Reality check: the cost–effectiveness of removing body checking from youth ice hockey
Bibliographic record
Abstract
BACKGROUND/AIM: The risk of injury among Pee Wee (ages 11-12 years) ice hockey players in leagues that allow body checking is threefold greater than in leagues that do not allow body checking. We estimated the cost-effectiveness of a no body checking policy versus a policy that allows body checking in Pee Wee ice hockey. METHODS: Cost-effectiveness analysis alongside a prospective cohort study during the 2007-2008 season, including players in Quebec (n=1046), where policy did not allow body checking, and in Alberta (n=1108), where body checking was allowed. Injury incidence rates (injuries/1000 player-hours) and incidence proportions (injuries/100 players), adjusted for cluster using Poisson regression, allowed for standardised comparisons and meaningful translation to community stakeholders. Based on Alberta fee schedules, direct healthcare costs (physician visits, imaging, procedures) were adjusted for cluster using bootstrapping. We examined uncertainty in our estimates using cost-effectiveness planes. RESULTS: Associated with significantly higher injury rates, healthcare costs where policy allowed body checking were over 2.5 times higher than where policy disallowed body checking ($C473/1000 player-hours (95% CI $C358 to $C603) vs $C184/1000 player-hours (95% CI $C120 to $C257)). The difference in costs between provinces was $C289/1000 player-hours (95% CI $C153 to $C432). Projecting results onto Alberta Pee Wee players registered in the 2011-2012 season, an estimated 1273 injuries and $C213 280 in healthcare costs would be avoided during just one season with the policy change. CONCLUSION: Our study suggests that a policy disallowing body checking in Pee Wee ice hockey is cost-saving (associated with fewer injuries and lower costs) compared to a policy allowing body checking. As we did not account for long-term outcomes, our results underestimate the economic impact of these injuries.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".