Knowledge and Practice of Road Safety Rules and Regulations among Secondary School Students
Bibliographic record
Abstract
Road traffic accidents are increasing in alarming ways. During adolescence period driving for curiosity, careless driving, and peer pressure are most common resulting high road traffic accidents. The main objective of the study was to explore the knowledge and practice of road safety rules and regulations. A descriptive cross sectional study design was used to conduct the study. Non-probability quota sampling technique was used to select 240 respondents from 800 students of class 11 and 12 from Pokhara Secondary School. Self-administered questionnaire was used to collect the information from respondents. The obtained data was entered on SPSS 16 version program and analyzed and interpreted by using descriptive statistics and inferential statistics. The findings revealed that more than half (59.2%) of the respondents had moderately adequate knowledge. Nearly three-forth (70.4%) of the respondents had average practice as a pedestrian and 75.7 percent of the respondents as a driver had average practice. Only 7.5 percent of the respondents were exposed to road traffic accident and high speed was the main cause of accident. More than three quarter (75.7%) had always driven vehicles without license. There was significant association (p=0.034) between the faculty of respondents (science and management) and level of knowledge of respondents on road safety rules and regulations. The study concluded that there was moderate level of knowledge and average level of practice of both drivers and pedestrians on road safety rules and regulations among secondary school students. It therefore suggests awareness programs on road safety rules and regulations are significant or school students to promote safety.
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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 | 0.000 |
| 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) | 0.002 | 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".