Abstract
The percentage of UK-based students who reported a mental health condition to their university rose from less than 1% in the 2010/11 academic year to 5.7% in 2021/22. It is estimated that 14% of 10–19-year-olds globally experience mental health conditions, yet this remains largely undiagnosed and untreated. These challenges can impact significantly on a student’s academic performance, relationships and overall well being. Lena (by Inspire) is a social enterprise that provides expert, confidential wellbeing services for students, aimed at helping them deal with challenges in their academic and private lives. Through this service for students, Lena has accumulated a dataset of student journeys including Clinical Outcomes in Routine Evaluation (CORE-10) scores.
The objective of this study is to analyze the counselling outcome data from a student counselling service. Descriptive statistics were calculated to understand client characteristics and CORE outcome scores.
The dataset consists of 4713 students, aged 18 or over, attending counselling from January 2012 to September 2024. The mean age of clients attending the student counselling service was 24, and over 65% of clients were female. 81.6% of clients were in full time education. In this study, a paired t-test was conducted to evaluate the difference in student CORE10 scores, pre and post counselling. The results indicated a statistically significant difference in scores (p < 0.001) with a mean difference of 7.676. These findings suggest that there was a significant improvement in scores pre and post counselling, providing evidence of the effectiveness of the intervention.
Using these insights, we can gain a deeper understanding of the client's journey through student mental health services, leading to actionable recommendations for improving and personalising services. Future work will involve identifying further trends and patterns within the dataset and using machine learning to predict client counselling outcomes.
The objective of this study is to analyze the counselling outcome data from a student counselling service. Descriptive statistics were calculated to understand client characteristics and CORE outcome scores.
The dataset consists of 4713 students, aged 18 or over, attending counselling from January 2012 to September 2024. The mean age of clients attending the student counselling service was 24, and over 65% of clients were female. 81.6% of clients were in full time education. In this study, a paired t-test was conducted to evaluate the difference in student CORE10 scores, pre and post counselling. The results indicated a statistically significant difference in scores (p < 0.001) with a mean difference of 7.676. These findings suggest that there was a significant improvement in scores pre and post counselling, providing evidence of the effectiveness of the intervention.
Using these insights, we can gain a deeper understanding of the client's journey through student mental health services, leading to actionable recommendations for improving and personalising services. Future work will involve identifying further trends and patterns within the dataset and using machine learning to predict client counselling outcomes.
| Original language | English |
|---|---|
| Pages | 1-1 |
| Number of pages | 1 |
| Publication status | Published (in print/issue) - 10 Sept 2025 |
| Event | 13th European conference on Mental Health - Antwerp, Belgium Duration: 10 Sept 2025 → 12 Sept 2025 Conference number: 13th https://ecmh.eu/ |
Conference
| Conference | 13th European conference on Mental Health |
|---|---|
| Abbreviated title | ECMH-2025 |
| Country/Territory | Belgium |
| City | Antwerp |
| Period | 10/09/25 → 12/09/25 |
| Internet address |
