Effectiveness of bicycle helmet legislation to increase helmet use: a systematic review
Bibliographic record
Abstract
BACKGROUND: Head injuries related to bicycle use are common and can be serious. They can be prevented or reduced in severity with helmet use; however, education has resulted in modest helmet use in most developed countries. Helmet legislation has been proposed as a method to increase helmet wearing; while this social intervention is thought to be effective, no systematic review has been performed. OBJECTIVES: This review evaluates the scientific evidence for helmet use following legislation to identify the effectiveness of legislative interventions to increase bicycle helmet use among all age groups. SEARCH STRATEGY: Comprehensive searches of CENTRAL, MEDLINE, EMBASE, CINAHL, Web of Science, British Education Index, LILACS Database, TRIS (Transport Research Information Service), the grey literature, reference lists, and communication with authors was performed to identify eligible studies. SELECTION CRITERIA: Eligible studies for this review were community based investigations including cohort studies, controlled before-after studies, interrupted time series studies, non-equivalent control group studies Data collection and analysis: Two reviewers extracted the data regarding the percentage of helmet use before and after legislation from each study. Individual and pooled odds ratios were calculated along with 95% confidence intervals. MAIN RESULTS: Out of 86 prescreened articles, 25 were potentially relevant to the topic and 11 were finally included in the review. Of 11 studies, eight were published articles, two were published reports, and one was an unpublished article. One additional survey was incorporated following personal communication with the author. While the baseline rate of helmet use among these studies varied between 4% and 59%, after legislation this range changed to 37% and 91%. Helmet wearing proportions increased less than 10% in one study, 10-30% in four studies, and more than 30% in seven studies. While the effectiveness of bicycle helmet legislation varied (n = 11 studies; OR range: 1.2-22), all studies demonstrated higher proportions of helmet use following legislation, particularly when the law was targeted to a specific age group. CONCLUSIONS: Legislation increased helmet use among cyclists, particularly younger age groups and those with low pre-intervention helmet wearing proportions. These results support legislative interventions in populations without helmet legislation.
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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 | 0.013 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".