An Australian survey on health and injuries in adult competitive surfing
Bibliographic record
Abstract
BACKGROUND: There is limited research that explores health and injuries of surfers. The aim of this study is to describe the health and injury profile of adult Australian competitive surfers. METHODS: In this cross-sectional study, all registered participants at the 2014 Australian Surfing Titles were invited to complete an online survey comprising: 1) demographic and surfing information; 2) health-related quality of life using the SF-12 questionnaire; and 3) surfing injury history. Descriptive statistics were used to describe the survey responses. The sample consisted of 227 (77% male) surfers with mean age of 35.0±13.2 years. They spent on average, 10.0±6.5 hours per week surfing. RESULTS: The mean SF-12 physical and mental health component scores were significantly higher than the population norm at 53.3±5.4 and 55.6±6.2, respectively. A total of 175 (81%) respondents reported incurring at least one surfing-related injury in their lifetimes, while 90 (58%) respondents reported incurring at least one surfing-related injury in the current season. The most commonly injured body regions were the lower back, foot, knee, and ankle, while the most frequent types of injury were abrasion and laceration. CONCLUSIONS: Although adult Australian competitive surfers report greater physical and mental health-related quality of life compared to the general population, surfing-related injuries are relatively common. The present study reveals a higher burden of lower back injuries compared to previous reports as well as differences in injury profiles amongst the surfing disciplines.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".