Driving Behaviors in Iran: A Descriptive Study Among Drivers of Mashhad City in 2014
Bibliographic record
Abstract
BACKGROUND: Driver-related behaviors are substantial causes for motor vehicle accidents. It has been estimated that about 95% of all accidents are due to driver-related dangerous behaviors and approximately 60% of accidents are directly caused by driving behaviors. The aim of this study was to assess driving behaviors and its possible related factors among drivers in Mashhad city, Iran. METHOD: In a cross-sectional design, a total number of 514 drivers in Mashhad, Iran Surveyed. Manchester driver behavior questionnaire with 50 questions evaluated dangerous driving behaviors in 4 categories "aggressive violations", "ordinary violations", "errors" and "lapses". RESULTS: In this study, the median age of drivers was 31. Besides, 58.2% of men mentioned having a history of driving accident. Our study indicated smoking and alcohol drinking as risk factors of having more accidents. Hookah abuse is a predictor of aggressive violations and errors. CONCLUSION: This is the first study to assess the relation of personal car and its market value with the likelihood of having accidents. Due to major influences of driving fines, cigarette smoking, alcohol consumption and addiction on violations and errors, we recommend pivotal measures to be taken by road safety practitioners regarding driving surveillance.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".