The Stride program: Feasibility and pre-to-post program change of an exercise service for university students experiencing mental distress
Bibliographic record
Abstract
Rates of mental illness are disproportionately high for young adult and higher education (e.g., university student) populations. As such, universities and tertiary institutions often devote significant efforts to services and programs that support and treat mental illness and/or mental distress. However, within that portfolio of treatment approaches, structured exercise has been relatively underutilised and greater research attention is needed to develop this evidence base. The Stride program is a structured 12-week exercise service for students experiencing mental distress. We aimed to explore the feasibility of the program and assess pre- and post-program change, through assessments of student health, lifestyle, and wellbeing outcomes. Drawing from feasibility and effectiveness-implementation hybrid design literatures, we conducted a non-randomised feasibility trial of the Stride program. Participants were recruited from the Stride program (N = 114, Mage = 24.21 years). Feasibility results indicated the program was perceived as acceptable and that participants reported positive perceptions of program components, personnel, and sessions. Participants’ pre-to-post program change in depressive symptomatology, physical activity levels, mental health-related quality of life, and various behavioural outcomes were found to be desirable. Our results provide support for the feasibility of the Stride program, and more broadly for the delivery and potential effectiveness of structured exercise programs to support university students experiencing mental distress.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".