Analysis of the effectiveness concerning initiatives for pedestrians’ safety on roads: a questionnaire survey
Bibliographic record
Abstract
Abstract Introduction. One of the most important issues and challenges of public safety policy in highly developed countries is the problem of ensuring road safety, along with dealing with health and material losses resulted from accidents. Unfortunately, Poland, in comparison to other EU countries, has one of the worst statistics regarding accidents and mortality ratios, therefore it is essential to undertake long-term actions aimed at improving road safety, as well as educating drivers and pedestrians. Aim. The purpose of this study is to analyse the problem and to investigate whether traffic safety campaigns are effective, and if they reach young audience. Material and methods. The research was conducted in the first quarter of 2018 among the students of Faculty of Health Sciences of the Medical University of Lublin. The study method applied was a diagnostic survey, and the tool was an original questionnaire. The participation was voluntary and anonymous. The obtained results were the subject of later statistical analysis. As many as 153 young respondents aged between 18 and 25 were tested, out of whom 73.68% were women and 26.32% were men. Results. Over half of the surveyed (56.3%) declared walking as the main form of participation in traffic, and 25.9% of the surveyed used public transport. The most numerous group of the examined (34.21%) thought that the Polish roads were rather unsafe. As the reasons for the low safety on the Polish roads the respondents identified: bad condition of the roads (69.1%), drivers’ recklessness (67.1%), and maladjustment of the driving style to weather conditions (60.5%). All respondents have encountered campaigns concerning road safety, however not too often (56.58% less than once a month). The surveyed encountered these social campaigns on TV (88.2%), on the Internet (54.6%), and on billboards (23.7%). Conclusion. Promotional campaigns concerning road safety have a greater impact on women. In the examined group, around 67% of women changed their behaviour as a result of the campaigns, and only 42.5% of men. Social campaign which was best remembered, and had the biggest influence on the respondents (50.3%) was „Say STOP to reckless driver you love”.
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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 | 0.005 | 0.010 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".