Familial Aggregation of High‐Risk Driving Behaviors in Northwestern Iran: A Cross‐Sectional Study
Bibliographic record
Abstract
Although the association between human factors, such as driving traffic risky behavior (DRB) and road traffic crashes (RTCs), have extensively been studied, there is a gap in understanding the role of familial predisposing factors in DRB occurrence. This study in northwestern Iran aimed to elucidate the sociodemographic profile of drivers and assess the familial aggregation (FA) of DRB in first‐degree relatives. This cross‐sectional study used stratified random sampling to examine the FA of DRB among 541 individuals in Tabriz, Iran, in 2023. The head of household served as a proband and first‐degree relatives were included. Data were collected using two standard self‐administered questionnaires. The generalized estimating equations with 95% confidence intervals (CIs) assessed the FA of DRB. The overall prevalence of high‐risk driving behavior was 46.02%, with significant FA observed between mothers and offspring (OR: 1.97, 95%CI: 1.05–3.69). Fathers or offspring with violation driving behaviors significantly increased the likelihood of similar violations among their offspring or fathers approximately. Offspring’s slip behaviors were significantly associated with these behaviors in their parents and siblings. Moreover, lapse behaviors showed significant FA among siblings. Our findings showed that FA exists in the DRB, particularly in the slip behavior dimension, with aggregation between fathers‐offspring, mothers‐offspring, and siblings. No FA of DRB was found between spouses. Regardless of the reason for FA, these imply that the family plays a significant role in DRB occurrence, suggesting the potential effectiveness of a family‐based prevention program. Screening programs are recommended to identify DRB in relatives referred to a trauma referral hospital to provide targeted preventive interventions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".