Factors predicting local effectiveness of impaired driving laws, British Columbia, Canada
Bibliographic record
Abstract
Objective: In 2010 British Columbia introduced laws allowing police to suspend licenses and impound vehicles of drinking drivers based on roadside breathalyzer results. This study examines regional variation in the effectiveness of the new laws and identifies local factors that may have modified the law’s effectiveness. Methods: First, we used an interrupted time series design with a negative binomial mixed model to study the change in rate of single vehicle nighttime crashes (SVNCs, a proxy for alcohol related crashes) in each of British Columbia’s 118 police patrol after the laws were introduced. Next, the role of explanatory factors was studied using patrollevel estimated percent reduction in SVNCs as the response variable in a linear regression model with spatially autocorrelated errors that included explanatory factors as covariates. Results: The average rate of SVNCs across police patrols was 12% lower (95%CI:9.1%-14.8%) following the new laws, and all police patrols included in our model had estimated reductions in SVNCs. Reductions in SVNCs were positively associated with the number of alcohol serving establishments per capita and with socioeconomic status and negatively associated with baseline rates of traffic citations. Conclusions: The laws were associated with improved road safety across the province but were least effective in more socioeconomically disadvantaged regions. Increased effectiveness in regions with more alcohol serving establishments may have indicated a specific deterrent effect on drinking drivers who frequent these establishments The laws were also more effective in regions with lower baseline traffic law enforcement (i.e. lower citation rates), perhaps because media reports about the IRP regime created unease in drinking drivers who previously knew that their risk of punishment for traffic law violations was low.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".