The Influence of Peer-to-Peer Learning via Technology on Youth Soccer Coaches’ Neuromuscular Training Warm-up Exercise Error Detection
Bibliographic record
Abstract
Objective: To evaluate whether a peer-to-peer (P2P) learning technology integrated instruction (intervention) workshop, compared to a standard in person instruction (control) workshop improves coaches’ self-efficacy and ability to identify neuromuscular training warm-up exercise errors. Methods: Calgary Minor Soccer Association clubs (n= 6) agreed to participate in a randomized controlled trial. In each club, one of each type of workshop were randomly allocated to the scheduled dates. Coaches (n=85) randomly attended a control or intervention workshop. At the end of the workshop, the soccer NMT warm-up exercise test, a video-based test where coaches identify common NMT exercise errors, was completed. At the beginning and end of the workshop, the soccer NMT warm-up self-efficacy scale was completed to assess coaches’ self-efficacy in their ability to identify NMT exercises errors on a 7-point Likert scale. Results: Eighty-five youth soccer coaches attended the control (n=41) or the intervention workshop (n= 44). Mean NMT warm-up exercise test scores were 72% (95% CI: 68.38 - 76.44) for the control and 71% (95% CI: 67.50 - 79.38) for the intervention workshop. Mean change in NMT warm-up self-efficacy scores were 0.98 (95% CI: 0.56 – 1.40) for the control and 1.77 (95% CI: 1.41 – 2.14) for the intervention workshop. Multivariable linear regression analyses indicated that workshop delivery method was not associated with the exercise test score (beta= -3.45, 95%CI: -10.80 - 3.91, R2=0.13) but was associated with a greater difference in change of self-efficacy scores for the intervention workshop (beta= 0.97, 95%CI: 0.26 – 1.89, R2=0.13). Conclusions: A P2P learning technology integrated instructional workshop did not alter coaches tested ability to identify exercise mistakes but did increase coaches’ self-efficacy in identifying exercise mistakes compared to a standard in person workshop.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".