Pre-competition habits and injuries in Taekwondo athletes
Bibliographic record
Abstract
BACKGROUND: Over the past decade, there has been heightened interest in injury rates sustained by martial arts athletes, and more specifically, Taekwondo athletes. Despite this interest, there is a paucity of research on pre-competition habits and training of these athletes. The purpose of this pilot study was to assess training characteristics, competition preparation habits, and injury profiles of Taekwondo athletes. METHODS: A retrospective survey of Canadian male and female Taekwondo athletes competing in a national tournament was conducted. Competitors at a Canadian national level tournament were given a comprehensive survey prior to competition. Items on training characteristics, diet, and injuries sustained during training and competition were included. Questionnaires were distributed to 60 athletes. RESULTS: A response rate of 46.7% was achieved. Of those that responded, 54% dieted prior to competition, and 36% dieted and exercised pre-competition. Sixty-four percent of the athletes practised between 4-6 times per week, with 54% practicing 2 hours per session. Lower limb injuries were the most common (46.5%), followed by upper extremity (18%), back (10%), and head (3.6%). The majority of injuries consisted of sprains/strains (45%), followed by contusions, fractures, and concussions. More injuries occurred during training, including 59% of first injuries. CONCLUSION: More research needs to be conducted to further illustrate the need for appropriate regulations on weight cycling and injury prevention.
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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 | 0.001 | 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) | 0.002 | 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".