Acceptability and utility of digital well‐being and mental health support for university students: A pilot study
Bibliographic record
Abstract
AIM: To assess the acceptability and explore the utility of a novel digital platform designed as a student-facing well-being and mental health support. METHODS: An adapted version of i-spero® was piloted as a student-facing well-being support and as part of routine university-based mental health care. In both pathways, student participants completed baseline demographics and brief validated measures of well-being and mental health. Weekly measures of anxiety (GAD-7) and depression (PHQ-9) and a Week 8 Experience Survey were also scheduled. Integrated mixed methods analysis was used to assess acceptability and explore the utility of these platforms. RESULTS: Students in the well-being (n = 120) and care pathways (n = 121) were mostly female and between 19 and 22 years of age. Baseline screen positive rates for anxiety and depression were high in both the well-being (68%) and care pathways (80%). There was a substantial drop in adherence over Week 1 (50% well-being; 40% care) followed by minor attrition up to Week 8. Anxiety and depressive symptom levels improved from baseline in students who dropped out after Week 1 (p ≤ .06). The student experience was that i-spero® improved their emotional self-awareness, understanding of progress in care, and knowledge about when to seek help. Most students agreed (>75%) that i-spero® should form part of regular university student wellness support. CONCLUSIONS: Digital well-being and mental health support seems acceptable to university students; however, engagement and persistence are areas for further development. Such digital tools could make a positive contribution to an evidence-based stepped approach to student well-being and 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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".