The impact of non-pharmaceutical COVID-19 interventions on collisions, traffic injuries and fatalities across Québec
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
• COVID-19 prompted jurisdictions to curb down contagion by introducing multiple non pharmaceutical measures. • Road safety research has consistently shown the protective effects of these measures on traffic injuries and fatalities. • For the province of Quebec we introduce a unique non pharmaceutical COVID index, which considers 58 interventions. • Analyses demonstrate that the combination of COVID measures had uneven effects across regions. • We propose three research areas to build a deeper understanding of how non pharmaceutical COVID-19 interventions may have impacted road safety. 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it