Exploring the association between a periodic safe-ride program and urban alcohol-impaired driving crashes in Quebec, Canada: a cross-sectional time-series analysis
Bibliographic record
Abstract
INTRODUCTION: In Canada, alcohol-impaired driving is a persistent public health concern in need of effective community-based prevention strategies. This study examined the association between the number of rides offered by a safe-ride program in the province of Quebec every December and alcohol-related injury crashes during the 2000-2019 period. METHOD: Safe-ride programs in four cities were examined (Montreal, Quebec, Sherbrooke and Trois-Rivières) using an ecological approach. The data set was structured as a balanced cross-sectional time series. Random-effects negative binomial regression modelled the relationship between the number of rides provided by the safe-ride program and night-time alcohol-related crashes involving serious injuries and fatalities, with individual city population as an offset variable. RESULTS: The median number of night-time alcohol-related crashes for the months of December for the 2000-2019 period was 3.0 (IQR=1.5-4.5). The median number of rides offered was 16 894 (IQR=15 586-18 391). The association between the number of rides provided by the safe-ride program and night-time alcohol-related crashes (IRR=1.0002; 95% CI 0.9999, 1.0005) was not significant. CONCLUSION: The role of the number of rides provided by the safe-ride program in reducing night-time alcohol-related crashes was inconclusive. Specific program features may influence the findings. Future research is needed to understand the specific characteristics of safe-ride programs that could influence their putative benefits.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".