A bird’s eye view of driving safety culture: Truck drivers’ perceptions of unsafe driving behaviors near their trucks
Bibliographic record
Abstract
BACKGROUND: Transportation accidents are a global health concern and a leading cause of death. OBJECTIVE: A pragmatic way to decrease these accidents is to examine the routine opportunities that lead to them. Opportunities for accidents were identified by qualitatively examining the tacit knowledge possessed by truck drivers who observe unsafe driving behaviors near their trucks. METHODS: Face-to-face interviews were conducted with 158 truck drivers from 30 states in the United States (US) and three Canadian provinces. During the interviews, truck drivers made 703 observations of unsafe actions they routinely observe car drivers doing near their trucks. The observations were coded and analyzed with the assistance of a qualitative data analysis software program. RESULTS: The findings revealed 20 unsafe driving behaviors that lead to elevated risk for car drivers. The most common unsafe action (observed by 89% of truckers) involved cars passing trucks and then cutting back into their lane too soon - the 'front no zone' safe space. Driving distractions comprised the second group of most commonly observed risky behaviors. CONCLUSIONS: The findings reveal that new drivers should receive truck driver awareness training as part of their licensing process and that public health campaigns be developed on the risks of driving near trucks.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".