Association between leisure activity and risky driving behaviour in young drivers in Canada
Bibliographic record
Abstract
Background: There are myriad risk factors for risky driving behaviour in youth. Perceived environment which is defined as the perception of driving risk and norms is the most complex factor. Leisure activities are a central part of youth’s daily lives that can substantially shape the driving perceived environment by providing the platform for interaction with peers, family, society, and media. However, the potential relationship between leisure activities and risky driving behaviour has seldom been studied. The purpose of this study was to examine the relationship between various leisure activities and risky driving behaviour among young drivers in Canada. Methods: An online survey-based cross-sectional study was conducted. Participants aged 16–24 years were approached through Facebook advertisements. The survey comprised of four questionnaires, namely, sociodemographic, personality trait (Mini-IPIP), leisure activities, and Behaviour Young Novice Driver Scale (BYNDS). Chi-square test examined differences between the driver group and proportional odds logistic regression was used to determine the relationship between the predictor variables and risky driving behaviour. Results: Participants (n=964), aged 18.34±2.31, were grouped into high risk (46.9%), medium risk (32.4%), and low risk (20.7%) drivers. Those with higher levels of drug engagement (OR=2.09, CI 95%=1.21-3.71), time with friends (OR=1.98, CI 95%=1.46-2.68), social media engagement (OR=1.83, CI 95%=1.34-2.49), and movie watching engagement (OR=1.52, CI 95%=1.00-2.31) tended to manifest more risky driving behaviour. In contrast, those with high levels of reading/writing engagement (OR=0.60, CI 95%= 0.42-0.85), volunteering engagement (OR=0.60, CI 95%=0.36-0.96), and playing video game engagement (OR=0.56, CI 95%=0.38-0.81) were less likely to perform risky driving behaviour. Other factors such as owning a car (OR=3.01, CI 95% 2.21-4.11), being male (OR=2.52, CI 95%=1.85-3.42), being simultaneously employed and a student, (OR=1.58, CI 95%=1.16-2.16), high driving exposure (OR=2.58, CI 95%=1.54-4.41), high neuroticism (OR=1.83, CI 95%=1.23-2.73), high extroversion (OR=1.60, CI 95%=1.09-2.35), and low imagination (OR=1.53, CI 95%=1.01-2.34) increased the likelihood of risky driving behaviour. Conclusions: This study provides new insight and explores the association between leisure activities and risky driving behaviour. Results from this study could be used to further explore leisure activities as a potential determinant of risky driving behaviour in future injury prevention research.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".