Evaluation of a School-Based Intervention to Reduce Traffic-Related Injuries among Adolescents in Beijing
Bibliographic record
Abstract
OBJECTIVE: Millions of adolescents are killed or injured in traffic accidents on the world's roads each year, but data on traffic-injury prevention programs targeting adolescents are limited, especially from developing countries. The aim of the study was to evaluate the effectiveness of a traffic-injury prevention program targeting adolescents in China. METHODS: We conducted a school-based traffic-safety intervention program with 2,759 students in two middle schools and two high schools in Beijing. An open-cohort, pre-post design with intervention and control groups was used to evaluate the intervention effect. RESULTS: Compared with the control group, the intervention group reported a significant increase in knowledge and awareness of traffic safety and a decrease in self-reported unsafe traffic behaviours. Students in middle school and girls reported better intervention effects than their high school and male counterparts. CONCLUSION: This study suggests that school-based traffic-injury prevention programs may increase participants' knowledge of traffic signs and awareness of traffic safety issues. The high traffic mortality in China, particularly in Chinese adolescents, suggests that more age- and culture-appropriate traffic safety promotion programs are needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".