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Record W3189351602 · doi:10.1016/j.aap.2021.106324

The impact of COVID-19 on road safety in Canada and the United States

2021· article· en· W3189351602 on OpenAlex
Ward Vanlaar, Heather Woods-Fry, Hannah Barrett, Craig Lyon, Sarah Brown, Carl Wicklund, Robyn Robertson

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueAccident Analysis & Prevention · 2021
Typearticle
Languageen
FieldEngineering
TopicTraffic and Road Safety
Canadian institutionsTraffic Injury Research Foundation
FundersTransport CanadaPublic Health Agency of Canada
KeywordsPandemicInjury preventionPoison controlSuicide preventionOccupational safety and healthCoronavirus disease 2019 (COVID-19)Human factors and ergonomicsEnvironmental healthPsychology2019-20 coronavirus outbreakPublic healthTransport engineeringApplied psychologyDemographyEngineeringMedicineSociology

Abstract

fetched live from OpenAlex

The COVID-19 pandemic has led to the implementation of unprecedented public health measures. The effect of these lockdown measures on road safety remain to be fully understood, however preliminary data shows reductions in traffic volume and increases in risky driving behaviors. The objective of the present study is to compare self-reported risky driving behaviors (speeding, distracted driving, drinking and driving, and drugged driving) during the pandemic in Canada and the U.S. to determine what differences exist between these two countries. Data was collected using the Road Safety Monitor (RSM), an annual online public opinion survey that investigates key road safety issues, administered to a representative sample of N = 1,500 Canadian drivers and N = 1,501 U.S. drivers. Respondents were asked about the likelihood of engaging in risky driving during the pandemic as compared to before COVID-19. Results show the majority of respondents indicated their behavior did not change, and most positively, a small proportion reported they were less likely to engage in these risky driving behaviors. However, notable proportions indicated they were more likely to engage in risky driving behaviors during the pandemic, as compared to before COVID-19. Of those who indicated this, U.S. drivers had significantly higher percentages compared to their Canadian counterparts. Behaviors most often reported by this sub-section of drivers who admit to being more likely to engage in risky driving during the pandemic were speeding (7.6%) and drinking and driving (7.6%) in the U.S., and speeding (5.5%) and distracted driving (4.2%) in Canada. Logistic regression results confirm that country was a significant factor, as U.S. drivers had greater odds of reporting they were more likely to engage in these risky driving behaviors, with the exception of speeding. Age also had a significant effect, as increasing age was associated with lower odds of reporting that these risky driving behaviors were more likely during the pandemic. Conversely, sex did not have a significant effect. Overall, the current findings suggest that a small proportion of drivers reported being more likely to engage in risky driving behaviors and the pandemic may have led to changes in the profiles of those drivers engaging in risky driving behaviors during lockdown measures. These results have important implications for policies and can inform how to manage road safety during future lockdowns.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.690
Threshold uncertainty score0.270

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.255
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it