Down, set, record: Assessing warm-up structure and use of neuromuscular training exercises in Canadian adolescent tackle football
Bibliographic record
Abstract
Implementing injury prevention strategies is critical for coaches and sport organizations to promote player safety. Neuromuscular training (NMT) warm-up programs that involve aerobic, strength, agility, balance/coordination, and head-and-neck control exercises have demonstrated injury rate reductions of >35% across many adolescent sports. Tackle football (i.e., American football) has amongst the highest injury rates within youth sport and lacks examination of current warm-up structure and exercises. Our video-analysis study examined footage from 23 practice and game warm-ups (46 total) to document the duration and exercises completed for nine adolescent (males, ages 14–17) tackle football teams over a 9-week season within Calgary (Canada). We found that teams typically spent less than five minutes actively warming-up with most exercises being aerobic (51%) and dynamic stretching (29%), while <10% of exercises involved static stretching (6%) or NMT components of strength (9%), agility (3%), balance/coordination (1%), and head-and-neck control (1%). Warm-up length and use of NMT components were similar between games and practices, and over the season. Our findings demonstrate that NMT exercises are not being used in tackle football, which supports future implementation and evaluation of a tackle football-specific NMT program. We also provide coaches with a method for examining their team's warm-up structure.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".