Acceptance of Risk and Confidence Assessing Avalanche Terrain and Conditions: A Large Cross-Sectional Study
Bibliographic record
Abstract
IntroductionThe COVID-19 pandemic affected the ski industry globally, including limiting access to ski resorts and prompting more skiers and snowboarders to explore the backcountry. In this study, we quantified the willingness to take risks (risk propensity) and self-perceived ability to assess hazards in the backcountry and to explore correlations between these factors.MethodsWe based our study on a previously reported data set gathered under the supervision of our senior author, who collected anonymous responses to a 29-question online survey completed by 4792 self-identified backcountry skiers and snowboarders (aged ≥18 yr) in the United States and Canada. The survey was distributed primarily through regional avalanche centers, education providers, and skiing organizations. Pearson correlation coefficients and multivariable linear regression models were used to analyze associations among variables. More specifically, we examined the relationships among confidence in assessing avalanche terrain and willingness to take risks, level of avalanche training, years of experience, and days per season of backcountry skiing.ResultsWe identified a positive correlation between confidence in assessing avalanche terrain and willingness to take risks, level of avalanche training, years of experience, and days per season of backcountry skiing. Female respondents demonstrated lower risk willingness and self-reported ability to assess avalanche risk compared to males. Over 30% of individuals lacking level 1 avalanche training expressed confidence in appraising complex terrain. Our findings demonstrated a positive correlation between greater risk propensity, formal avalanche education, and increasing confidence in assessing avalanche terrain. However, we also observed concerningly high confidence levels among skiers with minimal or no training.ConclusionsAvalanche education should focus on aligning skiers' confidence with their actual abilities to reduce overconfidence and enhance safety. We recommend that future research aim to include a more diverse sample, especially those less engaged in formal avalanche education.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".