THE IMPACT OF NON-PHARMACEUTICAL COVID-19 INTERVENTIONS ON ROAD SAFETY ACROSS QUEBEC
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 a significant focus in understanding the unintended consequences of the pandemic. This study concentrates on 17 administrative units to assess the impact of NP-COVID-19 interventions on traffic injuries, and 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 Quebec (QCnPI-Index) was created based on 58 interventions implemented from March 2020 to July 2022 in each administrative unit. The association between QCnPI-Index and the two outcomes was mixed. Outaouais, Laval and Laurentides were the only regions where traffic fatalities were significantly reduced. Decreases by 97% (Incidence Rate Ratio [IRR]: 0.03, 95% Confidence Interval [CI]): 0.01,0.62), 99.0% (IRR: 0.01, 95% CI: 0.00,0.27) and 88% (IRR: 0.12, 95% CI:0.02,0.58) were observed respectively. Traffic light injuries significant reductions, except for Côte-Nord and Nord-du-Québec, were observed in 15 regions. The highest reduction of traffic injuries was observed in Laval with a 76% decrease (IRR: 0.24, 95% CI:0.17,0.34). The findings underscore the complex relationship between NP-COVID-19 interventions and road safety, emphasizing the need for comprehensive efforts to understand their diverse effects. Further investigation is warranted to comprehend the discrepancy in the reduction of fatalities compared to injuries. This study highlights the importance of exploring additional factors, such as road safety interventions and analysing specific road users like pedestrians and cyclists, to better understand the impact of NP-COVID-19 interventions on road safety.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".