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 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 | 0.001 |
| 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".