Does Increasing the Severity of Penalties Assessed in Association With the “Zero Tolerance for Head Contact” Policy Translate to a Reduction in Head Impact Rates in Youth Ice Hockey?
Bibliographic record
Abstract
OBJECTIVE: The risk of concussion is high in Canadian youth ice hockey. Aiming to reduce this burden, in 2011, Hockey Canada implemented a national "zero tolerance for head contact (HC)" policy mandating the penalization of any player HC. In 2018 to 2020, Hockey Canada further amended this HC policy including stricter enforcement of severe HCs. This study aimed to compare HC rates, head impact location, and HC enforcement prepolicy, postpolicy, and after policy amendments in elite U15 Canadian youth ice hockey. DESIGN: This is a prospective cohort study. SETTING: A collection of events with the video camera located at the highest point near center ice in public ice hockey arenas in Calgary, Alberta. PARTICIPANTS: A convenience sample of 10 AA U15 games prepolicy (2008-2009), 8 games postpolicy (2013-2014), and 10 games after policy amendments (2020-2021). INDEPENDENT VARIABLES: An analysis of 3 cohort years regarding the HC-policy implementation and amendments. MAIN OUTCOME MEASURES: Using Dartfish video-analysis software, all player contacts and HCs [direct (HC1), indirect (eg, boards, ice) (HC2)] were tagged using validated criteria. Univariate Poisson regression clustering by team-game offset by game length (minutes) was used to estimate incidence rates (IR) and incidence rate ratios (IRR) between cohorts. RESULTS: With additional rule modifications, a 30% reduction in HC1s emerged (IRR 2013-2020 = 0.70, 95% CI, 0.51-0.95). Since the HC-policy implementation, HC1s decreased by 24% (IRR 2008-2020 = 0.76, 95% CI, 0.58-0.99). The proportion of HC1s penalized was similar across cohorts (P 2008-2009 = 14.4%; P 2013-2014 = 15.5%; P 2020-2021 = 16.2%). CONCLUSIONS: The HC-policy amendments have led to decreased HC1 rates. However, referee enforcement can further boost the HC-policy effectiveness. These findings can help future referee training and potential rule modifications to increase player safety nationally.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".