Injury Prevention in Youth Tackle Football
Bibliographic record
Abstract
This thesis contains two projects that aim to investigate injury and injury prevention strategies in Canadian adolescent tackle football. The first project aimed to examine the current utilization of Neuromuscular Training components (NMT) in tackle football warm-ups and the second project examined adolescent (ages 14-17) tackle football epidemiology. Objectives: 1. To describe the current time spent by adolescent tackle football teams in five key neuromuscular training (NMT) components (aerobic, agility, balance and coordination, strength, and head on neck control) and determine if time in warm-up components differed throughout the season. 2. To describe injury rates, burden, types, mechanisms, and risk factors in adolescent (ages 14-17) community tackle football players in one season. Methods: Teams consented to video-recording of practice and game warm-ups. Video was analyzed using Dartfish tagging software (Dartfish, USA). Validated injury surveillance methods were used during a prospective cohort in a single nine-week competition season for participants aged 14-17. Injury rates (IR), concussion rates (CR), and incidence rate ratios (IRR) were reported based on univariable Poisson regression analyses (offset by player-hours and controlling for cluster by team). Results: Teams spent a median of 456.2 seconds in warm-up prior to sessions and a median time of 275 seconds in active warm-up components. Teams spent more time in some NMT components (aerobic and strength) compared to others (balance, agility and coordination, and head on neck control), however other than aerobic (58%) the use of other NMT components was low (time in NMT components 1-9%). Teams were relatively consistent with component utilization throughout the season. The overall IR was 4.61 injuries/1000 player-hours (95%CI; 3.84 – 5.53) and the CR was 1.20 concussions/1000 player-hours (95%CI; 0.90-1.61). Concussion rates were higher in games (IR=3.86 concussions/1000 player game-hours 95%CI; 2.74 – 5.43) than practices (IR=0.44 concussions/1000 practice player hours, 95%CI;0.25 – 0.75) (IRR=8.82,95%CI; 4.52- 18.27). Previous history of injury in the past 12 months (IRR=1.66,95%CI; 1.07-2.57) and being obese (BMI > 30.00) (IRR=2.55, 95%CI; 1.35-4.84) were associated with higher rates of practice-related injury. Lifetime history of concussion (IRR=1.58, 95%CI; 1.00 – 2.50) and being in the 75th percentile for height (IRR=1.58, 95%CI; 1.19 – 2.18) were associated with higher game-related injury rates, with the former being insignificant and the latter significant. Conclusions: Injury and concussion rates are high in adolescent tackle football. There are opportunities for research examining injury and concussion prevention strategies in tackle football in Canada. Football teams do not engage in NMT warm-up components and there is significant opportunity for implementation of such a prevention strategy in this sport.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".