A mixed-methods approach to understanding the need for embedded physical activity interventions for mental health within university counselling centres
Bibliographic record
Abstract
Despite the wide-reaching benefits of physical activity for mental health, there are a lack of effective physical activity interventions that can be embedded within university counselling centres. In collaboration with Health and Wellness Services at the University of Toronto, the current study aimed to assess the feasibility, acceptability and effectiveness of a six-week intervention for improving acute mental health concerns and physical activity. Participants completed pre- and post-questionnaires to evaluate the effectiveness and feasibility of the program. Interviews with participants and key stakeholders (clinicians and Director of Services) were conducted to gain insight on the feasibility and acceptability of a physical activity referral scheme for mental health. Participants were help-seeking students (Mage = 23 years), who were referred from Health and Wellness Services. Overall, 75% of the recruited sample remained engaged in the program (N = 54). There was a significant increase in total MET-minutes per week of physical activity (Mpre = 893.58 ± 678.35 vs Mpost = 1641.15 ± 984.54; t (53) = 5.33, p < .001). There were also significant improvements in mental health status (Mpre = 110.89 ± 25 vs Mpost = 140.67 ± 31.37; t (53) = 6.43, p < .001). Interviews suggest the program is evaluated favourably and provide comprehensive suggestions for an embedded physical activity referral scheme. The results demonstrate the importance of embedded physical activity interventions for mental health to help reduce the burden on university counselling centres and improve the mental health status of university students.
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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 | 0.116 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".