Evaluation of a Bicycle Helmet Safety Program for Children
Bibliographic record
Abstract
BACKGROUND: Helmets have been shown to decrease the risk of brain injury; however, helmets must be worn correctly and fit well in order to be effective. The objective of this study is to determine whether kindergarten-aged children could learn and retain appropriate helmet wearing technique through an educational bicycle safety program. METHODS: Retrospective analysis of scores in questionnaires administered before and after an educational intervention to kindergarten students four to six years of age. The study took place in Prince Edward Island, Canada. A Wilcoxon Sign-Rank Test was used to determine if there was a significant overall increase in knowledge; McNemar chi-square tests were used to determine if there was an increase in knowledge for individual questions. RESULTS: There was significant improvement in pre-test to immediate post-tests scores and pre-test to delay post-test scores when the results were stratified by age, sex, bike riding status, and helmet wearing status (p<0.001 for all comparisons). In particular, correct responses for the questions regarding appropriate helmet distances from the eyes increased from 38.9% in the pre-test to above 90% in the post-tests (p<0.001). Correct responses for the question pertaining to appropriate fitting of helmet straps increased from 71.7% pre-test to above 90% in the post-tests (p<0.001). CONCLUSIONS: There was improved knowledge of appropriate helmet-wearing technique among kindergarten-aged children as a result of the educational intervention, and knowledge gains were retained for at least one month.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".