Perceived key injury risk factors in World Cup alpine ski racing—an explorative qualitative study with expert stakeholders
Bibliographic record
Abstract
BACKGROUND: There is limited knowledge about key injury risk factors in alpine ski racing, particularly for World Cup (WC) athletes. OBJECTIVE: This study was undertaken to compile and explore perceived intrinsic and extrinsic risk factors for severe injuries in WC alpine ski racing. METHODS: Qualitative study. Interviews were conducted with 61 expert stakeholders of the WC ski racing community. Experts' statements were collected, paraphrased and loaded into a database with inductively derived risk factor categories (Risk Factor Analysis). At the end of the interviews, experts were asked to name those risk factors they believed to have a high potential impact on injury risk and to rank them according to their priority of impact (Risk Factor Rating). RESULTS: In total, 32 perceived risk factors categories were derived from the interviews within the basic categories Athlete, Course, Equipment and Snow. Regarding their perceived impact on injury risk, the experts' top five categories were: system ski, binding, plate and boot; changing snow conditions; physical aspects of the athletes; speed and course setting aspects and speed in general. CONCLUSIONS: Severe injuries in WC alpine ski racing can have various causes. This study compiled a list of perceived intrinsic and extrinsic risk factors and explored those factors with the highest believed impact on injury risk. Hence, by using more detailed hypotheses derived from this explorative study, further studies should verify the plausibility of these factors as true risk factors for severe injuries in WC alpine ski racing.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".