Heads Above the Rest: Examining Head impacts in Canadian High School Football
Bibliographic record
Abstract
This thesis contains three projects focused on concussion and head impacts in tackle football. First, is a systematic review and meta-analysis. Objective: To examine youth football concussion and head impact rates, modifiable risk factors, and football-specific prevention strategies. Methods: Nine databases were searched. Two authors (with a third to resolve disagreements) completed study screening and assessment of bias. Results: Concussion rates for high school (ages 13-19) and minor football (ages 5-15) were 0.78/1000 athlete exposures and 1.15/1000 athlete exposures. Of prevention strategies, contact training and contact restrictions had the strongest evidence supporting their effectiveness. Conclusions: The high rates of concussion and head impacts affirm the need for prevention strategies in youth football. The second manuscript investigated head impact rates in Canadian high school football. Objective: To describe head impact rates in Canadian high school football. Methods: Games (n=14) involving two teams were recorded during the 2019 season and analyzed to identify head impacts. Results: The offense experienced head impacts at a higher rate than the kicking and receiving units, but not the defense. Conclusion: To help reduce the head impact rates in this cohort, contact training emphasizing the removal of the head from contact may be beneficial. The third manuscript evaluated a score-based running time rule. Objective: To describe the effect of the score-based running time rule on the rates of head impacts in Canadian high school football. Methods: Video analysis was used to identify head impacts in games (n=14) involving two teams that were followed during the 2019 football season. Results: The rates of head impacts in games where running time came into effect were lower for the offense and defense, but not special team units (kicking team and receiving team). Conclusions: The score-based running time rule was associated with lower head impact rates for two of four team units.
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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 | 0.026 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.011 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".