Preliminary Evidence Of A Relationship Between Injury And Sport Camera Use In Winter Sliding Sports
Bibliographic record
Abstract
PURPOSE: The accessibility of digital technologies has led to an increased use of video cameras in sliding winter sports. However, very little is known on the risks associated with the use of such equipment. In other contexts, camera use was associated with a social facilitation effect involving an increase in performance (Yu et al., 2015). In winter sliding sports like snowboard and alpine skiing, the culture is characterized by a valorization of risk taking (Anderson, 1999). This effect could translate in greater risk taking when a camera is around (Rodrigue et al, 2012). The aim of this study was to explore the relationship between camera use and injury risk in winter sliding sports, while considering psychosocial factors associated with injuries including age, sex, perceived skill level, intentional risk taking and personality traits (impulsivity and sensation seeking). METHODS: The study was a self-reported follow-up survey conducted online among canadian winter sliding sports athletes before and after a winter ski season within an interval of 4 months. RESULTS: Among the 224 adolescents and adults (121 men and 103 women) who completed the surveys, 32,6% were aged 14-25 years, 32,3% aged 26-25 years and 36,2% aged 36 years +. Descriptive statistics indicates that 37,1% were filmed during sports practice at least once during past 12 months prior to the study an 42,0% were filmed at least once during the follow-up ski season. Among them, 25,7% reported that they take more risks when they are filmed “sometimes”, “often” or “always”. A logistic regression analysis predicting the occurrence of an injury by the end of the ski season indicates that camera use during the ski season is significantly associated with injury risk (OR = 0,25 p < 0,001) even after including psychosocial factors usually associated with injury risk in the model, including intentional risk taking, perceived skill level and sensation seeking also being significant predictors of injury. CONCLUSIONS: These results suggest a possible injury risk associated with the use of a camera on the slopes, partially explained by a social facilitation effect, but it is unclear at this moment if this risk could also involve the camera itself depending on the type of use (i.e. on a selfie stick). These results should be supported by objective data from an experimental design.
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 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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".