Efficacy of a student led physical activity coaching program for university students and employees
Bibliographic record
Abstract
Background: Physical inactivity is a leading risk factor for many of Canada’s major chronic diseases. Research shows that physical activity (PA) improves overall health and cognitive functioning, which is important in academia (ParticipACTION, 2021). The purpose of this study was to determine the effectiveness of Move More North Shore, a physical activity program designed to increase PA and decrease sedentary behaviour. Program Delivery: Twelve participants were referred from university counselling, medical services, or self-referral (employees only). Student Active Health Coaches (SAHC) provided motivation and support to participants through weekly, one-on-one meetings over an eight-week period, to introduce the habit of PA into their daily lives. Due to COVID-19, program delivery was entirely virtual. Evaluation: Participants completed a pre-intervention interview and post-intervention survey which addressed participant knowledge, attitudes, and behaviours, and feedback on program structure. Significantly more days of PA/week were recorded post-intervention versus pre-intervention t(11) = 3.432, p<0.003 (d = -0.991), M1=1.50 days/week, M2=3.33 days. Participants reported significantly more confidence in maintaining PA post-intervention versus pre-intervention t(10) = 3.786, p<0.002 (d = -1.142), M1=3.36/5 confidence in becoming active, M2=4.18/5 confidence in maintaining PA. Conclusions: The pilot resulted in increased PA levels and confidence, feelings of connectedness, improved well-being, and meaningful learning experiences for SAHCs. Sedentary-time was not significantly different post-intervention, which may be due to reduced emphasis on this outcome and COVID-19 restrictions. Funding: Vancouver Coastal Health Active Living Grant.
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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 | 0.002 | 0.004 |
| 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 | 0.000 |
| Open science | 0.001 | 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".