Risk factors of bicycle traffic injury among middle school students in chaoshan rural areas of china
Bibliographic record
Abstract
BACKGROUND: Bicycle injuries are a leading cause of accidental death among children in the world, and bicycle-related injuries are also very common in China, thus to find out bicycle injury risk factors is imperative. This study aims to identify the cyclist-, bicycle- and road-related risk factors of bicycle injury, to develop health education programs as an intervention and to provide a scientific basis for establishing policies against bicycle injury. METHODS: We selected two middle schools randomly among seven schools in Chaoshan rural areas,where the main means of transportation for students from home to school was bicycle. The subjects were middle school students from 7th to 9th grades from Gucuo Middle School and Hefeng Middle School. Cyclists were surveyed through questionnaires about bicycle injury in the past 12 months. RESULTS: Multivariable logistic analysis showed that compared with a combination-type road、 motor lane and a non-intact road were both risk factors of bicycle injuries. This was followed by riding with fatigue, non-motor lane and inattentive riding. CONCLUSION: Bicycle injuries are frequent in China. Three risk factors on bicycle traffic injury among middle school students in Chaoshan rural areas of China were identified. This study provides important data to develop intervention strategies for China and other developing countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".