A systematic review of correct bicycle helmet use: how varying definitions and study quality influence the results
Bibliographic record
Abstract
BACKGROUND: Bicycle helmets effectively reduce the risk of bicycle-related head injuries and trauma; however, they must fit properly to be effective. Little is known about the prevalence of correctly worn helmets and factors associated with proper helmet use. OBJECTIVE: To examine proper bicycle helmet use through a systematic review. METHODS: Comprehensive searches of electronic medical databases were performed, and completed by grey literature and reference list checks to identify eligible studies. Studies eligible for inclusion had to involve cyclists and report on the prevalence of correct or incorrect helmet use. Two reviewers independently selected studies and data were extracted regarding the prevalence and factors influencing proper helmet wearing of cyclists. RESULTS: An inclusive search strategy led to 2285 prescreened citations; 11 of the studies were finally included in the review. Overall, correct helmet use varied from 46% to 100%, depending on the criteria used by researchers to define proper helmet use; stricter criteria reduced the proportion of properly worn helmets. Adulthood, female sex and educational interventions were associated with correct helmet use in some studies. Self-reported poor helmet fit (OR = 1.96; 95% CI 1.10 to 3.75), posterior positioning of helmet (OR = 1.52; 95% CI 1.02 to 2.26) and helmet loss in crash (OR = 3.25; 95% CI 1.82 to 5.75) increased the risk of head injury. In addition, educational programmes on helmet use in schools increased correct helmet use among schoolchildren. CONCLUSIONS: This systematic review outlines the current state of the literature including the variability in research methodology and definitions used to study proper helmet-wearing behaviour among cyclists.
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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 | 0.064 | 0.275 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.012 |
| Bibliometrics | 0.019 | 0.021 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".