Evaluating the Effectiveness of Safety Awareness Initiatives in Promoting Helmet Adoption among E-Bike Riders
Bibliographic record
Abstract
E-bikes have gained significant popularity in China from their convenience and eco-friendliness, but the lack of helmet use among riders poses serious head injury risks. This study evaluated the effectiveness of safety campaigns and educational initiatives in promoting helmet adoption among e-bike riders in Guangdong Province, China, across three perceived effectiveness levels: ineffective, moderately effective, and effective. Data from a questionnaire survey were analyzed using an optimized eXtreme Gradient Boosting (XGBoost) model, with hyperparameters tuned via Optuna and performance validated through 10-fold cross-validation, achieving 84.6% accuracy. Class imbalance was addressed using the synthetic minority over-sampling technique, yielding macro-averaged precision, recall, and F1-scores of approximately 0.82, indicating balanced predictive performance. Model interpretability with SHapley Additive exPlanations (SHAP) identified safety education frequency, enforcement of helmet use, riding experience, ideal education locations, attitudes toward helmet use, and age group as the most influential predictors of helmet adoption effectiveness. Safety education frequency contributed most at the ineffective (25.36%) and effective (18.65%) levels, while enforcement was most influential at the moderately effective level (19.11%), with persuasion-plus-fines outperforming education-only strategies. Partial dependence plots (PDPs) and individual conditional expectation (ICE) plots showed that monthly or quarterly education substantially improves perceived effectiveness, whereas infrequent or no education increases perceived ineffectiveness. Younger riders (under 25 years) and those with negative helmet attitudes were more likely to perceive initiatives as ineffective. Findings highlight actionable recommendations: institutionalizing frequent safety education in high-impact locations, integrating enforcement with education, and tailoring interventions to demographic and attitudinal profiles, offering policymakers evidence-based approaches to enhance helmet adoption and rider 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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".