Winning at all costs: a review of risk-taking behaviour and sporting injury from an occupational safety and health perspective
Bibliographic record
Abstract
Professional athletes involved in high-performance sport are at a high injury risk, which may lead to long-term health consequences. Professional athletes often expose themselves to risky behaviours, resulting in a higher acceptance level of occupational risk compared to other occupations. To date, many studies have focused on elite athletes' specific injury prevention techniques. The objective of this narrative review is to (1) summarise elite athletes' attitudes towards important occupational safety and health (OSH) practices, including injury reporting, medicine usage and personal protective equipment (PPE) usage, and (2) explore factors that may influence elite athletes' injury awareness. If injury awareness were given a similar weighting in elite sports as in any other highly physical occupation, the potential benefits to elite athletes and their long-term health could be highly significant. This review identifies that most elite athletes are not aware that sporting injuries are occupational injuries requiring behaviours determined by OSH rules. All the 39 studies identified met the moderate methodological quality criteria according to the Mixed Methods Appraisal Tool (MMAT). The factors impeding athletes' injury awareness from achieving occupational health standards are discussed from three safety management perspectives: organisational, societal and individual. This review contributes to a better understanding of how to build a positive safety culture, one that could reduce elite athletes' injury rate and improve their long-term wellbeing. Further research is required to develop a quantitative measurement instrument to evaluate occupational health awareness in the sport context. Based on the papers reviewed, the study population was categorised as elite, professional, high-performance amateur and student-athletes.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".