Understanding youths’ attitudes and practices regarding listening to music, video recording and terrain park use while skiing and snowboarding
Bibliographic record
Abstract
BACKGROUND: Skiing and snowboarding are popular activities among Canadian youth and these sports have evolved to include certain risk behaviours such as listening to music, using terrain parks, and video recording yourself or others. The objective of this study was to determine the prevalence of these risk behaviours and identify factors that are associated with the risk behaviours. METHODS: Using focus group methodology, a questionnaire was developed to capture aspects of the Theory of Planned Behaviour. A cross-sectional study was conducted where the questionnaire was administered to youth aged 13-18 during two winter seasons at two ski hills in Manitoba, Canada. RESULTS: The sample was comprised of 735 youth (mean age 14.9; 82.1% male, 83.6% snowboarding). The most common behavior was using the TP (83.1%), followed by listening to music that day (36.9%), and video recording that day (34.5%). Youth had significantly higher odds of listening to music that day if they planned to next time (OR 19.13; 95% CI: 10.62, 34.44), were skiing or snowboarding alone (OR 2.33; 95% CI: 1.10, 4.95), or thought listening to music makes skiing or snowboarding more exciting or fun or makes them feel more confident (OR 2.30; 95% CI: 1.31, 4.05). They were less likely to if they believed that music made it more difficult to hear or talk to others (OR: 0.35; 95% CI: 0.18, 0.65). Youth had significantly higher odds of using the terrain park if they believed that terrain parks were cool, challenging, or fun (OR: 5.84; 95% CI: 2.85, 11.96) or if their siblings used terrain parks (OR: 4.94; OR: 2.84, 9.85). Those who believed that terrain parks were too busy or crowded (OR: 0.31; 95% CI: 0.16, 0.62) were less likely to use them. Youth had significantly higher odds of video recording that day if they reported that they plan to video record next time (OR: 8.09, 95% CI: 4.67, 14.01) or if they were skiing or snowboarding with friends (OR: 3.65, 95% CI: 1.45, 9.18). Youth had significantly higher odds of video recording that day if they agreed that recording makes them try harder and improved their tricks (OR: 3.34, 95% CI: 1.38, 8.08) compared to those who neither agreed nor disagreed. Youth were less likely to record themselves that day if their friends did not do so (OR: 0.36; 95% CI: 0.16, 0.80). CONCLUSION: Common predictors of engaging in risk behaviours suggest that injury prevention programs may not have to be specific to each behaviour. Some strategies for injury prevention are suggested.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".