The relationship between motor vehicle collisions and cigarette smoking in Ontario: Analysis of CAMH survey data from 2002 to 2016
Bibliographic record
Abstract
Research has shown that tobacco users have an increased risk of collisions compared to nonsmokers. Studies from 1967 through 2013 documented a crude relative risk of collision involvement of about 1.5 among smokers compared to nonsmokers. In January 2009, in response to concerns about the health risks associated with potentially high concentrations of secondhand smoke resulting from smoking in vehicles, the provincial government in Ontario, Canada, introduced legislation restricting smoking in vehicles where children and adolescents are present. We examined the association between reported smoking and involvement in a motor vehicle collision in a large representative sample of adult drivers in Ontario, Canada, from 2002 and 2016, with particular focus on 2002-2008 and 2010-2016, periods before and after the legislation. Data are based on the Centre for Addiction and Mental Health (CAMH) Monitor. Among licensed drivers, prevalence of self-reported collision involvement within the past year for 2002-2008 was 9.39% among those who currently smoked compared to 7.08% of nonsmokers. Following implementation of the legislation, for 2010-2016, the prevalence of collisions for smokers was 7.01% and for nonsmokers was 6.02%. The overall difference for both smokers and nonsmokers between the two time periods was statistically significant; however, the difference between the two groups for the pre-legislation period was significant even after adjusting for potential confounders, while post legislation the difference was not significant. Prior to the legislation, the prevalence of collision was higher among smokers than nonsmokers; following the introduction of the legislation the prevalence was similar for the two groups.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".