Comparison of Self-Declared Mobile Use While Driving in Canada, the United States, and Europe: Results from the European Survey of Road Users’ Safety Attitudes
Bibliographic record
Abstract
Existing literature on hand-held mobile use while driving includes little research about the differences in prevalence worldwide. The current research aims to increase the available knowledge by comparing rates of self-reported hand-held mobile use behaviors while driving 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 toward mobile use while driving were measured as part of the E-Survey of Road Users’ Attitudes (ESRA 1) conducted in 25 countries during 2015–2016. Survey data was collected uniformly, allowing for full comparability among regions. The descriptive analysis compared rates of drivers’ mobile use behaviors, 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. Regional comparisons of the descriptive results demonstrate that both self-declared behaviors were the lowest in Canada. Multivariate models show that U.S. drivers’ personal acceptability, attitudes, and support for zero-tolerance measures had a significant effect on their self-declared rates of hand-held mobile use while driving. Furthermore, when examining self-declared talking on a hand-held mobile device while driving across all regions, a common social phenomenon known as the bandwagon effect was found, as those who agreed that almost all car drivers occasionally talk on a hand-held mobile while driving were 2.41 times more likely to report doing so themselves. Implications and suggestions for further research are discussed.
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 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) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".