Bicycle helmet campaigns and head injuries among children. Does poverty matter?
Bibliographic record
Abstract
OBJECTIVES: To assess the impact of a community based bicycle helmet programme aimed at children aged 5-12 years (about 140,000) from poor and well off municipalities. METHODS: A quasi-experimental design, including a control group, was used. Changes in the risk of bicycle related head injuries leading to hospitalisation were measured, using rates ratios. RESULTS: Reductions in bicycle related head injuries were registered in both categories of municipalities. Compared with the pre-programme period, the protective effect of the programme during the post-programme period was as significant among children from poor municipalities (RR= 0.45 95%CI 0.26 to 0.78) as among those from richer municipalities (RR=0.55 95%CI 0.41 to 0.75). CONCLUSION: Population based educational programmes may have a favourable impact on injury risks in poor areas despite lower adoption of protective behaviours.
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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 | 0.024 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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".