Static and Dynamic Balance and Injury Prevalence in Snowboard Instructors
Bibliographic record
Abstract
Background. Snowboarding is a quite popular winter sport, though associated with the risk of injury. Aim: to determine the relationship between sport injuries and static and dynamic balance in snowboard instructors. Methods. The study included snowboard instructors from Ontario, Canada. Questionnaires were given before and after the winter season to obtain injury history. Static balance was evaluated with a Wii Balance Board. Dynamic balance was evaluated using the Y balance test. The results were compared between different genders, age and days on-snow per season. Results. Male snowboard instructors had, on average, higher static balance scores than the females. Both male and female scores for testing with eyes closed were significantly lower than with eyes open. The female snowboard instructors had, on average, higher dynamic balance scores than the males. However, for eyes closed testing, female snowboarders’ scores were noticeably better than the males’ scores. Nine of out twenty snowboard instructors had sustained one or more injuries in the past snowboarding season. One female and one male sustained two injuries each, and in total there were ten injuries amongst twenty snowboarders. Conclusions. There was no significant difference between prevalence of injury and balance amongst different genders. Keyword: snowboarding, sports injuries, static balance, dynamic balance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.006 | 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".