International Snow Science Workshop (2002: Penticton, B.C.) SNOWSMART: Communicating snow risk management to Canada's youth
Bibliographic record
Abstract
Youth are frequent participants in the winter sports of skiing, snowboarding and snowmobiling. They often lack experience and take more risks, resulting in injuries and even death. The purpose of SNOWSMART is to develop culturally relevant messages for Canadian youth to increase their knowledge and awareness ofrisk associated with winter sports. Assisting youth to make positive, smart choices and changing behaviours, will help to reduce the number ofwinter sports related search-and-rescue responses, injuries and deaths in Canada Data was collected through surveys, one-on-one interviews and focus groups with Canadian youth aged 12 - 18. Youths' existing attitudes towards risk associated with the three sports were identified. Participants in each sport have distinctly different attitudes, risk perception and behaviours. Threat of injury is not a big deterrent for youth. Many believe injuries are cool and lead to progress in the sport. Participants suggested school-based learning from experienced role models in their sport and from people who survived traumatic injuries as an ideal way to reach their population. Teacher-delivered lesson plans were designed for Grade 7 and 10 physical education and science curricula. A video, public service announcements and facilitation tips/strategies outlining how to deliver SNOWSMART are included. Students' posttests indicate a heightened awareness of smart risk taking and avoidance ofinjury. This is the first comprehensive winter sports risk management/awareness program combining a school-based approach with a marketing campaign. SNOWSMARTwill assist motivated youth to develop the knowledge and skills necessary to prevent personal injury and loss oflife while participating in winter sports.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.006 |
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".