Incidence of Head Contacts, Penalties, and Player Contact Behaviors in Youth Ice Hockey: Evaluating the “Zero Tolerance for Head Contact” Policy Change
Bibliographic record
Abstract
BACKGROUND: To reduce the risk of concussion in youth ice hockey, Hockey Canada implemented a national "zero tolerance for head contact" (HC) policy in 2011. A previous cohort study revealed higher concussion rates after this implementation in players aged 11 to 14 years. However, it is unknown whether the elevated risk was due to higher HC rates or factors such as increased concussion awareness and reporting. PURPOSE: To compare the rates of primary and secondary HCs and HC policy enforcement in elite U15 ice hockey leagues (players <15 years) before (2008-2009) and after (2013-2014) the zero-tolerance policy change. STUDY DESIGN: Cohort study; Level of evidence, 3. METHODS: = 16; 486 players) HC policy implementation were video recorded. Videos were analyzed with validated criteria for identifying HC types (primary/direct contact by players [HC1], secondary/indirect contact via boards, glass, or ice surface [HC2]) and other player-to-player contact behavior. Referee-assessed penalties were cross-referenced with the official Hockey Canada casebook, and penalty types were displayed using proportions. Univariate Poisson regression (adjusted for cluster by team game, offset by game length [minutes]) was used to estimate HC incidence rates (IRs) and incidence rate ratios (IRRs) between cohorts. RESULTS: A total of 506 HCs were analyzed, 261 before HC policy implementation (IR, 16.6/100 team minutes) and 245 after implementation (IR, 15.5/100 team minutes). The HC1 rate (IRR, 1.05; 95% CI, 0.86-1.28) and HC2 rate (IRR, 0.74; 95% CI, 0.50-1.11) did not significantly differ before versus after implementation. Only 12.0% and 13.6% of HC1s were penalized pre- and postimplementation, respectively. Before implementation, HC1s were commonly penalized as roughing or elbowing penalties (59%), while after implementation, HC1s were penalized with the HC penalty (76%), and only 8% as roughing or elbowing. CONCLUSION: Despite implementation of the "zero tolerance for HC" policy, there was no difference in the rate of HC1s and HC2s or the proportion of HC1 penalized from before to after implementation. This research is instrumental in informing Hockey Canada's future referee training and rule enforcement modifications.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".