The prevalence and correlates of texting while driving among a population-based sample of Ontario students
Bibliographic record
Abstract
Objective: Texting while driving (TWD) has a deleterious impact on driving performance and may pose a significant challenge to traffic safety. This challenge may be particularly relevant for young and inexperienced drivers. This study examined the prevalence and risk factors of writing text messages or emails while driving during the past 12 months. Method: This study analyzed a subpopulation of 1,133 licensed students 16 years of age or older from the 2013 Ontario Student Drug Use Survey (OSDUHS), a population-based survey of students in Ontario, Canada. Results: Our results indicate that 36% of licensed drivers reported writing a text message while driving during the past 12 months; of those who did, 56% reported doing so 4 or more times. Graduated licensing was the strongest factor predicting TWD. Compared to students with the more restrictive G1 license, students with a G2 or full license were 9.4 times more likely to report TWD after controlling for the effect of all other factors. Older students, white students, and students attending school in urban centers were more likely to report TWD, and the amount of time spent on social media sites, being a passenger with a driver using substances, and past-year collisions were also significantly associated with TWD. Gender differences and participation in driver education training were not associated with TWD. Conclusions: This research demonstrates that TWD is an extremely common behavior among licensed student drivers in Ontario, particularly among those who have passed the first stage of graduated licensing. TWD is associated with other risky driving behaviors and outcomes, and the findings from this study underscore the need to better understand the harms associated with this behavior.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".