Risk management framework for competitive alpine skiing—co-developed with stakeholders
Bibliographic record
Abstract
Previous research has shown that stakeholders in the competitive alpine skiing communities consider risk management to be crucial in sports injury prevention. However, to date, there is a lack of a publicly available systematic and structured risk management approach for the competitive alpine skiing context. This work describes the codevelopment process of a risk management framework with stakeholders in the field of competitive alpine skiing. A panel of international experts invited through personal requests and with expertise in health protection and performance enhancement in competitive alpine skiing convened three times through online group meetings to co-develop a risk management framework through different activities. The underlying discussions focused on the fundamental questions of 'why', 'what', 'who', 'how' and 'for whom' and included the debate on specific examples from sports practice. The outcome after three meetings was a risk management framework. This framework includes a competitive alpine skiing-specific prevention wheel that integrates different stakeholder views relevant to different levels, their risk priorities, the main five domains and intervention areas identified from the literature, the graded and progressive timescale to intervene and the potential targets for risk management interventions. Moreover, the framework includes a decision-making tree, which operationalises the prevention wheel into a step-by-step sequence for risk management, including risk identification, risk assessment and risk mitigation. It should help stakeholders recognise their responsibilities and the potential actions they can take. Practical examples are provided to demonstrate how to apply the framework and to illustrate the complexity and dynamic interaction of the various factors in the competitive alpine skiing setting. The risk management framework developed lays a strong foundation for creating a safer environment for alpine skiers. It likewise contributes to providing overall awareness of the complexity and inter-relations of risks and prevention measures in the sport. By doing so, this framework has the potential to initiate further processes and on-field translation to sustainably and long-term improve athlete health and safety in competitive alpine skiing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.046 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".