Measuring the Effects of Initiating Body Checking at the Atom Age Level
Bibliographic record
Abstract
Abstract The purpose of this study was to determine the effects of initiating body checking at the Atom age level. The study used an experimental design in which teams from the Ottawa District Minor Hockey Association (ODMHA) represented the control group, while teams from the Ontario Hockey Federation (OHF) represented the treatment group. The study was conducted over a 3-year period from 1998 to 2001. The comparison sampling between the two leagues was as follows: ODMHA Yr1 = 69 teams versus OHF Yr1 = 74 teams, ODMHA Yr2 = 59 teams versus OHF Yr2 = 101 teams, ODMHA Yr3 = 46 teams versus OHF Yr3 = 90 teams. Measures of interest reported here included player selection bias, injury incidence, and variables that describe the characteristics of the flow of the game such as goals for, goals against, and number of penalties. Athlete exposures for each league by year were computed using 15 players per team for a 20 game plus 20 practice schedule. Statistical analysis of the difference in proportions of injuries reported by the two leagues based on the CHA injury insurance dataset showed that no significant difference occurred per year of the study. In addition, the rates of injuries were lower in each year than those reported by previously published studies. Body checking, which includes body contact is considered by many to be a skill within the game of ice hockey that can be taught at the younger age levels in a manner that does not lead to a higher incidence of injuries, or unfavorable changes in the game. The results of this study support that contention. In the present study, it was assumed that education was an essential component of the introduction process and that coaches were provided the necessary background to enable them to teach the introduction of body checking as a skill.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".