University students’ use of mental health services: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: International estimates suggest around a third of students arrives at university with symptoms indicative of a common mental disorder, many in late adolescence at a developmentally high-risk period for the emergence of mental disorder. Universities, as settings, represent an opportunity to contribute to the improvement of population mental health. We sought to understand what is known about the management of student mental health, and asked: (1) What proportion of students use mental health services when experiencing psychological distress? (2) Does use by students differ across health service types? METHODS: A systematic review was conducted following PRISMA guidelines using a Context, Condition, Population framework (CoCoPop) with a protocol preregistered on Prospero (CRD42021238273). Electronic database searches in Medline, Embase, PsycINFO, ERIC and CINAHL Plus, key authors were contacted, citation searches were conducted, and the reference list of the WHO World Mental Health International College Student Initiative (WMH-ICS) was searched. Data extraction was performed using a pre-defined framework, and quality appraisal using the Joanna Briggs Institute tool. Data were synthesised narratively and meta-analyses at both the study and estimate level. RESULTS: 7789 records were identified through the search strategies, with a total of 44 studies meeting inclusion criteria. The majority of included studies from the USA (n = 36), with remaining studies from Bangladesh, Brazil, Canada, China, Ethiopia and Italy. Overall, studies contained 123 estimates of mental health service use associated with a heterogeneous range of services, taking highly variable numbers of students across a variety of settings. DISCUSSION: This is the first systematic quantitative survey of student mental health service use. The empirical literature to date is very limited in terms of a small number of international studies outside of the USA; studies of how services link together, and of student access. The significant variation we found in the proportions of students using services within and between studies across different settings and populations suggests the current services described in the literature are not meeting the needs of all students.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.031 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".