Does the inclusion of vehicle impoundment in provincial short-term administrative driver’s license suspension programs reduce total and alcohol-related fatal collisions in Canada?
Bibliographic record
Abstract
Objectives: To determine (1) whether the implementation of vehicle impoundment as part of provincial short-term administrative driver licensing suspension (ADLS) programs significantly reduced total and alcohol-related collision fatalities, and (2) if provinces with vehicle impoundment as part of their short-term ADLS programs see greater reductions in total and alcohol-related fatal collisions when compared to provinces without a vehicle impoundment law.Methods: Data on monthly total and alcohol-related fatal collisions from January 2005 to December 2016 are drawn from British Columbia (BC), Alberta (AB), Saskatchewan (SK), Manitoba (MB) and Ontario (ON). Changepoint time series analysis of fatal crashes is employed to detect within-province differences after implementing short-term impoundment programs, and between province differences comparing provinces with short-term impoundment programs (BC, introduced October 2010; AB, introduced July 2012; and SK, introduced July 2014) and those without (ON and MB). Outcome measures are the monthly per capita total and alcohol-related fatal collisions.Results: A significant reduction in per-capita alcohol-related fatal crashes was observed in British Columbia (−47.4%) in the period following the adoption of vehicle impoundment in their short-term ADLS. A significant decrease was also observed in Alberta (−37.5%), though this trend began prior to policy change; no significant effect was observed in per capita alcohol-related fatal crashes rates in Saskatchewan (−6.1%) in the two years following the introduction of vehicle impoundment. Ontario and Manitoba, two provinces without mandatory vehicle impoundment laws, also experienced significant reductions in per-capita alcohol-related fatal crashes between 2005 and 2016 (−36.4% and −35%, respectively).Conclusions: While mandatory vehicle impoundment programs for driving in the “warn” range (0.05%–0.08% BAC) have shown success in reducing fatal crashes in British Columbia, similar reductions in two other provinces with short-term vehicle impoundment were not observed. Moreover, large reductions in fatal crashes were observed in two provinces without vehicle impoundment as part of their short-term ADLS programs. Collectively, these findings suggest that vehicle impoundment, alone, has limited impact on fatal crash rates and that other factors help to explain the observed trends. Further analysis of new vehicle impoundment programs is warranted.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".