The impact of non-pharmaceutical COVID-19 interventions on collisions, traffic injuries and fatalities across Québec
Bibliographic record
Abstract
The association between non-pharmaceutical COVID-19 (NP-COVID-19) interventions, which aimed to regulate public behaviour to curb the spread of COVID-19, and road safety has become an important area of research to explore the unintended consequences of this pandemic. This study focuses on the 17 regions of the province of Quebec in Canada to assess the relation of NP-COVID-19 interventions on collisions, light and severe injuries, and traffic fatalities. Interrupted time-series analyses were conducted from 2015 to 2022, using daily traffic fatality and injury data per 100 000 population. A COVID-19 NP interventions index for Québec (QCnPI-Index) was created based on 58 interventions implemented from March 2020 to June 2022 in each region. Multiple controls commonly used in the road safety literature, such as weather conditions and seasonal patterns, were applied. The association between the QCnPI-Index and the four outcomes was mixed. First, the QCnPI-Index was associated with considerable reductions for collisions and light injuries in all regions. Significant reductions in severe injuries were linked to the index across six regions: Montérégie, Laurentides, Lanaudière, Laval, Outaouais and Montréal. No concomitant changes were observed in traffic fatalities across any region. Findings underscore the complex relationship between NP-COVID-19 interventions and road safety, emphasizing the need for more comprehensive efforts to understand their diverse effects. Further investigation is warranted to comprehend the discrepancy in the reduction of injuries and collisions compared to fatalities. This study ultimately highlights the importance of continuing exploring in future research additional factors, such as road safety interventions during COVID-19 periods, and concentrating on pedestrians and cyclists, to better understand the impact of NP-COVID-19 interventions on other road safety dimensions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".