The human factor in alpine skiing and snowboarding accidents
Bibliographic record
Abstract
Introduction: Hundreds of millions of people practice winter sports worldwide. Alpine skiing and snowboarding are associated with a possible risk of injury. There are at least three important factors that can affect safety in wilderness activities (environmental factors, technical factors and human factors). Awareness of human factors would allow us to reduce the risk in winter sports. Material and method: The objective of this study is to find out, through a self-explanatory cross-sectional personal survey, what and how human factors are involved in alpine skiing and snowboarding accidents. Results: 219 surveys were carried out of a total of 3,911 patients attended at the different health care points. The highest percentage of respondents related their accident to distraction or complacency, both in 72.2% of the respondents. Other factors that were pointed out by more than 50% were; lack of knowledge (60.4%), lack of following the norms (58.5%), fatigue (57.5%), lack of situational awareness (57%) and stress with (53.8% of the respondents). Conclusions: By identifying these most frequent human factors during downhill skiing and snowboarding, actions can be taken to prevent or contain human error.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".