Injuries in Women's Recreational Ice Hockey: Outcome and Follow-up
Bibliographic record
Abstract
Participation in ice hockey by women is increasing in many parts of North America; however, research into injuries and the patterns of injury among females associated with this activity is limited. The purpose of this research was to examine the nature and sequelae of injuries suffered by female recreational ice hockey players. This prospective study followed 314 female players from 33 teams in Edmonton, Canada during an entire hockey season. Injury and game attendance data were collected using monthly telephone interviews throughout the season. Six-months-post-injury players were contacted to determine if injuries had modified their desire to continue playing ice hockey. One hundred and two players reported a total of 125 injuries. The anatomic region most often injured was the lower extremity (31.2%), and the most common diagnosis was sprain/strain (52.0%). The predominant injury mechanism was player contact, either as a result of collision with another player or a body check (40.0%). While less than 1% of injuries resulted in hospitalization, 17.6% of injuries resulted in an absence from hockey of eight or more days. Of the 102 players who were injured, 86 (84.3%) responded to the follow-up questionnaire. Seventy-six (88.4%) players indicated that they intended to continue to play hockey. While a number of players acknowledged the possibility and consequences of injury, these were not sufficient to keep them from playing the sport.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".