Use of Electronic Communication Devices by Canadian Drivers in Urban Areas
Bibliographic record
Abstract
The use of electronic communication devices (ECDs) such as cell phones, smart phones, and tablets by drivers has been found to increase the likelihood of motor vehicle crashes in a number of epidemiological studies. The last time that cell phone use in Canada was observed, an estimated 3.6% of drivers were talking on cell phones in rural areas in 2009 and 3.3% were using them in urban areas in 2010. Given that most Canadian jurisdictions have passed legislation prohibiting the use of hand-held devices by drivers, there is interest in whether there has been a change in the use of these devices. An observational survey was conducted at 286 urban sites across Canada during September 2012. A total of 70,686 drivers of light duty vehicles were observed while they were stopped at a traffic light or a stop sign. Whether they were using an ECD was recorded as well as the type of usage (i.e., speaking, typing, both), driver age and gender, number of passengers in the vehicle, and type of vehicle. The weighted national urban survey results show that an estimated 4.6% (± 0.5) of the drivers used an ECD, varying by jurisdiction from 1.3 to 7.0%. ECD use was more frequent among young drivers (<25 years of age), drivers of light trucks, drivers without passengers, and somewhat more by female drivers. ECDs were used for talking by 2.3% of drivers and for typing by 1.7% of drivers. Nationally, the use of hand-held ECDs for talking was 61% lower in 2012 than that observed in 2007. A detailed analysis of drivers talking on ECDs before and after laws prohibiting their use came into effect indicated that usage was significantly lower after the law in nine jurisdictions.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".