The sex disparity in risky driving: A survey of Colombian young drivers
Bibliographic record
Abstract
Objective: The overrepresentation of young drivers in poor road safety outcomes has long been recognised as a global road safety issue. In addition, the overrepresentation of males in crash statistics has also been recognised as a pervasive young driver problem. Whilst progress in road safety evidenced as a stabilisation and/or reduction in poor road safety outcomes has been made in developed nations, less-developed nations contribute the greatest road safety trauma, and developing nations such as Colombia continue to experience increasing trends in fatality rates. The aim of the research was to explore sex differences in self-reported risky driving behaviours of young drivers, including the associations with crash-involvement, in a sample of young drivers attending university in Colombia. Methods: The Spanish version of the Behaviour of Young Novice Drivers Scale (BYNDS-Sp) was applied to a sample of 392 students (225 males) aged 16–24 years attending a major university in an online survey. Appropriate comparative statistics and logistic regression modelling were used when analysing the data. Results: Males reported consistently more risky driving behaviours, with approximately one-quarter of all participants reporting risky driving exposure. Males reported greater crash-involvement, with violations such as speeding associated with crash-involvement for both males and females. Conclusion: Young drivers in Colombia appear to engage in the same risky driving behaviours as young drivers in developed nations. In addition, young male drivers in Colombia reported greater engagement in risky driving behaviour than young female drivers; a finding again consistent with the behaviours of young male drivers in developed nations. As such, the research findings suggest that general interventions such as education, engineering, and enforcement should target transient rule violations such as speeding and using a handheld mobile phone while driving for young drivers in Colombia. Future research should investigate how these interventions could be tailored specifically for the Colombian cultural context, including how their effects can be evaluated, prior to implementation.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".