Bibliographic record
Abstract
PURPOSE: To examine injury patterns among female field hockey players and to broaden the current base of knowledge by identifying the injury rates of different playing positions. It was hypothesized that goalkeepers would have the highest rate of injury, followed by forwards. METHODS: High school, university, and national level female field hockey players (N = 158) completed an anonymous questionnaire. They reported personal characteristics (age, height, weight); field hockey information (level, years of experience, surface); injury history (type, site, cause, severity); and back pain information. Injury rates were calculated per athlete-year. RESULTS: The most frequently injured site of the body was the lower limb (51%), followed by the head/face (34%), upper limb (14%), and torso (1%). The most prevalent types of injuries were ankle sprains, followed by hand fractures and head/face injuries. Goalkeepers had the highest rate of injury (0.58 injuries/athlete-year), whereas midfielders were the most injured field players (0.36 injuries/athlete-year). Back pain was reported by 59% of the sample, and the lower back was the most common site of this pain. CONCLUSION: There are differences in the rates of injury among playing positions in field hockey and in the types of acute injury sustained at each position. The high number of injuries to the head and face region is also cause for concern. Although most of these injuries are minor, the serious injuries that do occur can be very severe. Now that these patterns have been identified, further examination of the playing situations that lead to injury should be undertaken.
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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 | 0.000 |
| 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) | 0.004 | 0.001 |
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".