Student-led Exercise Testing And Prescription Has Benefits For Both Students And Their Community Volunteers
Bibliographic record
Abstract
PURPOSE: Service-learning opportunities allow students to apply their knowledge and skills through engagement with their community. Previous studies have suggested that student-led exercise testing and health screening can benefit both students and their community participants. In a third year Kinesiology course “Physiological Assessment and Training”, students at the University of Prince Edward Island are provided with an introduction to health-focused personal training and develop and manage personalized training programs for community volunteers. The purpose of this study was to investigate the impact of student-led training programs on student learning and health-related fitness outcomes for program participants. METHODS: Participants included 43 women and 13 men aged 30-65 years (mean age: 52.3 ± 10.0 years) with stable health. Students led participants through aerobic and musculoskeletal fitness tests before and after completing a 4-week training program based on participants’ fitness and interests. RESULTS: Following the program, participants experienced significant increases in grip strength (67.8 kg vs 71.9 kg), push-ups (12.6 vs 16.9), one-leg stance with eyes closed (9.4 seconds vs 12.2 seconds) and sit-and reach (31.1 cm vs 33.0 cm) (all p < 0.05). There were no changes observed in estimated VO2max (32.8 ml/kg/min vs 33.9 ml/kg/min) or one-leg stance with eyes open (39.8 seconds vs 40.2 seconds) (all p > 0.05). CONCLUSION: These results suggest that even relatively brief student-led personal training programs may provide meaningful benefits to students and their community volunteers.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".