Influence of non-pharmaceutical COVID-19 interventions on speed-related and alcohol-related traffic injuries in five cities of the province of Québec, Canada
Bibliographic record
Abstract
INTRODUCTION: Understanding the impact of non-pharmaceutical COVID-19 interventions (NPIs) on road safety has become increasingly important to uncover the unintended consequences of the pandemic. This study explores how NPIs influenced alcohol-related and speed-related traffic collisions, including fatalities and serious injuries, in five cities of the province of Québec, Canada: Montréal, Québec, Laval, Longueuil and Sherbrooke. METHODS: We performed Poisson interrupted time-series analyses using daily traffic fatality and injury data from 2015 to 2022, to assess the change in rate expressed per 10 000 population. A Québec COVID-19 NPIs Index was applied, incorporating 58 interventions enacted from March 2020 to March 2022 in these cities. We accounted for weather conditions and seasonal patterns and divided the pandemic period into four semesters to better understand changes over time. RESULTS: The analysis revealed a nuanced interaction between NPIs and road safety. Alcohol-related injuries decreased during stringent NPIs, particularly in Montréal, Québec city and Longueuil. In contrast, the effects on speed-related incidents were mixed, with Montréal and Laval, showing increases and the other three cities displaying no meaningful changes across the four semesters. CONCLUSIONS: These findings highlight the necessity for ad hoc road safety strategies that address specific patterns of alcohol consumption and speeding risks during future pandemic-related disruptions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".