Impact of non-pharmaceutical COVID-19 interventions on cyclist and pedestrian injuries in five cities of the province of Quebec
Bibliographic record
Abstract
The relationship between non-pharmaceutical COVID-19 (NP-COVID-19) interventions, which aimed to regulate public behavior to curb the spread of the virus, and road safety has become a crucial area of research to explore the unintended consequences of the pandemic. This study examines the five cities of Quebec, Canada, to assess the impact of NP-COVID-19 interventions on injuries and killed and severe traffic injuries involving cyclists and pedestrians . 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 developed, incorporating 58 interventions implemented from March 2020 to March 2022 across the cities. Multiple controls commonly used in road safety research, such as weather conditions and seasonal patterns, were applied. We divided the pandemic period into four distinct semesters to facilitate our understanding of changes within the pandemic. Our findings reveal a complex interaction between NPIs and road safety, with reductions in pedestrian injuries and KSI during periods of stringent NPIs, particularly in Montreal and Quebec City. Conversely, for cyclists, the impact varied, showing both increases and decreases in injuries and KSI across different cities and semesters. These results underscore the need for tailored road safety strategies that consider the unique patterns of pedestrian and cyclist mobility during 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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".