Risk Perception and Crash Involvement of Cell Phone Users While Driving Among Young Drivers in Developing Countries: The Case of Qatar
Bibliographic record
Abstract
Background: Cell phone use while driving is a significant safety problem all around the world. It is considered one of the main factors contributing to road crashes among young drivers. Aim: To address this problem, it is important to determine how young drivers perceive the risk of using a cell phone while driving and to understand whether the perception of risk is correlated with their crash involvement. Methods: Data were collected through a detailed questionnaire from young drivers in Qatar to assess potential correlations between the drivers’ demographic background, perception of risk, and crash involvement. Logistic regression models were developed to explore the relationships between those variables. Results: The analysis revealed that female drivers had a higher perception of risk related to using cell phones while driving compared to male drivers. Drivers with higher education levels were found to also have a higher perception of risk when compared to less educated drivers. The analysis showed that participants who perceived lower risk of answering a call while driving were more likely to be involved in a crash. Conclusion: These results can be useful to identify the groups that should be targeted through countermeasures. Different countermeasures were presented, and directions for future research 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".