Comparison Of Self-Declared Mobile Use While Driving In Canada, The United States, And Europe
Bibliographic record
Abstract
Existing literature on distracted driving includes very little research about the differences in self-reported prevalence worldwide. The current research aims to increase the available knowledge by comparing rates of various self-reported distracted driving behaviours from three different regions (Canada, the United States, and Europe). Self-declared mobile use (talking on a hand-held mobile, sending a text message or email), personal acceptability and attitudes towards mobile use while driving were measured as part of the E-Survey of Road users’ Attitudes (ESRA1) conducted in 25 countries during 2015-2016. The descriptive analysis compared rates of drivers’ mobile use behaviours, opinions, and attitudes by region. Two multivariate models predicting self-declared talking on a hand-held phone while driving, and self-declared sending of a text message or email while driving, were also estimated. This presentation will provide an overview of results from this research and place the findings in a broader context of distracted driving in particular and road safety in general, with special attention to differences between regions.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".