Canadian adolescent tackle football coaches’ helmet fitting experience, procedures, and beliefs for helmets and mouthguards effectiveness against injury
Bibliographic record
Abstract
Wearing a helmet and mouthguard are mandatory for adolescent (ages 14–18) tackle football (American football) players. Coaches are usually responsible for fitting player helmets despite many organizations not mandating/providing training. This structure may create a significant concern for player safety, as 60% of adolescent players wear a poor fitting helmet and wearing a poor fitting helmet may be associated with a higher risk of concussion. We conducted a cross-sectional survey to examine Canadian adolescent tackle football coaches’ current helmet fitting procedures and their beliefs towards the effectiveness of helmets and mouthguards for injury prevention. Amongst the 101 coaches from three leagues, most coaches (51%–88%) believed that wearing a mouthguard could protect against concussions, dental injuries, and/or other orofacial injuries. Helmet fitting procedures were highly variable within and between teams, with coaches using between 1 and 7 criteria. Despite 78%–92% of coaches believing that wearing a proper fitting helmet could protect against concussion and/or reduce its severity, 31%–96% of coaches who fit helmets have never received any training. Lack of formal helmet fit training was reported as the largest barrier to fitting helmets, which supports that tackle football organizations should take immediate action to provide coaches with formal helmet fit training.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".