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 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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".