Identifying motorist characteristics associated with youth bicycle–motor vehicle collisions
Bibliographic record
Abstract
Objective: The objective of this study was to identify driver characteristics associated with youth bicycle–motor vehicle collisions in Alberta, Canada. Methods: Edmonton and Calgary police collision report data from the years 2010–2014 were used. From these data, motor vehicle collisions involving youth (<18 years old) were identified (cases). The controls were drivers who, over the same period, were involved in separate motor vehicle–only collisions but deemed not at fault using an automated culpability analysis. Control selection used the quasi-induced exposure method, assuming that not-at-fault drivers in collisions are representative of the typical driver (source population). Descriptive statistics, including proportions, medians, and interquartile ranges (as appropriate) were used to describe the characteristics of the case and control drivers. Purposeful variable selection techniques were used to inform multivariable logistic regression models and results are presented as adjusted odds ratios (aORs) and 95% confidence intervals (CIs). Results: Four hundred twenty-three drivers involved in youth bicycle–motor vehicle collisions were identified, as were 243,927 not-at-fault control drivers. Drivers >54 years old had higher odds of involvement in youth bicycle–motor vehicle collisions than drivers between 25 and 39 years old (aOR = 1.37; 95% CI, 1.03, 1.82). Compared to driving between 3:01 p.m. and 6:00 p.m., driving between 12:01 a.m. and 6:00 a.m. (aOR = 0.27; 95% CI, 0.11, 0.66), between 6:01 a.m. and 9:00 a.m. (aOR = 0.61; 95% CI, 0.44, 0.85), or between 9:01 a.m. and 12:00 p.m. (aOR = 0.26; 95% CI, 0.16, 0.41) had lower odds of bicyclist collision, whereas driving between 6:01 p.m. and 12:00 a.m. had higher odds (aOR = 1.34; 95% CI, 1.01, 1.79). Driving a truck/van had lower odds of bicyclist collision compared to driving a passenger car (aOR = 0.67; 95% CI, 0.48, 0.94). Conclusions: Culpability analysis is typically applied to motorists to identify transient exposures; however, this study used culpability analysis to select control drivers who could be compared with drivers involved in youth bicycle–motor vehicle collisions. This study highlights motorist characteristics in youth bicycle–motor vehicle collisions. In doing so, we hope to inform primary prevention strategies for motorists and the environment that will reduce collisions.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".