835 MEP006 – Performance enhancement goes hand-in-hand with health protection – stakeholders’ perceptions on testing and training measures in high-performance snow sports
Bibliographic record
Abstract
Background High-performance snow sports such as alpine skiing, freestyle skiing and snowboarding are known to be high-risk sports. Preventative testing and training measures may be considered for athletes’ preparation to support performance enhancement while protecting their health. Objective To explore the perspectives and perceptions of high-performance snow sports stakeholders on preventative testing and training measures in snow sports. Design Exploratory qualitative study with semi-structured interviews, following Grounded Theory principles. Setting High-performance snow sports athletes and staff participating on the World Cup and European Cup circuits of alpine skiing, snowboarding and free skiing. Participants Thirteen athletes, coaches, physiotherapists, and sports psychologists from different national teams, including Switzerland, Germany, Austria, Canada, Finland, Japan, Norway, New Zealand, and the United States of America. Interventions Exploratory study without intervention. Main Outcome Measurements Athletes’ and stakeholders’ perceptions and emerging concepts based on constant comparative data analysis. Results Participants described preparing the athletes in their best condition to perform as the overarching goal of testing measures and training methods. To do so, they mentioned two main targets: performance enhancement and health protection. Participants acknowledged health as a premise to perform optimally, considering testing and monitoring approaches, goal setting and training as part of preventive interventions to protect athlete performance. This continuous cyclic process is driven by communication and shared decision-making among all stakeholders, using testing and monitoring outputs to inform for goal setting, and training and injury prevention planification. Such approach helps athletes to achieve their goal of winning while being fit and healthy throughout athletes’ short- and long-term development. Conclusions The ultimate goal of testing and training in high-performance snow sports is winning. One of the components of this systematic approach is health and performance protection. Our findings provide insight into testing methods’ role to assess athletes’ status and inform training goals and prevention strategies.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".