Relative Crash Involvement Ratio Associated with Different Sources of Young Drivers’ Distraction
Bibliographic record
Abstract
This paper aims to identify crash risk factors associated with young drivers’ distractions. Many factors were implicated including using mobile phones while driving or driving with passengers. Data needed for this study were collected from an online questionnaire survey. Beside young drivers’ distraction data, records on drivers’ demographics including age, gender and educational level were collected. Safety-related data on seat belt use were also collected. Each of the variables, contributing to young drivers’ distraction, safety, and educational level, was sorted into four categories according to young driver age (≤18, 19-22, 23-26, and 27-30 years old). The Relative Crash Involvement Ratio (RCIR) was estimated using the Quasi Induced Exposure Method (QIEM) using Microsoft Excel 2007. The results were then exported to the Statistical Package for Social Science Version 19 software. Paired t-test and ANOVA analysis were used to find the statistical significance in the RCIR values. Among the general findings, five outcomes were most prominent. The higher the educational level, the less likely young drivers would get involved in a crash. Young drivers ≤18 years old were almost 4.5 times more likely to get involved in a crash when 3 passengers travelled with them compared to 27-30 year-old drivers. Young drivers of all age groups were more likely to get involved in a crash when using mobile phones. Crash risk was reduced by up to 83 % when young drivers stopped using their mobile phone while driving. Crash risk was reduced by up to 93.43 % when young drivers used seat belt while driving. Finally, countermeasures to improve young drivers’ safety were proposed.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.004 | 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".