Road Use Pattern and Street Crossing Habits of Schoolchildren in India
Bibliographic record
Abstract
Road traffic accidents (RTAs) contribute significant DALYs in the global burden of diseases. Vulnerable groups particularly pedestrians and children are at an increased risk. Road use pattern, street crossing habits, and road safety awareness are important determinants of RTAs. The present study was carried out to assess the road use pattern and street crossing habits of schoolchildren. This cross-sectional study included 497 schoolchildren of 12-15 years. The interview technique was used as a tool for data collection on a predesigned questionnaire. A total of 40.4% of schoolchildren did not like to go to school alone and wanted somebody from the family to drop them to school. About one quarter of the students were afraid of traffic and expressed their inability to deal with traffic on the road. A total of 10.7% reported crossing the street in groups, and 1.4% reported running while crossing the street. Only 80.9% of students received some form of road safety training, and the parents and schools were the major source of information for such safety training. Age <14 years and a lower level of mother's education were found to be significant contributors for poor road crossing habit in univariate as well as multivariate analysis. The study suggests that the knowledge regarding safe road use and street crossing was lacking among study participants albeit in a small proportion only. Safety aspects can be partly strengthened by imparting practical knowledge about road use pattern, street crossing habits, and road safety procedures.
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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 | 0.000 | 0.000 |
| 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 | 0.001 | 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".