al., 2016). Across Europe and North America, university wellbeing services are increasingly recognised as a first point of access for psychological support, yet demand has outpaced provision (Thorley, 2017).

In the UK, similar patterns are evident. The proportion of young people enrolling in higher education has risen from 33% in 2000 to 58% in 2020 (Bolton, 2024). This increase partly explains the sharp rise in students reporting mental health conditions on entry to university (HESA, 2023), and the increased use of embedded university wellbeing services (Thorley, 2017). However, evidence also indicates that prevalence rates of mental health conditions are unevenly distributed across the student population, with specific demographic groups—particularly ethnic minority students—facing greater risk (Arday et al., 2022; Broglia et al., 2021a).

In the general UK population, ethnic minority groups report higher levels of common mental health disorders and more severe symptoms at initial mental health assessments than their White counterparts (Baker, 2020; NCCMH, 2023). Similar findings have been replicated in the UK student population, though findings are mixed (Arday et al., 2022; Schochet et al., 2023). Despite these indications of higher mental health needs, little is known about how ethnic minority students engage with embedded university wellbeing services. Existing research has tended to focus on overall usage patterns rather than on barriers to access, service dropout, or cultural experiences specific to ethnic minority university students.

Barriers to help-seeking are often categorised as individual, cultural, or practical. Individual barriers include stress (Olaniyan, 2021), ethnicity (Bryant et al., 2022; Soorkia et al., 2011), gender and personal coping styles (Cage et al., 2020; Sagar-Ouriaghli et al., 2020). Cultural barriers encompass recognition of mental health distress, cultural identity, and different forms of stigma—defined here as self-stigma (internalised shame about experiencing distress), societal stigma (perceived negative judgements from society, and in-group stigma (concern about disapproval from one’s own community) (Jennings et al., 2017; Shea et al., 2019). Practical barriers include time to attend appointments, competing academic commitments, and limited awareness of available services (Baik et al., 2019; Cage et al., 2020; Li et al., 2018). While these categories provide a useful framework, the mental health treatment gap is particularly concerning among ethnic minority students. More research specific to university wellbeing services in the UK is needed to understand how barriers to help-seeking contribute to lower service utilisation across diverse groups.

To conceptualise these issues, we draw on the Health Belief Model (HBM) (Rosenstock et al., 1988), which emphasizes perceived threats, benefits, barriers, and self-efficacy as predictors to health behaviour. Self-efficacy—an individual’s confidence in their ability to seek and engage with support—is particularly relevant here, as students who lack confidence in navigating services or disclosing distress may be less likely to persist with counselling. Importantly, the HBM acknowledges socio-cultural influences, including ethnicity, that shape health beliefs and behaviours. Prior research has applied the HBM to mental health service use among students of diverse ethnicities (Bird et al., 2020; Langley et al., 2018, 2021; Nobiling & Maykrantz, 2017; Panicker et al., 2023) and specifically focused on barriers to help-seeking (S. B. Kim & Lee, 2022; J. E. Kim & Zane, 2016; Morena et al., 2024), making it an appropriate framework for this study.

1.1. Aims

The current study utilised a sequential explanatory mixed-methods design and aimed to:

Examine the incidence and severity of mental health difficulties and the utilisation of embedded university wellbeing services by a diverse group of university students.
Identify barriers to both initially accessing and continuing counselling support among ethnic minority students.
Inform clinical practice and service development in the university mental health sector.

1.2. Research Questions

Specifically, the study aimed to answer the following two research questions:

(1): What are the utilisation, help-seeking and dropout rates of ethnically diverse students utilising university student wellbeing services and how do these rates differ as a function of ethnicity?
(2): What individual, cultural, and practical barriers may potentially impact decisions to dropout from therapy after attending an initial session amongst ethnic minority students utilising university student wellbeing services?

1.3. Hypotheses

Phase 1 of the current study tested the following hypotheses:

(1): Rates of mental health difficulties will be higher in ethnic minority students compared to White students.
(2): Ethnic minority students will report lower rates of help-seeking than White students.
(3): Dropout rates after initial assessment by student wellbeing services will be higher among ethnic minority students than White students.
(4): Levels of individual, cultural and practical barriers to utilising university student wellbeing services will be higher among ethnic minority students than White students.

Although barriers can be individual, cultural, or practical, our hypotheses focus on cultural factors given prior evidence linking ethnicity with disparities in service use. Individual and practical barriers were explored in greater detail within the qualitative phase (Phase 2).

2. Materials and Methods

2.1. Research Design

A sequential explanatory mixed-methods design (denoted as QUAN → qual) was used to address the research questions in two phases (Creswell et al., 2003; Johnson & Onwuegbuzie, 2004). In Phase 1, the first and main component of the study, an online survey battery was administered to an ethnically diverse sample of university students to understand the incidence and severity of mental health difficulties and the utilisation of embedded university wellbeing services. This phase was prioritised to provide breadth and establish baseline rates of distress, help-seeking, and service dropout across different ethnic groups. Informed by responses to open-text questions from the quantitative results, Phase 2 consisted of semi-structured interviews to understand how ethnic minority students made sense of barriers and decisions to discontinue therapy. The mixed-methods design was chosen because statistical differences alone will not be sufficient to provide richer and contextual understanding. While Phase 1 provides a snapshot of university students’ mental health difficulties and help-seeking behaviours that allows for comparisons across ethnic groups, Phase 2 offers deeper insights into the contextual and cultural nuances influencing ethnic minority students’ decisions to drop out after initial assessment.

2.2. Recruitment

Participants were recruited through voluntary response sampling between February 2023 and February 2024. Information about the research was circulated through Student Union representatives, student societies, the Student Mental Health Research Network (SMaRteN), social media, and the King’s College London Research Participation System. In addition, posters advertising the study were displayed across University of London campuses. The study posters were disseminated via City, University of London’s Sona system, which is a web-based participant recruitment and study management platform. To encourage a diverse sample, targeted outreach was made to ethnic minority societies and networks.

Potential participants were invited to express their interest in participating in the study via a Qualtrics QR code or link located on the study advert and were directed to an electronic copy of the Participant Information Sheet, outlining the research study in more detail. They were then sent a Qualtrics link to complete the informed consent form online and, after giving their consent, they completed the study survey battery which took approximately 20 min. Upon completion of the survey battery, they were directed to a debrief page. Eligibility criteria required participants to be over 18 and enrolled at a University of London institution. This study received ethical approval by the City, University of London Psychology Ethics Committee (ETH2122-1642, ETH2223-1354, and ETH2324-0346). Students were incentivised to take part in the study by being offered the chance to enter a voucher prize draw.

2.3. Participants

The quantitative component (Phase 1) included a sample of 203 student participants from University of London institutions. The demographic characteristics for student participants in the Phase 1 sample are summarised in Table 1.

Due to small subgroup sizes, ethnic minority categories (Asian or Asian British, Black or Black British, Mixed or multiple ethnic groups, and Other ethnic group) were combined into a single “ethnic minority” group for quantitative analysis. While this approach facilitated statistical testing, it is a limitation that may obscure within-group differences (see Section 4.1).

2.4. Measures

2.4.1. Phase 1 Survey Battery

The Phase 1 survey battery comprised six instruments: a demographic questionnaire and five validated measures. Data were collected online using Qualtrics XM software (https://www.qualtrics.com). The demographic questionnaire included items on age, ethnicity, gender, level of study and student status (home student: UK-domiciled; international student: non-UK domiciled). The mental health and service utilisation section comprised the following validated measures: (1) The Stigma Scale for Receiving Psychological Help (SSRPH; Komiya et al., 2000), a 5-item measure assessing public stigma towards help-seeking. Responses are rated on a three-point Likert scale. Internal consistency in the original US undergraduate sample was acceptable (α = 0.72). (2) The Self-Stigma for Seeking Help (SSSH; Vogel et al., 2006) is a 10-item scale assessing internalised social stigma associated with seeking psychological help. Responses are rated on a three-point Likert scale. Internal consistency in the original study was very good (α = 0.83). (3) The Beliefs toward Mental Illness (BMI) Scale (Hirai & Clum, 2000) is a 21-item measure assessing cross-cultural differences in beliefs around mental illness across three subscales: dangerousness, poor social and interpersonal skills, and incurability. Items are rated on a six-point Likert scale. Internal consistency was high for both Asian (α = 0.91) and American (α = 0.89) student samples. (4) The Barriers to Seeking Mental Health Counseling (BMHC; Shea et al., 2019) is a 27-item scale assessing perceived barriers to mental health service use across six subscales: Negative Perceived Value, In-group Stigma, Discomfort with Emotions, Lack of Knowledge, Lack of Access, and Cultural Values. Items are rated on a six-point Likert scale. Average internal consistency across subscales was very good (α = 0.84). (5) The Counseling Centre Assessment of Psychological Symptoms-34 (CCAPS-34; Locke et al., 2012) is a 34-item clinical tool designed for use in university counselling centres to assess psychological distress across seven domains: depression, generalized anxiety, social anxiety, academic distress, eating concerns, hostility, and alcohol use, with an overall distress index. Responses are rated on a 5-point Likert scale. Initially developed for US university students, the CCAPS-34 has also been validated with UK student populations (Broglia et al., 2021b; Harrold et al., 2024; Schochet et al., 2023). Internal consistency was excellent for university student samples (α = 0.92) across studies.

The final section of the survey battery included questions on students’ help-seeking behaviours related to university wellbeing services, including service utilisation and dropout. Students’ experiences of mental health support were assessed using six questions: (1) “Are you currently receiving mental health support from outside your university?”, (2) “Have you contacted your university counselling centre for support?, (3) “Have you been offered an assessment?”, (4) “Have you attended your university counselling centre for an assessment?”, (5) “After attending the assessment did you attend counselling sessions if offered?”, and (6) “Did you drop out before completing the sessions?” The final item in this section invited eligible participants to take part in Phase 2 semi-structured interviews.

In addition, three open-text questions were included to allow participants the opportunity to explain: (a) their reasons for not attending a university wellbeing service, (b) their reasons for not attending sessions after an initial assessment was offered, and (c) their reasons for dropping out before completing sessions. These open-text responses, embedded within the survey battery, formed the first qualitative data set.

2.4.2. Phase 2 Interviews

Following the completion of the survey battery, 11 participants provided responses to the open-text responses, which were analysed thematically and used to inform the development of the Phase 2 interview schedule (Appendix A). Ethnic minority participants who completed the final section of the survey battery were invited to in-depth semi-structured interviews to explore how they made sense of barriers and decisions to discontinue counselling. The interviews were guided by an interview schedule and comprised seven questions with supplementary probes. An example of a question included: “What was your experience like of reaching out for help and then waiting for the first session?” Table 2 presents demographic details of the five participants who participated in the Phase 2 semi-structured interviews.

2.5. Statistical Analyses

2.5.1. Phase 1 Quantitative Data Analysis

Statistical analyses were conducted using IBM SPSS Statistics for Windows, version 29 (IBM Corp., 2023, Armonk, NY, USA). Differences in mental health difficulties between White and ethnic minority students, as measured by the CCAPS-34 were examined using analyses of covariance (ANCOVAs), with Chi-square tests used to assess differences in the proportion of students meeting clinically significant thresholds. Differences in help-seeking behaviours and service utilisation between groups were also analysed using Chi-square tests, while group differences in counselling dropout rates were assessed using Fisher’s exact test. Group differences in individual, cultural and practical barriers to help-seeking were examined using ANCOVAs. Pearson’s correlation coefficients and logistic regression analyses were conducted to identify predictors of help-seeking among ethnic minority students. The dependent variable was whether a student had sought support from university wellbeing services (yes/no). Independent variables included student status (home or international), societal stigma (SSRPH), self-stigma (SSSH), in-group stigma, negative perceived value, cultural values, and overall distress (CCAPS–34 Distress Index). Several post hoc analyses were performed to investigate relationships within the ethnic minority group, specifically. Normality of continuous variables was assessed using the Shapiro–Wilk test; all variables met assumptions of normality, and therefore parametric tests were applied. A priori power analysis using G*Power 3.1.9.7 (Faul et al., 2007) indicated that, to detect a medium effect size (f = 0.25) with 80% power, a total sample size of 398 participants was required for Phase 1.

2.5.2. Phase 2 Qualitative Data Analysis

Semi-structured interviews were conducted via Zoom and explored how ethnic minority students made sense of barriers and decisions to discontinue counselling from students who participated in Phase 1 of the study. Reflexive thematic analysis was deemed the most appropriate methodology for this component of the study due to its flexibility and its ability to combine both deductive and inductive analytic approaches to the data. This study component was to explain the quantitative findings in more detail and to allow issues to be explored in more depth. The completed transcript data were analysed using a six-step approach of reflexive thematic analysis (Braun & Clarke, 2022). The interviews were audio-recorded, and transcript data were transcribed verbatim directly from the audio recording. This was first performed based on responses of 11 participants to the open-text survey questions in Phase 1 and then repeated using the semi-structured interview data from the participants in Phase 2.

Specific consideration has been given to thematic analysis and how to ensure that the criteria for trustworthiness are met (Nowell et al., 2017). The following steps were taken in order to ensure trustworthiness of the qualitative data analysis: (1) a reflexive diary was kept throughout data collection and the analytic process, (2) researchers reviewed the initial coding of the transcripts and the development of themes together, (3) a prolonged engagement with the data, involving a detailed familiarisation with the data and then extended to revisiting the data throughout each stage of the phased analysis detailed above, (4) each subtheme and theme have been supported by data extracts from all participants to support the themes, and (5) both the method and the analysis have been clearly outlined and then described in detail.

2.5.3. Integration of Data Sets

Following completion of the quantitative and qualitative analyses, the two strands were integrated to identify points of convergence and divergence. Quantitative results highlighted key group-level differences, particularly around cultural values, in-group stigma, and lack of access, while the qualitative findings provided nuanced accounts of how these barriers were experienced in practice (e.g., therapeutic misalignment, confidentiality concerns, perceptions of undeservingness, and time-related constraints). To strengthen this integration, we developed a joint display (Creswell & Plano Clark, 2011) table linking research questions, statistical outcomes (with effect sizes), qualitative themes, and Health Belief Model constructs, which together illustrate how cultural and structural barriers shape the lived experiences of ethnic minority students in engaging with university wellbeing services.

3. Results

3.1. Quantitative Results

3.1.1. Descriptive Statistics

Comparisons were conducted between White and ethnic minority student groups. No significant differences were observed between home and international students, χ² (n = 203) = 1.30, p = 0.254, φ = 0.09. A significantly higher proportion of ethnic minority students were undergraduates (80.2%, n = 101) compared to White students (64.9%, n = 50), χ² (n = 203) = 5.04, p = 0.025, φ = −0.17 (small effect size). Ethnic minority students (M = 21.89, SD = 5.86) were significantly younger than White students (M = 24.91, SD = 8.67), t(203) = 2.69, p = 0.008, d = 0.43 (medium effect size). Gender distributions did not significantly differ between the two groups, χ² (n = 203) = 0.830, p = 0.080, φ = 0.20 (small to moderate effect).

3.1.2. Psychological Distress

ANCOVA results (controlling for age) showed no significant differences for most of the CCAPS-34 subscales, including Distress Index, Depression, Social Anxiety, Academic Distress and Hostility. White students scored significantly higher than ethnic minority students on Generalized Anxiety (partial η² = 0.03, small effect), Eating Concerns (partial η² = 0.03, small effect), and Alcohol Use (partial η² = 0.15, large effect). Adjusted means, standard deviations, and ANCOVA results are shown in Table 3 and Table 4, respectively.

We conducted further analyses to examine clinical cut off scores for each subscale. Many of the group comparisons were not significant apart from comparisons for Eating Concerns and Alcohol Use which were both significant with higher levels of distress observed in White students compared to ethnic minority students, with small effect sizes. Further details are shown in Table 5 below.

3.1.3. Help-Seeking

Overall, 43 ethnic minority students (34.1%) and 19 White students (24.7%) reported contacting their university wellbeing services. This difference was not statistically significant, χ² (n = 203) = 1.59, p = 0.21, φ = −0.10, reflecting an exceedingly small effect size with limited practical significance. Among those who made contact, a higher proportion of White students (84.2%) than ethnic minority students (69.8%) were offered an initial assessment. This difference was also non-significant, χ² (n = 62) = 0.78, p = 0.34, φ = 0.15. The small effect suggests only a modest difference in access to initial assessments between groups.

3.1.4. Service Utilisation

Ten White students (62%) and 24 ethnic minority students (80%) attended an initial assessment when offered. This difference was not significant, Fisher’s exact p = 0.29. The effect size (φ = 0.20) suggests a small-to-moderate association, implying a potentially meaningful trend towards greater first-session attendance among ethnic minority students. Eight White students (80%) and 15 ethnic minority students (65%) attended further sessions after the initial assessment. This difference was not significant, χ² (n = 46) = 0.35, p = 0.44, φ = 0.17, a small effect.

3.1.5. Dropout

Of those who began counselling, three White students (30%) and 11 ethnic minority students (45.8%) subsequently dropped out. Fisher’s exact test indicated no significant association between ethnicity and dropout, p = 0.47 (two-tailed), with a small effect size (φ = 0.16).

3.1.6. Barriers to Help-Seeking

ANCOVA (controlling for age) indicated no significant ethnic group differences in societal stigma (SSRPH), self-stigma (SSSH), or beliefs toward mental illness (BMI). However, ethnic minority students reported significantly higher overall scores on the Barriers to Seeking Mental Health Counselling (BSMHC) scale, F(1, 200) = 12.66, p < 0.05, partial η² = 0.06, indicating a medium effect size. Follow-up analyses revealed that this effect was driven by significantly higher in-group stigma (F(1, 200) = 13.97, p < 0.001, partial η² = 0.065; moderate effect size) and cultural values (F(1, 200) = 44.98, p < 0.001, partial η² = 0.184; large effect size). We hypothesised that ethnic minority students would report higher levels of practical barriers to accessing support compared to White students. No significant group differences were observed between the two ethnic groups, F(1, 200) = 0.000, p = 0.988, partial η² = 0.000. Ethnic minority students reported significantly higher lack of access scores than White students, F(1, 200) = 4.41, p = 0.037, partial η² = 0.022, reflecting (a small effect size). The means and adjusted means and ANCOVA test statistics are presented in Table 6 and Table 7, respectively.

3.1.7. Post Hoc Analyses

To further explore factors influencing help-seeking within the ethnic minority group, a series of post hoc analyses were conducted. Pearson’s correlation indicated a small, non-significant negative association between in-group stigma and seeking support from university student wellbeing services, r(124) = −0.08, p = 0.40. A binary logistic regression was conducted to examine predictors of help-seeking among ethnic minority students. The dependent variable was whether participants had sought support from university wellbeing services (yes/no). Independent variables were student status (home or international), societal stigma (SSRPH), self-stigma (SSSH), in-group stigma, negative perceived values, cultural values and overall distress (CCAPS-34 Distress Index). The overall model was statistically significant χ² (7, n = 126) = 19.93, p = 0.006, correctly classifying 66.7% of cases. It explained 14.6% (Cox & Snell R²) and 20.2% (Nagelkerke R²) of the variance in help-seeking. Two predictors made significant individual contributions to the model: cultural values and the distress index (as shown in Table 8). The strongest predictor was cultural values, where higher scores were associated with a lower likelihood of seeking support (OR = 0.89, 95% CI [0.79, 0.99], p = 0.036). Higher distress was associated with greater help-seeking, with students 2.15 times more likely to seek support for each unit increase in distress (OR = 2.16, 95% CI [1.19, 3.90], p = 0.011). No other predictors reached statistical significance.

3.2. Qualitative Results

The qualitative phase (Phase 2) was designed to explain and contextualise the quantitative findings, particularly the observed ethnic differences in perceived barriers, cultural values and help-seeking behaviours. Reflective thematic analysis of interview transcripts (n = 5) and open-text survey responses (n = 11) generated four overarching themes with their 12 associated subthemes (Table 9). These themes captured students lived experiences of engaging with or discontinuing university wellbeing services, with attention to cultural, relational, and systemic factors. Illustrative quotations from transcripts are provided to highlight the depth and nuance of participants’ perspectives, with identifiers distinguishing interview participants (IP) from survey participants (SP).

3.2.1. Theme One: Therapeutic Relationship

Participants emphasised that the therapist-client relationship played a very significant role in their decision to continue or drop out of counselling. Several described struggling to “connect” with their therapist, often due to feeling of being misunderstood or experiencing cultural incongruence. In all interview cases, the therapist was of a different ethnicity to the participant, which many felt contributed to the disconnect.

Not Feeling Understood.

Some participants described difficulty communicating their difficulties in a way the therapist seemed to understand:

“I felt like the psychiatrist was unwilling to understand my current problems and unable to understand my childhood experiences.”
(SP12)

“I do think like because of where and how I grew up I probably at the time didn’t verbalise it (my difficulties) as well as maybe they were used to.”
(IP5)

Cultural Misunderstandings.

Participant reported that therapists sometimes made assumptions based on ethnicity or failed to account for cultural differences, particularly in attitudes towards mental health and help-seeking. One participant was repeatedly advised to talk to his parents, despite this being culturally inappropriate:

“I cannot have that conversation with them. It is not that prevalent back in India so that was not an option for me. My parents are particularly insensitive to these things like they usually brush it off.”
(IP3)

For some participants, cultural mismatch was significant enough to influence future help-seeking choices:

“Sometimes a cultural difference is going to put me off and actually, funny enough next time I chose a counsellor of my ethnicity to make it much easier.”
(IP3)

International student status sometimes amplified cultural assumptions:

“I remember him making assumptions that because I was from overseas. That I, all my feelings like what I was going through or why I came to therapy was because of like cultural differences.”
(IP5)

Such cultural misunderstandings undermined trust, reduced the perceived relevance of counselling, and often led to the discontinuation of counselling.

3.2.2. Theme Two: Privacy at the Expense of Connection

Across all participants, confidentiality concerns shaped how and where they sought support, with many avoiding disclosure to family or limiting visibility within the university setting.

Don’t Speak to Anyone About It.

Most participants avoided discussing counselling with family members, especially parents, due to anticipated stigma:

“I can’t have a conversation with them, that was not an option for me.”
(IP3)

On the contrary, one participant described encouragement from both of her parents:

“It was one of my mum’s priorities…she initially gave me instructions of what to do…otherwise I wouldn’t have really looked into it honestly.”
(IP4)

This subtheme of keeping quiet about accessing counselling was strongly linked to the subtheme of Visibility expressed as a fear of being seen whilst waiting for a counselling session.

Distance.

Some participants sought counselling outside university services to maintain separation between academic and person life:

“I felt like I’d rather not have my umm like mental health details associated with my university. So, I’d rather get something in the community. …It felt more anonymous.”
(IP2)

Visibility.

Many participants valued remote sessions from home or a private space, where they can reduce the risk of being seen (by either peers or lecturers) accessing services:

“I actually prefer virtual meetings, so I don’t have to go anywhere.”
(IP2)

However, others felt online delivery hindered connection, influencing dropout decisions:

“(The assessment was online) was fine but it was harder to connect because of the setup…I didn’t follow through with an appointment because it was online.”
(IP1)

3.2.3. Theme Three: Undeservingness

Participants frequently minimised their own need, prioritising others’ needs or the perceived busyness of the service over their own. These perceptions, coupled with their own doubts about the usefulness of counselling, contributed to attrition.

Not Important Enough.

Some participants felt their difficulties were not worth the therapist’s time in sessions or that their symptoms were not severe enough:

“I was sort of placed on the back burner.”
(SP2)

Others’ Needs Are Greater.

Several participants assumed that others need was greater:

“I assumed other people must have much more serious issues.”
(SP6)

The Service Is Really Busy.

Participants perceptions that services are overwhelmed, reinforced their reluctance to continue and not wanting to be a burden

“I also know that [there are] a lot of students reaching out for help through them, so, they might be really busy.”
(IP2)

It Won’t Help Me.

Several participants felt that sessions were ineffective and did not feel they were getting any benefit from them:

“I just didn’t see the point of you know carrying on.” (IP1) and “I did not feel it would help me.”
(SP8)

Others decided to manage independently and chose to cope alone.

“I guess I feel like it doesn’t help me necessarily…maybe I can handle it on my own and do it myself … I may as well just get on with.”
(IP4)

3.2.4. Theme Four: Time Is of the Essence

Timing emerged as a major determinant of engagement and participants’ desire to continue with sessions. Participants cited waiting lists, inconvenient scheduling, and session length restrictions as barriers to engagement.

Waiting Time.

Delays between initial assessment and first session were attributed to reduced motivation:

“The counselling session was months away…so wouldn’t be useful at all to me.”
(SP1)

Timing.

The lack of flexibility in university student wellbeing services and fixed session times, often clashed with responsibilities, such as those with caring responsibilities.

“Time ended up clashing with picking my child up from school, so I cancelled my appointment.”
(SP5)

Restriction.

Some international students found the standard 50-min restrictive.

“It felt it felt rushed…I couldn’t go into a lot of depth…and it felt very superficial from their side as well.”
(IP1)

Comparisons with more flexible, non-western models in home countries reinforced this perception:

“My psychologist would just see me until I would like stop talking or until their next client was there. Sometimes I was there an hour and a half, 2 hr sometimes…it was kind of fluid and flexible.”
(IP5)

3.3. Mixed-Methods Results

The quantitative and qualitative strands were integrated to examine areas of convergence and divergence. While statistical analyses identified higher cultural and access-related barriers among ethnic minority students, qualitative accounts elaborated on how these barriers manifested in practice, such as cultural misunderstandings, confidentiality concerns, and time constraints. A joint-display table (Table 10) was developed to illustrate these cross-strand connections and highlight implications for practice.

The sequential explanatory mixed-methods design of this study provides both depth and context to the quantitative results, illustrating how barriers to seeking university student wellbeing services are higher for students from ethnic minority backgrounds. Participants described cultural misunderstandings, assumptions based on ethnicity, and a lack of cultural fit within the therapeutic relationship. These experiences often eroded trust and contributed to disengagement.

Quantitative findings showed no significant ethnic differences in service utilisation; dropout was mirrored by the qualitative accounts. While some participants actively engaged with services, others withdrew due to confidentiality concerns, perceived undeservingness, and practical barriers such as inconvenient scheduling or long waiting times. The Time is of the Essence theme echoed the quantitative finding that practical barriers—especially lack of access—were higher among ethnic minority students, suggesting that structural delays can interact with cultural and interpersonal factors to discourage ongoing engagement.

Taken together, the integrated findings indicate that while overt service usage rates may appear similar across ethnic groups, the underlying experiences, decision-making processes, and perceived barriers differ in ways that are culturally and contextually shaped. These insights informed the interpretation of the quantitative results and highlight areas for targeted intervention, particularly in enhancing cultural competence, improving relational fit, and addressing service accessibility.

4. Discussion

This mixed-methods study set out to examine ethnic differences in university student’s mental health difficulties, help-seeking behaviours, service utilisation, and barriers to counselling, integrating quantitative survey data with qualitative interview and open-text responses. Few significant differences were observed in service use or dropout between White and ethnic minority students; however, ethnic minority students reported higher perceived barriers, particularly in-group stigma, cultural values, and lack of access. Qualitative findings provided essential context for these patterns, revealing that cultural incongruence in the therapeutic relationship, confidentiality concerns, perceptions of undeservingness, and practical time-related constraints shaped engagement with services. Together, the findings suggest that while service usage rates may be statistically similar across groups, the underlying experiences, motivations, and disengagement processes differ in culturally specific ways. These insights underscore the importance of moving beyond usage metrics to consider the qualitative dimensions of engagement and point to the need for culturally responsive and flexible counselling provision in higher education settings.

Consistent with the Health Belief Model (HBM) construct of perceived severity, both White and the ethnic minority students reported higher rates of clinically significant psychological distress. However, contrary to expectations, no group differences were observed on the CCAPS-34 Distress Index. The absence of group differences may reflect the institutional context, in which ethnic minority students formed the majority, potentially enhancing perceived self-efficacy to manage mental health through greater sense of belonging (Campbell-Whatley et al., 2021). Prior research also shows that increased sense of belonging reduces anxiety and depression among minority students (Gummadam et al., 2016), potentially offsetting distress disparities.

White students’ higher scores on generalised anxiety, alcohol use and eating concerns suggest differences in perceived susceptibility to certain mental health difficulties. There are several possible explanations for the current findings. There is some evidence to suggest that eating concern differences align with the literature linking the thin-ideal prevalent in Western cultural contexts (Keel & Klump, 2003; Makino et al., 2004). The elevated alcohol use may partially reflect cultural or religious norms among ethnic minority participants, such as abstention in Muslim communities (though religion was not measured in the current study). These patterns suggest that while overall severity may not differ, specific symptom domains are culturally specific.

The most notable ethnic differences emerged in perceived barriers, a central HBM construct. Ethnic minority students scored higher on in-group stigma and cultural values (partial η² = 0.184, large effect), consistent with UK studies identifying these as significant deterrents to help-seeking (Arday, 2018; Sancho & Larkin, 2020). The qualitative themes of Therapeutic Relationship and Privacy at the Expense of Connection illustrate how such barriers operate: cultural misunderstandings, assumptions based on ethnicity, and fear of disclosure to family or community reduced perceived benefits of counselling and eroded trust.

Interestingly, no group differences were found in societal stigma, self-stigma and beliefs about mental illness, which are in contrast to meta-analytic findings that stigma disproportionately affects minority group help-seeking behaviours (Clement et al., 2015). This may reflect the composition of the sample, in which ethnic minority students were not numerically marginalised. However, as the HBM suggests, even in low-stigma environments, cultural values and in-group expectations can influence whether services are perceived as culturally congruent and personally relevant.

Ethnic minority students reported greater lack of access, aligning with prior research showing structural constraints can disproportionately impact marginalised groups (Jennings et al., 2015; Marsh & Wilcoxon, 2015). The qualitative theme of Time is of the Essence illustrated how long waiting times, inflexible scheduling, and session restrictions acted as both perceived and actual barriers—limiting timely engagement and reducing perceived benefits. Such barriers may outweigh perceived severity and perceived benefits, particularly when logistical challenges reinforce feelings of undeservingness or low self-efficacy.

Contrary to much of the existing literature (Broman, 2012; Campbell et al., 2022; Lipson et al., 2022; Miranda et al., 2015), no ethnic differences emerged in help-seeking, initial assessment attendance, or session continuation. This may reflect the influence of cues to action, such as institutional outreach, peer encouragement, or proactive service promotion, which can prompt engagement regardless of perceived barriers (Gee et al., 2020; Glickman et al., 2021). Post hoc analyses identified two significant predictors of help-seeking among ethnic minority students: cultural values (negative association) and psychological distress (positive association). When distress (perceived severity) is high enough, it may override cultural barriers (Broglia et al., 2021a; Cage et al., 2020), whereas strong cultural norms can suppress perceived benefits and thus reduce engagement (Arday, 2018; Memon et al., 2016).

Although dropout rates were higher among ethnic minority students than White students (45.8% vs. 30%), the difference was not statistically significant. Qualitative findings, however, suggest that perceived barriers—particularly therapeutic misalignment and lack of cultural attunement—were central to disengagement. These findings contrast with findings from several US-based studies, which reported significant ethnic disparities in counselling dropout rates (de Haan et al., 2018; Kilmer et al., 2019; Kivlighan et al., 2019; Levy et al., 2005). This may be further explained by a mismatch between therapist approach and client expectations reduces perceived benefits and can undermine continued participation even after initial engagement.

Participants’ preference for therapists from a similar ethnic background reflects the importance of tailoring services to enhance perceived benefits and self-efficacy. While ethnicity matching was not universally desired, the lack of discussion on culture during sessions and perceived misunderstandings suggest a lack of counsellor cultural competence. This observation is supported by King (2022) who found only 18.7% of counsellors discussed issues related to race and ethnicity in counselling. Literature suggests that explicitly acknowledging cultural differences can enhance therapeutic alliance and perceived counsellor credibility (Zhang & Burkard, 2008), although such discussions must be handled skilfully to beneficial.

Confidentiality concerns and the preference to separate mental health from academic or family contexts influenced whether counselling was perceived as safe and beneficial. Remote delivery reduced visibility and stigma for some participants but hindered relational connection for others—highlighting the importance of offering choice. This is reflected in the literature where it has been posited that online therapy acts to reduce self-stigma and to encourage help-seeking (Wallin et al., 2018). However, this is not supported by other studies which found that stigma persisted even with online therapy (Hanley & Wyatt, 2021). What is clear from the current results and the existing literature, tailoring delivery modes can increase perceived benefits for different minority groups.

Feelings of unworthiness and perceptions of burdening already overstretched student services reduced participants’ perceived benefits of counselling and lowered self-efficacy for help-seeking. Consistent with prior research (Broglia et al., 2021b; J. E. Kim & Zane, 2016; Tang & Masicampo, 2018), these perceptions can lead to self-deprioritisation even in the presence of high distress. Long wating lists, rigid session timings, and dissatisfaction with the 50 min session format emerged as high-impact perceived barriers to engagement. There seems to be very limited research on the impact of the session length on client satisfaction or its effect on dropout, particularly when students had already expended significant effort to seek help. These barriers underscore the need for more flexible and responsive service models that consider students varied and time-constrained lives (Priestley et al., 2022).

4.1. Limitations

Several limitations should be considered when interpreting these findings. The study sample was drawn from University of London institutions with highly diverse student populations, in which ethnic minority students were not numerically underrepresented. This limits generalisability of the findings to other universities and regions of the UK where ethnic minority students are in the minority and may experience different forms of marginalisation. Recruitment also relied on voluntary response sampling, which may have introduced self-selection bias. Students with a pre-existing interest in mental health, more positive attitudes toward counselling, or prior service use may have been more likely to participate. Further to this, response rates by individual recruitment channels were not systematically recorded, which reinforces the self-selection concerns already noted.

This could have influenced the relatively high help-seeking rates observed. While the sample size for the quantitative phase (n = 203) was adequate for group comparisons, it limited the ability to analyse within-group differences between specific ethnic minority subgroups. For example, Black students and those who had dropped out of counselling were underrepresented, which may have obscured subgroup-specific patterns. Mental health measures were also self-reported and may be subject to social desirability bias, particularly in cultures where discussing mental health is stigmatised (Latkin et al., 2017). The use of anonymised surveys likely mitigated some of this bias, but underreporting—especially among certain cultural groups—remains possible (Reisinger, 2022). Finally, the gender distribution in the sample was predominantly female at both stages of data collection. As a result, the perspectives represented in this study are weighted toward female viewpoints. This imbalance is a limitation, given that university student wellbeing services are accessed and needed by individuals of all genders.

4.2. Future Directions

Future research should extend this work to more varied university or regional contexts, particularly institutions where ethnic minority students are in a minority, to examine whether patterns observed in the current study—such as similar service utilisation rates but higher perceived barriers—persist in different demographic environments. This would be taking an anti-racist approach using a strengths approach decentring Whiteness and focusing on the strengths of what has worked well (Nelson & Zanti, 2020).

Further investigation is needed into within-group differences among ethnic minority students, especially among Black, mixed-heritage, and other underrepresented groups in the current study sample. Employing purposive sampling or co-created recruitment with cultural groups and networks could enhance engagement from these groups from the beginning.

Given the study’s finding that cultural values and in-group stigma are critical barriers, future work should test targeted interventions designed to address these factors—such as cultural competence training for counsellors, peer-led outreach programmes, and culturally adapted service models—using experimental or quasi-experimental designs.

Research should also explore the impact of service accessibility innovations—such as same-day appointments, culturally matched counsellors, and hybrid delivery modes—on reducing perceived barriers and improving retention. Longitudinal mixed-methods studies could clarify how perceived severity, susceptibility, and benefits shift over time and how these shifts influence sustained engagement.

Finally, dropout from university wellbeing services remains under-research, particularly in UK contexts. Larger-scale quantitative studies should examine predictors of dropout, incorporating both service-level factors (e.g., waiting times, mode of delivery) and individual-level factors (e.g., stigma, cultural values, self-efficacy) to inform retention strategies.

5. Conclusions

This study demonstrates that while ethnic minority and White students reported similar rates of help-seeking, initial service use, and dropout, the underlying processes shaping engagement were distinct. Ethnic minority students reported significantly higher perceived barriers in the current survey data—particularly in-group stigma, cultural values, and lack of access—which were reinforced by qualitative themes of Therapeutic Relationship, Privacy at the Expense of Connection, Undeservingness, and Time is of the Essence. These barriers reduced the perceived benefits of counselling and, in some cases, undermined self-efficacy for seeking and sustaining support. Conversely, higher levels of psychological distress increased the likelihood of help-seeking, suggesting that perceived severity can, at times, override cultural and practical constraints.

These findings have practical implications for higher education counselling services. Efforts to enhance cultural responsiveness, increase therapist diversity, provide flexible and accessible delivery options, and strengthen culturally tailored outreach are likely to improve both perceived benefits and actual access. By explicitly addressing cultural values, in-group stigma, and logistical barriers, universities can reduce attrition and foster sustained engagement. This study reveals important insights for the need to move beyond utilisation rates and towards more nuanced understanding of how cultural, interpersonal and structural factors intersect to shape student engagement with university wellbeing services. Embedding culturally attuned, flexible, and student-centred models of care is essential for ensuring that all students—not just those who overcome the highest barriers—can access and benefit from timely psychological support.

Author Contributions

Conceptualization, R.T., J.D.J.N., A.-K.F.; methodology, R.T., J.D.J.N., A.-K.F.; formal analysis, R.T.; curation, R.T.; writing—original draft preparation, R.T.; writing—review and editing, J.D.J.N., A.-K.F.; supervision, A.-K.F., J.D.J.N. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by City, University of London Department of Psychology Ethics Committee (Ethics Code: ETH2122-164224; October 2022).

Informed Consent Statement

Informed consent was obtained from all participants involved in the study.

Data Availability Statement

Data are not publicly available due to ethical approval limitation. Additional information could be obtained by sending an email request to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A. Interview Schedule

(1): How did you first find out about the support available and how did you make contact?
(2): How long did it take to be offered an assessment?
(3): How many sessions were you offered?
(4): How many sessions did you complete before dropping out?
(5): What was the experience like of reaching out for help and then waiting for the first session?
(6): What was your experience like of the first session?
(7): Could you tell me about your decision not to continue with sessions?
(8): Is there anything else about your experience of stopping sessions that we haven’t covered that you would like to share?

References

Arday, J. (2018). Understanding mental health: What are the issues for black and ethnic minority students at university? Social Sciences, 7(10), 196. [Google Scholar] [CrossRef]
Arday, J., Branchu, C., & Boliver, V. (2022). What do we know about black and minority ethnic (BAME) participation in UK higher education? Social Policy and Society: A Journal of the Social Policy Association, 21(1), 12–25. [Google Scholar] [CrossRef]
Auerbach, R. P., Alonso, J., Axinn, W. G., Cuijpers, P., Ebert, D. D., Green, J. G., Hwang, I., Kessler, R. C., Liu, H., & Mortier, P. (2016). Mental disorders among college students in the World Health Organization world mental health surveys. Psychological Medicine, 46(14), 2955–2970. [Google Scholar] [CrossRef] [PubMed]
Baik, C., Larcombe, W., & Brooker, A. (2019). How universities can enhance student mental wellbeing: The student perspective. Higher Education Research and Development, 38(4), 674–687. [Google Scholar] [CrossRef]
Baker, C. (2020). Mental health statistics for England: Prevalence, services and funding. House of Commons Library. [Google Scholar]
Bird, M. D., Chow, G. M., & Cooper, B. T. (2020). Student-athletes’ mental health help-seeking experiences: A mixed methodological approach. Journal of College Student Psychotherapy, 34(1), 59–77. [Google Scholar] [CrossRef]
Bolton, P. (2024). Higher education student numbers. Available online: https://researchbriefings.files.parliament.uk/documents/CBP-7857/CBP-7857.pdf (accessed on 1 June 2025).
Braun, V., & Clarke, V. (2022). Thematic analysis: A practical guide. SAGE. [Google Scholar]
Broglia, E., Millings, A., & Barkham, M. (2021a). Student mental health profiles and barriers to help-seeking: When and why students seek help for a mental health concern. Counselling & Psychotherapy Research, 21(4), 816–826. [Google Scholar] [CrossRef]
Broglia, E., Ryan, G., Williams, C., Fudge, M., Knowles, L., Turner, A., Dufour, G., Percy, A., Barkham, M., & on behalf of the SCORE Consortium. (2021b). Profiling student mental health and counselling effectiveness: Lessons from four UK services using complete data and different outcome measures. British Journal of Guidance & Counselling, 51, 204–222. [Google Scholar] [CrossRef]
Broman, C. L. (2012). Race differences in the receipt of mental health services among young adults. Psychological Services, 9(1), 38–48. [Google Scholar] [CrossRef]
Bryant, A., Cook, A., Egan, H., Wood, J., & Mantzios, M. (2022). Help-seeking behaviours for mental health in higher education. Journal of further and Higher Education, 46(4), 522–534. [Google Scholar] [CrossRef]
Cage, E., Stock, M., Sharpington, A., Pitman, E., & Batchelor, R. (2020). Barriers to accessing support for mental health issues at university. Studies in Higher Education, 45(8), 1637–1649. [Google Scholar] [CrossRef]
Campbell, F., Blank, L., Cantrell, A., Baxter, S., Blackmore, C., Dixon, J., & Goyder, E. (2022). Factors that influence mental health of university and college students in the UK: A systematic review. BMC Public Health, 22(1), 1778. [Google Scholar] [CrossRef]
Campbell-Whatley, G., O’Brien, C., Reddig, K., Sun, T., & Freeman-Green, S. (2021). Non-majority student perceptions of diversity and inclusion at a PWI and an HBCU. Journal for Multicultural Education, 15(3), 253–269. [Google Scholar] [CrossRef]
Clement, S., Schauman, O., Graham, T., Maggioni, F., Evans-Lacko, S., Bezborodovs, N., Morgan, C., Rüsch, N., Brown, J. S. L., & Thornicroft, G. (2015). What is the impact of mental health-related stigma on help-seeking? A systematic review of quantitative and qualitative studies. Psychological Medicine, 45(1), 11–27. [Google Scholar] [CrossRef] [PubMed]
Creswell, J. W., & Plano Clark, V. L. (2011). Designing and conducting mixed methods research (2nd ed.). SAGE. [Google Scholar]
Creswell, J. W., Plano Clark, V. L., Gutmann, M. L., & Hanson, W. E. (2003). An expanded typology for classifying mixed methods research into designs. In A. Tashakkori, & Y. C. Teddlie (Eds.), Handbook of mixed methods in social and behavioral research (pp. 209–240). SAGE. [Google Scholar]
de Haan, A. M., Boon, A. E., de Jong, J. T. V. M., & Vermeiren, R. R. J. M. (2018). A review of mental health treatment dropout by ethnic minority youth. Transcultural Psychiatry, 55(1), 3–30. [Google Scholar] [CrossRef]
Faul, F., Erdfelder, E., Lang, A. G., & Buchner, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175–191. [Google Scholar] [CrossRef] [PubMed]
Gee, C. B., Khera, G. S., Poblete, A. T., Kim, B., & Buchwach, S. Y. (2020). Barriers to mental health service use in Asian American and European American college students. Asian American Journal of Psychology, 11(2), 98–107. [Google Scholar] [CrossRef]
Glickman, K. L., Smith, S. W., & Woods, E. C. (2021). Psychological distress, attitudes toward seeking help, and utilization of college counseling at a predominantly minority college. Journal of American College Health, 71, 765–774. [Google Scholar] [CrossRef] [PubMed]
Gummadam, P., Pittman, L. D., & Ioffe, M. (2016). School belonging, ethnic identity, and psychological adjustment among ethnic minority college students. The Journal of Experimental Education, 84(2), 289–306. [Google Scholar] [CrossRef]
Hanley, T., & Wyatt, C. (2021). A systematic review of higher education students’ experiences of engaging with online therapy. Counselling and Psychotherapy Research, 21(3), 522–534. [Google Scholar] [CrossRef]
Harrold, K., Fett, A. K., & Haenschel, C. (2024). The effects of social identity incompatibility on student mental health. Education Sciences, 14(12), 1407. [Google Scholar] [CrossRef]
Higher Education Student Statistics (HESA). (2023). Higher education student statistics: UK, 2022/23—Student numbers and characteristics. Available online: https://www.hesa.ac.uk/news/08-08-2024/sb269-higher-education-student-statistics/numbers (accessed on 1 June 2025).
Hirai, M., & Clum, G. A. (2000). Development, reliability, and validity of the beliefs toward mental illness scale. Journal of Psychopathology and Behavioral Assessment, 22(3), 221–236. [Google Scholar] [CrossRef]
IBM Corp. (2023). IBM SPSS statistics for mac (Version 29.0.2.0) [Computer software]. IBM Corp. [Google Scholar]
Jennings, K. S., Cheung, J. H., Britt, T. W., Goguen, K. N., Jeffirs, S. M., Peasley, A. L., & Lee, A. C. (2015). How are perceived stigma, self-stigma, and self-reliance related to treatment-seeking? A three-path model. Psychiatric Rehabilitation Journal, 38(2), 109–116. [Google Scholar] [CrossRef] [PubMed]
Jennings, K. S., Goguen, K. N., Britt, T. W., Jeffirs, S. M., Wilkes, J. R. I., II, Brady, A. R., Pittman, R. A., & DiMuzio, D. J. (2017). The role of personality traits and barriers to mental health treatment seeking among college students. Psychological Services, 14(4), 513–523. [Google Scholar] [CrossRef] [PubMed]
Johnson, R. B., & Onwuegbuzie, A. J. (2004). Mixed methods research: A research paradigm whose time has come. Educational Researcher, 33(7), 14–26. [Google Scholar] [CrossRef]
Keel, P. K., & Klump, K. L. (2003). Are eating disorders culture-bound syndromes? Implications for conceptualizing their etiology. Psychological Bulletin, 129(5), 747–769. [Google Scholar] [CrossRef]
Kilmer, E. D., Villarreal, C., Janis, B. M., Callahan, J. L., Ruggero, C. J., Kilmer, J. N., Love, P. K., & Cox, R. J. (2019). Differential early termination is tied to client race/ethnicity status. Practice Innovations, 4(2), 88. [Google Scholar] [CrossRef]
Kim, J. E., & Zane, N. (2016). Help-seeking intentions among Asian American and White American students in psychological distress: Application of the health belief model. Cultural Diversity and Ethnic Minority Psychology, 22(3), 311–321. [Google Scholar] [CrossRef]
Kim, S. B., & Lee, Y. J. (2022). Factors associated with mental health help-seeking among Asian Americans: A systematic review. Journal of Racial and Ethnic Health Disparities, 9(4), 1276–1297. [Google Scholar] [CrossRef]
King, K. M. (2022). Client perspectives on broaching intersectionality in counseling. Journal of Humanistic Counseling, 61(1), 30–42. [Google Scholar] [CrossRef]
Kivlighan, D. M., Jung, A., Berkowitz, A. B., Hammer, J. H., & Collins, N. M. (2019). ‘To show or no-show’? Therapist racial-ethnic disparities in clients’ nonattendance in therapy. Journal of College Student Psychotherapy, 33(1), 1–13. [Google Scholar] [CrossRef]
Komiya, N., Good, G. E., & Sherrod, N. B. (2000). Stigma scale for receiving psychological help. Journal of Counseling Psychology, 47(1), 138–143. [Google Scholar] [CrossRef]
Langley, E. L., Clark, G., Murray, C., & Wootton, B. M. (2021). The utility of the health belief model variables in predicting help-seeking intention for depressive symptoms. Australian Psychologist, 56(3), 233–244. [Google Scholar] [CrossRef]
Langley, E. L., Wootton, B. M., & Grieve, R. (2018). The utility of the health belief model variables in predicting help-seeking intention for anxiety disorders. Australian Psychologist, 53(4), 291–301. [Google Scholar] [CrossRef]
Latkin, C. A., Edwards, C., Davey-Rothwell, M. A., & Tobin, K. E. (2017). The relationship between social desirability bias and self-reports of health, substance use, and social network factors among urban substance users in Baltimore, Maryland. Addictive Behaviors, 73, 133–136. [Google Scholar] [CrossRef]
Levy, J. J., Thompson-Leonardelli, K., Smith, N. G., & Coleman, M. N. (2005). Attrition after intake at a university counseling center: Relationship among client race, problem type, and time on a waiting list. Journal of College Counseling, 8(2), 107–117. [Google Scholar] [CrossRef]
Li, W., Denson, L. A., & Dorstyn, D. S. (2018). Understanding Australian university students’ mental health help-seeking: An empirical and theoretical investigation. Australian Journal of Psychology, 70(1), 30–40. [Google Scholar] [CrossRef]
Lipson, S. K., Zhou, S., Abelson, S., Heinze, J., Jirsa, M., Morigney, J., Patterson, A., Singh, M., & Eisenberg, D. (2022). Trends in college student mental health and help-seeking by race/ethnicity: Findings from the national healthy minds study, 2013–2021. Journal of Affective Disorders, 306, 138–147. [Google Scholar] [CrossRef]
Locke, B. D., McAleavey, A. A., Zhao, Y., Lei, P., Hayes, J. A., Castonguay, L. G., Li, H., Tate, R., & Lin, Y. (2012). Development and initial validation of the counseling center assessment of psychological symptoms-34. Measurement and Evaluation in Counseling and Development, 45(3), 151–169. [Google Scholar] [CrossRef]
Makino, M., Tsuboi, K., & Dennerstein, L. (2004). Prevalence of eating disorders: A comparison of Western and non-Western countries. Medscape General Medicine, 6(3), 49. [Google Scholar] [PubMed]
Marsh, C. N., & Wilcoxon, S. A. (2015). Underutilization of mental health services among college students: An examination of system-related barriers. Journal of College Student Psychotherapy, 29(3), 227–243. [Google Scholar] [CrossRef]
Memon, A., Taylor, K., Mohebati, L. M., Sundin, J., Cooper, M., Scanlon, T., & de Visser, R. (2016). Perceived barriers to accessing mental health services among black and minority ethnic (BME) communities: A qualitative study in Southeast England. BMJ Open, 6(11), e012337. [Google Scholar] [CrossRef] [PubMed]
Miranda, R., Soffer, A., Polanco-Roman, L., Wheeler, A., & Moore, A. (2015). Mental health treatment barriers among racial/ethnic minority versus white young adults 6 months after intake at a college counseling center. Journal of American College Health, 63(5), 291–298. [Google Scholar] [CrossRef] [PubMed]
Morena, A. L., Gaias, L. M., & Wang, Y. (2024). Profiles of mental health help-seeking barriers in college students. Stigma and Health. [Google Scholar] [CrossRef]
National Collaborating Centre for Mental Health. (2023). Ethnic inequalities in improving access to psychological therapies (IAPT): Full report. Available online: https://www.nhsrho.org/wp-content/uploads/2023/10/Ethnic-Inequalities-in-Improving-Access-to-Psychological-Therapies-IAPT.Full-report.pdf (accessed on 1 June 2025).
Nelson, A. L. H., & Zanti, S. (2020). A framework for centering racial equity throughout the administrative data life cycle. International Journal of Population Data Science, 5(1), 1367. [Google Scholar] [CrossRef] [PubMed]
Nobiling, B. D., & Maykrantz, S. A. (2017). Exploring perceptions about and behaviors related to mental illness and mental health service utilization among college students using the health belief model (HBM). American Journal of Health Education, 48(5), 306–319. [Google Scholar] [CrossRef]
Nowell, L. S., Norris, J. M., White, D. E., & Moules, N. J. (2017). Thematic analysis: Striving to meet the trustworthiness criteria. International Journal of Qualitative Methods, 16(1), 1–13. [Google Scholar] [CrossRef]
Olaniyan, F. (2021). Paying the widening participation penalty: Racial and ethnic minority students and mental health in British universities. Analyses of Social Issues and Public Policy, 21(1), 761–783. [Google Scholar] [CrossRef]
Panicker, A. S., Samskani, M. S., Vimala, S., & Poornima, G. (2023). Psychological help-seeking among undergraduate medical students: A comparative study. International Perspectives in Psychology: Research, Practice, Consultation, 12(4), 238–244. [Google Scholar] [CrossRef]
Priestley, M., Broglia, E., Hughes, G., & Spanner, L. (2022). Student perspectives on improving mental health support services at university. Counselling and Psychotherapy Research, 22(1), 197–206. [Google Scholar] [CrossRef]
Reisinger, J. (2022). Subjective well-being and social desirability. Journal of Public Economics, 214, 104745. [Google Scholar] [CrossRef]
Rosenstock, I. M., Strecher, V. J., & Becker, M. H. (1988). Social learning theory and the health belief model. Health Education Quarterly, 15(2), 175–183. [Google Scholar] [CrossRef]
Sagar-Ouriaghli, I., Brown, J. S. L., Tailor, V., & Godfrey, E. (2020). Engaging male students with mental health support: A qualitative focus group study. BMC Public Health, 20(1), 1159. [Google Scholar] [CrossRef]
Sancho, T. N., & Larkin, M. (2020). “We need to slowly break down this barrier”: Understanding the barriers and facilitators that Afro-Caribbean undergraduates perceive towards accessing mental health services in the UK. Journal of Public Mental Health, 19(1), 63–81. [Google Scholar] [CrossRef]
Schochet, E., Haenschel, C., Gaigg, S., & Fett, A. K. (2023). Mental health, loneliness and stress of Asian, Black and White UK university students before and during the COVID-19 pandemic. Social Sciences & Humanities Open, 8(1), 100697. [Google Scholar] [CrossRef]
Shea, M., Wong, Y. J., Nguyen, K. K., & Gonzalez, P. D. (2019). College students’ barriers to seeking mental health counseling: Scale development and psychometric evaluation. Journal of Counseling Psychology, 66(5), 626–639. [Google Scholar] [CrossRef] [PubMed]
Soorkia, R., Snelgar, R., & Swami, V. (2011). Factors influencing attitudes towards seeking professional psychological help among South Asian students in Britain. Mental Health, Religion & Culture, 14(6), 613–623. [Google Scholar] [CrossRef]
Tang, Y., & Masicampo, E. J. (2018). Asian American college students, perceived burdensomeness, and willingness to seek help. Asian American Journal of Psychology, 9(4), 344–349. [Google Scholar] [CrossRef]
Thorley, C. (2017). Not by degrees: Improving student mental health in the UK’s universities. IPPR. Available online: https://www.ippr.org/articles/not-by-degrees (accessed on 1 June 2025).
Vogel, D. L., Wade, N. G., & Haake, S. (2006). Measuring the self-stigma associated with seeking psychological help. Journal of Counseling Psychology, 53(3), 325–337. [Google Scholar] [CrossRef]
Wallin, E., Maathz, P., Parling, T., & Hursti, T. (2018). Self-stigma and the intention to seek psychological help online compared to face-to-face. Journal of Clinical Psychology, 74(7), 1207–1218. [Google Scholar] [CrossRef]
Zhang, N., & Burkard, A. W. (2008). Client and counselor discussions of racial and ethnic differences in counseling: An exploratory investigation. Journal of Multicultural Counseling and Development, 36(2), 77–87. [Google Scholar] [CrossRef]

Table 1. Demographics characteristics of the quantitative sample in Phase 1 (n = 203).

Variables		Mean (SD)
Age		23.1 (7.3), range 18–55
Gender		n (%)
	Female	169 (84.9)
	Male	25 (12.6)
	Other	5 (2.5)
Ethnicity		n (%)
	White or White British	77 (38.7)
	Asian or Asian British	69 (34.7)
	Black or Black British	13 (6.5)
	Mixed or multiple ethnic groups	17 (8.5)
	Other ethnic group	23 (11.6)
Level of Study		n (%)
	Undergraduate	149 (74.9)
	Postgraduate	50 (25.1)
Student Status		n (%)
	Home	155 (77.9)
	International	44 (22.1)

Note: Student status refers to whether participants were home students (UK-domiciled) or international students (non-UK domiciled).

Table 2. Demographic characteristics of interviewed student participants in Phase 2 (n = 5).

	Gender	Ethnicity	Student Level	Student Status
Interviewee 1	Female	Asian/Asian British	Undergraduate	Home
Interviewee 2	Female	Asian/Asian British	Undergraduate	International
Interviewee 3	Male	Asian/Asian British	Postgraduate	International
Interviewee 4	Female	Black/Black British	Undergraduate	Home
Interviewee 5	Female	Asian/Asian British	Postgraduate	International

Note: Student status refers to whether participants were home students (UK-domiciled) or international students (non-UK domiciled).

Table 3. Adjusted means and standard deviations for CCAPS-34 subscales by ethnicity (n = 203).

Measure	Ethnicity	n	M	SD	Adj Mean
Distress Index	Ethnic minority	126	1.78	0.78	1.76
	White	77	1.92	0.88	1.97
Depression	Ethnic minority	126	1.60	0.97	1.57
	White	77	1.76	1.09	1.80
Generalized Anxiety	Ethnic minority	126	1.84	0.96	1.81
	White	77	2.16	1.07	2.20
Social Anxiety	Ethnic minority	126	2.06	0.96	2.04
	White	77	2.16	1.01	2.20
Academic Distress	Ethnic minority	126	2.35	0.92	2.32
	White	77	2.03	1.15	2.09
Eating Concerns	Ethnic minority	126	1.42	1.29	1.40
	White	77	1.91	1.43	1.92
Hostility	Ethnic minority	126	1.10	0.87	1.09
	White	77	1.12	0.88	1.13
Alcohol use	Ethnic minority	126	0.38	0.73	0.36
	White	77	1.15	1.17	1.18

Note: Adjusted means control for age. Higher scores indicate greater symptom severity. CCAPS-34 = Counseling Centre Assessment of Psychological Symptoms-34.

Table 4. ANCOVA results for CCAPS-34 subscales by ethnicity (n = 203).

Measure	F	p	Partial η²
Distress Index	3.09	0.080	0.02
Depression	2.25	0.135	0.01
Generalized Anxiety	6.74	0.010	0.03
Social Anxiety	1.15	0.284	0.01
Academic Distress	2.38	0.125	0.01
Eating Concerns	6.78	0.010	0.03
Hostility	0.12	0.735	0.00
Alcohol use	36.41	<0.001	0.15

Notes. Partial η² = effect size (small: 0.01, medium: 0.06, large: 0.14).

Table 5. Ethnic Group Differences in Clinically Significant CCAPS-34 Scores for All Subscales (n = 203).

		High—Clinically Significant		Low/Medium
Measure	Ethnicity	n	%	n	%	χ²
Distress Index	Ethnic minority	39	31	87	69.0	1.92
	White	32	41.6	45	58.4
Depression	Ethnic minority	52	41.3	74	58.7	1.34
	White	39	50.6	38	49.4
Generalized Anxiety	Ethnic minority	51	40.5	75	59.5	2.65
	White	41	53.2	36	46.8
Social Anxiety	Ethnic minority	42	33.3	84	66.7	0.72
	White	31	40.3	46	59.7
Academic Distress	Ethnic minority	65	51.6	61	48.4	1.13
	White	33	42.9	44	57.1
Eating Concerns	Ethnic minority	51	40.5	75	59.5	* 4.68
	White	44	57.1	33	42.9
Hostility	Ethnic minority	50	39.7	76	60.3	0.10
	White	28	36.4	49	63.6
Alcohol Use	Ethnic minority	19	15.1	107	84.9	* 12.28
	White	29	37.7	48	62.3

Note: * indicates group differences that are significant at p < 0.05.

Table 6. Adjusted means and standard deviations for SSRPH, SSSH, BMHC and BMI subscales by ethnicity (n = 203).

Measure		Ethnicity	n	M	SD	Adj Mean
Societal Stigma (SSRPH)		Ethnic minority	126	4.76	3.07	4.72
		White	77	4.47	3.02	4.54
Self-Stigma (SSSH)		Ethnic minority	126	23.68	6.35	23.51
		White	77	23.29	7.84	23.54
Barriers to Seeking Help (BMHC)		Ethnic minority	126	81.02	13.91	80.81
		White	77	73.03	14.53	73.38
	Negative Perceived Value	Ethnic minority	126	14.17	4.64	14.04
		White	77	13.86	5.21	14.07
	In-group Stigma	Ethnic minority	126	15.22	4.05	15.18
		White	77	12.86	4.15	12.92
	Discomfort with Emotions	Ethnic minority	126	18.25	4.94	18.08
		White	77	18.21	4.81	18.49
	Cultural Values	Ethnic minority	126	19.08	3.84	19.12
		White	77	15.08	4.62	15.01
	Lack of knowledge	Ethnic minority	126	8.96	4.32	8.90
		White	77	8.79	4.53	8.89
	Lack of Access	Ethnic minority	126	14.29	4.80	14.37
		White	77	13.03	4.65	12.90
Beliefs about Mental Illness (BMI)		Ethnic minority	126	40.78	16.54	40.49
		White	77	35.87	16.70	36.34

Note: Adjusted means control for age. SSRPH = Stigma Scale for Receiving Psychological Help. SSSH = Self-Stigma for Seeking Help. BMHC = Barriers to Seeking Mental Health Counseling, BMI = Beliefs toward Mental Illness.

Table 7. ANCOVA results for stigma, self-stigma, barriers and beliefs by ethnicity (n = 203).

Measure		F	p	Partial η²
Societal Stigma (SSRPH)		0.169	0.682	0.001
Self-Stigma (SSSH)		0.001	0.977	0.000
Barriers to Seeking Help (BMHC)		12.66	<0.001	0.06
	Negative perceived value	0.002	0.967	0.000
	In-group stigma	13.97	<0.001	0.065
	Discomfort with emotion	0.336	0.563	0.002
	Cultural values	44.98	<0.001	0.184
Beliefs about Mental Illness (BMI)		2.87	0.092	0.014

Note: Partial η² effect sizes: small = 0.01, medium = 0.06, large = 0.14.

Table 8. Logistic regression predicting help-seeking among ethnic minority students (n = 126).

Measure	B	S.E.	Wald	p	OR	95% CI for OR
Student Status	0.19	0.47	0.17	0.680	1.21	0.48–3.04
Societal Stigma (SSRPH)	−0.02	0.09	0.04	0.843	0.98	0.83–1.17
Self-Stigma (SSSH)	−0.04	0.04	1.04	0.308	0.96	0.88–1.04
In-group Stigma	0.06	0.06	1.01	0.315	1.06	0.95–1.19
Negative Perceived Values	−0.08	0.06	1.79	0.181	0.93	0.83–1.04
Cultural Values	−0.12	0.06	4.40	0.036	0.87	0.79–0.99
Distress Index (CCAPS-34)	0.77	0.30	6.42	0.011	2.16	1.19–3.90
Constant	1.36	1.46	0.87	0.352	3.88	-

Note: OR = odds ratio; CI = confidence interval; SSRPH = Stigma Scale for Receiving Psychological Help; SSSH = Self-Stigma for Seeking Help. Dependent variable: Help-seeking from university wellbeing services (0 = no, 1 = yes).

Table 9. Summary of themes and subthemes from thematic analysis.

Themes	Subthemes	Description
Therapeutic relationship	Not feeling understood Cultural misunderstandings	The theme of therapeutic relationship encapsulates the value that participants had in the connection with their therapist and its importance in engaging with the counselling.
Privacy at the expense of connection	Don’t speak to anyone about it Distance Visibility	This theme relates to participants’ desire to keep their mental health difficulties and help-seeking separate from their university but also in many cases their families.
Undeservingness	Not important enough Others’ needs are greater Service is really busy It won’t help me	This theme encapsulates the participants’ feelings of not being important enough and not wanting to be a burden, leading to prioritising others’ needs over their own, including the service as well as the other students in need of support.
Time is of the essence	Waiting time Timing Restriction	This theme relates to the importance of counselling sessions being offered at the time needed and at a time that is accessible to students.

Table 10. Joint display of mixed-methods results integrating quantitative and qualitative findings.

Research Question (RQ)	Key Quantitative Result (Statistic + Effect Size)	Qualitative Theme/Subtheme	HBM Construct(s)	Practice Implication
RQ1: Utilisation, help-seeking, dropout by ethnicity	No significant ethnic group differences in help-seeking (χ² = 1.59, p = 0.21, φ = −0.10); no significant differences in dropout (45.8% Ethnic Minority vs. 30% White, Fisher’s exact p = 0.47, φ = 0.16).	Privacy at the Expense of Connection (visibility, confidentiality concerns) and Undeservingness (others’ needs greater).	Perceived susceptibility; cues to action; self-efficacy	Service uptake may appear equal across groups, but confidentiality fears and feelings of unworthiness shape engagement—counsellors must address hidden deterrents.
RQ2: Barriers to help-seeking	Ethnic minority students reported significantly higher in-group stigma (F(1, 200) = 13.97, p < 0.001, partial η² = 0.065) and cultural values (F(1, 200) = 44.98, p < 0.001, partial η² = 0.184).	Therapeutic Relationship (not feeling understood; cultural misunderstandings).	Perceived barriers; perceived benefits	Counselling must improve cultural competence and relational attunement to reduce cultural barriers.
RQ2: Practical barriers	Ethnic minority students reported higher lack of access (F(1, 200) = 4.41, p = 0.037, partial η² = 0.022).	Time is of the Essence (waiting times, inflexible scheduling, session length restrictions).	Perceived barriers; self-efficacy	Structural changes (shorter waitlists, flexible timings, varied delivery modes) can enhance accessibility and persistence.
Predictors of help-seeking among ethnic minority students	Logistic regression: higher distress increased help-seeking (OR = 2.16, 95% CI [1.19, 3.90], p = 0.011); cultural values reduced help-seeking (OR = 0.87, 95% CI [0.79, 0.99], p = 0.036).	Undeservingness (“It won’t help me”), Therapeutic Relationship (cultural misfit).	Perceived severity; perceived benefits; perceived barriers	Severe distress may override barriers, but cultural norms still suppress help-seeking. Outreach should target cultural values and self-perceptions to encourage earlier engagement.

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Share and Cite

MDPI and ACS Style

Jones Nielsen, J.D.; Thomas, R.; Fett, A.-K. A Mixed-Methods Exploration of the Utilisation of Embedded University Wellbeing Services by Ethnic Minority Students in London. Educ. Sci. 2025, 15, 1263. https://doi.org/10.3390/educsci15091263

AMA Style

Jones Nielsen JD, Thomas R, Fett A-K. A Mixed-Methods Exploration of the Utilisation of Embedded University Wellbeing Services by Ethnic Minority Students in London. Education Sciences. 2025; 15(9):1263. https://doi.org/10.3390/educsci15091263

Chicago/Turabian Style

Jones Nielsen, Jessica D., Rachel Thomas, and Anne-Kathrin Fett. 2025. "A Mixed-Methods Exploration of the Utilisation of Embedded University Wellbeing Services by Ethnic Minority Students in London" Education Sciences 15, no. 9: 1263. https://doi.org/10.3390/educsci15091263

APA Style

Jones Nielsen, J. D., Thomas, R., & Fett, A.-K. (2025). A Mixed-Methods Exploration of the Utilisation of Embedded University Wellbeing Services by Ethnic Minority Students in London. Education Sciences, 15(9), 1263. https://doi.org/10.3390/educsci15091263

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Menu

A Mixed-Methods Exploration of the Utilisation of Embedded University Wellbeing Services by Ethnic Minority Students in London

Abstract

1. Introduction

1.1. Aims

1.2. Research Questions

1.3. Hypotheses

2. Materials and Methods

2.1. Research Design

2.2. Recruitment

2.3. Participants

2.4. Measures

2.4.1. Phase 1 Survey Battery

2.4.2. Phase 2 Interviews

2.5. Statistical Analyses

2.5.1. Phase 1 Quantitative Data Analysis

2.5.2. Phase 2 Qualitative Data Analysis

2.5.3. Integration of Data Sets

3. Results

3.1. Quantitative Results

3.1.1. Descriptive Statistics

3.1.2. Psychological Distress

3.1.3. Help-Seeking

3.1.4. Service Utilisation

3.1.5. Dropout

3.1.6. Barriers to Help-Seeking

3.1.7. Post Hoc Analyses

3.2. Qualitative Results

3.2.1. Theme One: Therapeutic Relationship

3.2.2. Theme Two: Privacy at the Expense of Connection

3.2.3. Theme Three: Undeservingness

3.2.4. Theme Four: Time Is of the Essence

3.3. Mixed-Methods Results

4. Discussion

4.1. Limitations

4.2. Future Directions

5. Conclusions

Author Contributions

Funding

Institutional Review Board Statement

Informed Consent Statement

Data Availability Statement

Conflicts of Interest

Appendix A. Interview Schedule

References

Share and Cite

Article Metrics

Article Access Statistics

Further Information

Guidelines

MDPI Initiatives

Follow MDPI