An Analysis of Head Impact Profiles and Safe Hockey Behaviors in Youth Hockey Players
Bibliographic record
Abstract
Abstract Although injury risk factors can be described in biomechanical terms, previous research has not described the relationship between style of play and the frequency and magnitude of head impacts. This paper is focused on the analysis of head impact profiles and safe hockey behaviors demonstrated during contact situations in a cohort of 13 Bantam-aged minor hockey players. Head impacts sustained at 27 home games during the 2006–2007 season were measured using the Head Impact Telemetry System (HITS). Hockey helmets (Canadian Standards Association approved) were equipped with six linear accelerometers and a wireless system that transmitted data to a receiver interfaced with a computer. Nine games were recorded using a field videography system to capture player collisions. A HITS profile was determined, which included the number of impacts (>10 g) (“g”=m/sec2) by player, the total number of impacts, the average number of impacts per game, and the mean linear acceleration value for all impacts per player. Based on these results, ten taped trials for the players sustaining greater than 180 total impacts over the season were qualitatively analyzed using a checklist to examine the safe hockey behaviors associated with body contacts. We captured 2753 head impacts over the season, with the average number of impacts per player per game ranging from 3 to 16 with a mean linear acceleration of 15.8 g (standard deviation=13.76). The checklist of safe hockey behaviors was used to analyze skating performance for a group of five players who experienced more than 180 impacts, with some impact magnitudes exceeding 98 g. In general, differences in indicators of skating skill, body position prior to check/contact, and actions during the contact were observed. While specific players may be predisposed to head impacts of a greater frequency and magnitude because of their style of play, these characteristics can be changed to reduce risk of potential injury.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".