Validation of observational before–after safety studies in Canada during COVID-19 pandemic: A “no treatment” evaluation
Bibliographic record
Abstract
The COVID-19 pandemic caused a substantial shift in global traffic volume and travel behavior. However, the literature lacks an assessment of its impact on road safety evaluations, raising concerns about the accurate assessment of safety countermeasures implemented during the pandemic and the evaluation of crash records within that timeframe. This study aimed to examine the applicability of existing methodologies to assess the effects of countermeasures implemented during the COVID-19. This was carried out in the context of a “no-treatment” evaluation for a set of signalized intersections in various Canadian jurisdictions, observing a time frame with COVID-related mobility restrictions in 2020 and 2021. The methodologies tested were the most well-known and used in the field, i.e. the comparison group (CG) method, the empirical Bayes (EB) method, the EB method with CGs, and the full Bayes (FB) method with linear intervention models. The results showed that all methods analyzed were able to identify the hypothetical treatment within the confidence levels of the estimated crash modification factors, with different degrees of accuracy and precision. These results, therefore, will be vital for practitioners to select and decide on the methodology to be used in assessing countermeasures occurred during COVID-19 pandemic.
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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 | 0.155 | 0.308 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".