Access to University Mental Health Services: Understanding the Student Experience: L’accès aux services universitaires de santé mentale : comprendre l’expérience des étudiants
Bibliographic record
Abstract
Objective: To describe student access to university mental health services and barriers and gaps in support.Methods: This multiple cohort study used self-report data from 4,138 undergraduate students who completed the U-Flourish Well-Being Survey at the start and completion of first year from 2018 to 2023.The survey incorporated validated measures of mental health symptoms, barriers to care, and open-text questions about the mental health care experience and perceived gaps.Quantitative analyses summarized utilization patterns and barriers.An interpretive qualitative analysis identified common themes about support services and opportunities for improvement from the student perspective.Results: At university entry, 43% of students screened positive for anxiety and/or depression, 30% reported a lifetime mental disorder and 23% a lifetime history of self-harm.Over first year, 15% of students surveyed accessed university mental health services.Access was more likely in students identifying as older, gender diverse, female, having a prior mental disorder and those who screened positive for anxiety or depression.Common attitudinal and practical barriers reported included thinking problems would resolve (74%), being uncomfortable sharing (73%), and not knowing how to get help (50%).Common stigma barriers included concerns about what family or friends might think.Students expressed that both campus-based well-being and mental health care offered during flexible hours and accessible through online booking were important.Conclusions: Student-tailored mental health literacy may be a sustainable approach to address the attitudinal and practical barriers identified.If such barriers are reduced, an increased service demand would be expected and improved efficiencies needed.A clear Statement of Services, an online singular point of access with embedded triage to signpost students to indicated levels of care, and clearly worked-out care pathways including to community-based services would better align with a stepped care model, improve efficiency and access, and foster realistic expectations around university mental health support.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| 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".