Evaluating the Effect of Policy Prohibiting Body Checking on Physical Contacts in U15 and U18 Youth Ice Hockey Leagues
Bibliographic record
Abstract
OBJECTIVE: To compare physical contacts (PCs) and head contacts (HCs) in nonelite U15 (ages 13-14) and U18 (ages 15-17) ice hockey players in body checking (BC) and non-BC leagues. DESIGN: Cohort video analysis study. SETTING: Ice hockey arenas in Calgary, Canada. PARTICIPANTS: Players from 13 BC and 13 non-BC games at the nonelite U15 and U18 levels (n = 52 total games). ASSESSMENT OF RISK FACTORS: Games were videotaped and analyzed to compare PC variables between leagues allowing and prohibiting BC. MAIN OUTCOME MEASURES: Validated methodology for PC type (trunk PC and other types of PC with limb/stick/head), intensity (low and high intensity), and HC. Incidence rate ratios (IRRs) were estimated using Poisson regression (controlling for cluster by team game and offset by player minutes) to compare the incidence of PCs in BC and non-BC games. RESULTS: The rate of trunk PCs was lower in the non-BC leagues for both U15 (IRR = 0.50, 99% confidence interval [CI]: 0.43-0.58) and U18 (IRR = 0.56, 99% CI: 0.46-0.67) players. This was most significant for BC contacts (U15: IRR = 0.18, 99% CI: 0.11-0.29; U18: IRR = 0.16, 99% CI: 0.08-0.34), although also significant for body contacts (U15: IRR = 0.54, 99% CI: 0.46-0.64; U18: IRR = 0.58, 99% CI: 0.48-0.70), other PCs, (U15: IRR = 0.60, 99% CI: 0.45-0.78; U18: IRR = 0.71, 99% CI: 0.58-0.88), and HCs (U15: IRR = 0.40, 99% CI: 0.22-0.71; U18: IRR = 0.37, 99% CI: 0.25-0.57). CONCLUSIONS: The incidence of BC was 82% lower in the U15 and 84% lower in U18 levels not allowing BC, with lower HC and other PC rates also in non-BC leagues. These findings demonstrate high adherence with BC policy change in youth ice hockey.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".