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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".