Prevalence of helmet use in skateboarders: A systematic review and narrative synthesis
Bibliographic record
Abstract
INTRODUCTION: Helmet adoption remains low among skateboarders, but the specific prevalence remains unclear. The purpose of this study was to summarize available evidence on helmet-use prevalence among skateboarders. METHODS: A systematic search of PubMed and Web of Science was conducted using the search terms: skateboard, skateboarding, protective gear, protective equipment, pads, or helmet. Articles were included if they were in English with specific data on helmet use among skateboarders. Articles excluded were those representing reviews or conference abstracts/presentations; or if helmet use prevalence for skateboarders could not be ascertained. Data extraction and quality assessment were performed independently by two reviewers, with a third resolving disagreements. Study quality was assessed using the Newcastle Ottawa Quality Assessment Scale. RESULTS: A total of 228 articles were identified, of which only 17 studies (7.5%) met inclusion criteria. Included studies spanned four decades (1992-2023) and covered populations across North America, Australia, and Europe. Helmet use prevalence was consistently low, ranging from 0% to 41.7%, with variations by age, sex, and region. Most studies were of moderate quality (n = 10). Samples were male dominant, with many studies not reporting additional demographics. Barriers to helmet use included comfort and perceived inconvenience. Findings also highlighted worse outcomes for skateboarders without helmets compared to those with helmets. CONCLUSIONS: Findings demonstrated a consistently low prevalence of helmet use among skateboarders, despite its established role in reducing head injuries. These findings underscore the need for targeted public health initiatives to promote helmet use and address barriers to adherence. PRACTICAL APPLICATIONS: Public health campaigns and educational programs should focus on increasing awareness of helmet effectiveness while addressing common barriers such as comfort and social norms. Additionally, improvements in helmet design and partnerships with influential skateboarders could help normalize helmet use and encourage adoption within the skateboarding community.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.094 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".