Roadside surveys evaluating immediate roadside suspensions for drinking drivers in British Columbia, Canada
Bibliographic record
Abstract
This paper presents evidence of the impact of new immediate roadside prohibitions (IRP) for drinking drivers as assessed by random roadside surveys of alcohol and drug use among nighttime drivers conducted prior to and following the introduction of IRP. Drivers were randomly selected from the traffic stream in five cities and asked to provide a breath sample to determine alcohol content and a sample of oral fluid to be tested for the presence of psychoactive drugs. The surveys were conducted between the hours of 21:00 and 03:00 on Wednesday through Saturday nights in June 2010 and again in June 2012. Driving after drinking decreased significantly following the introduction of IRP. In particular, the percentage of drivers with BACs over 80 mg/dL decreased by 59 per cent; drivers with BACs of at least 50 mg/dL decreased by 44 per cent. The decreases in drinking and driving were not restricted to specific sub-groups of drivers but were universal across age groups, sex, and communities. The results also revealed a changing pattern of drinking and driving. For example, the typical pattern of increased drinking and driving on weekend nights was not observed in 2012 and the prevalence of drinking drivers on the road during late night hours was less than half that found in 2010. The IRP program combined immediate short-term roadside suspensions with vehicle impoundment and monetary penalties to enhance the swiftness, certainty and perceived severity of sanctions for drinking and driving. These measures were associated with a substantial reduction in the prevalence of driving with a BAC over 50 mg/dL and driving with a BAC over 80 mg/dL.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".