Injuries and Risk Taking in Alpine Skiing
Bibliographic record
Abstract
The purpose of this study was to investigate the relation between motivation toward alpine skiing, attitude toward risk-taking behavior, risk-taking behavior, and injury incidence. Participants were skiers over eleven years of age. They completed a questionnaire that investigated skill level, sources of motivation toward alpine skiing, attitude toward risktaking behavior, and risk-taking behavior. A MANOVA was performed to compare three groups of skiers: (1) 163 skiers caught on the slope while performing a voluntary thrill-seeking behavior that could directly or indirectly lead to a sequence of events frequently associated to injuries (RISK TAKING); (2) 190 injured skiers (INJURED); and (3) 219 randomly selected skiers (UNINJURED). Significant differences were found between the three groups on age and skill level (p < 0.001). Skiers from the RISK TAKING group were younger (19.9 years old) than those from the INJURED group (24.7 years old) who were in turn younger than the UNINJURED group (30.7 years old). Skiers from the INJURED group were the least skilled, while those from the RISK TAKING group were the most skilled (p < 0.001). No differences were found between the INJURED and the UNINJURED groups on their source of motivation for skiing and their attitude toward risk taking. However, skiers from the RISK TAKING group were significantly different than the other two groups on those cognitive variables. They perceived the risky behaviors presented in vignettes as being less dangerous than skiers from the UNINJURED and the INJURED groups. These results suggest that in future prevention programs, the emphasis should be placed on the development of skiing technique among the lowskilled skiers. It also questions the strategy of targeting risk takers in prevention campaigns.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".