Perceptions Among Backcountry Skiers During the COVID-19 Pandemic: Avalanche Safety and Backcountry Habits of New and Established Skiers
Bibliographic record
Abstract
Introduction The coronavirus disease 2019 (COVID-19) pandemic impacted the ski industry worldwide by closing or limiting access to ski resorts. Subsequently, anecdotal reports of increased backcountry use emerged in the press, with concerns of inexperienced skiers causing or having problems in the backcountry. This study attempted to quantify this and identify motivations for new backcountry skiers. Methods Self-identified backcountry skiers and snowboarders (aged ≥18 y) in the United States and Canada completed an anonymous 29-question online survey distributed by regional avalanche centers, education providers, and skiing organizations (n =4792). Respondents were stratified by backcountry experience, defining “newcomers” who began backcountry skiing from 2019 to 2021, coincident with the COVID-19 pandemic. Percentages of ski days spent in the backcountry were compared before and during the COVID-19 pandemic using paired t-tests and across cohorts using repeated-measures analysis of variance. Avalanche education was compared using unpaired χ2 tests. Results Of established skiers, 81% noticed more people in the backcountry and 27% reported increasing their own use. Participants reported spending 17% (95% CI, 15.8–17.9) more of their days in the backcountry during the COVID-19 pandemic, with newcomers increasing their time spent by 36% and established skiers increasing their time spent by 13% ( P<0.0001). Of newcomers, 27% cited the COVID-19 pandemic as motivation to enter the backcountry and 24% lacked formal avalanche education, which is significantly higher than the 14% of established skiers ( P<0.0001). Conclusions Influenced by factors related to COVID-19, reported backcountry use increased during the pandemic. Newcomers had a lower level of avalanche education and less confidence in evaluating terrain. Because 80% of participants were recruited from avalanche safety or education websites, this likely underestimates skiers lacking avalanche awareness or education and is further limited by the nature of online surveys.
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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".