The knowledge and attitudes of field hockey athletes to injury, injury reporting and injury prevention: A qualitative study
Bibliographic record
Abstract
OBJECTIVES: Researchers have often struggled to successfully implement injury prevention strategies in real-world practice. This is despite such strategies proving successful in reducing overall injury incidence and burden. It has been hypothesised that this may be because the behavioural and contextual factors related to sports injury are not fully understood. Such factors stem from multiple key stakeholders, including the athlete. The primary aim of this study was to investigate athletes' knowledge and attitudes towards injury, injury reporting and prevention, as well as some of the barriers that may impact the future implementation of prevention strategies. DESIGN: Qualitative; with semi-structured interviews following an interpretivist approach. METHODS: Twenty-two field hockey athletes, playing in the top-tier Irish Hockey League were interviewed. Data were analysed using reflexive thematic analysis, with three general dimensions containing six higher-order themes. RESULTS: The findings highlighted that athletes have a varied understanding of injury, which tends to improve with experience. The reporting of injuries by athletes to members of the coaching staff was relatively poor. This may be due to limited resources and supports available to athletes which also cause challenges to injury prevention. CONCLUSIONS: Future injury prevention strategies in field hockey need to account for athletes' varied understanding of what constitutes an injury. Furthermore, policy changes to influence potential barriers to injury may assist in preventing or reducing the number of injuries being sustained by 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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".