Effective factors of improved helmet use in motorcyclists: a systematic review
Bibliographic record
Abstract
BACKGROUND: Road traffic injuries (RTI) are one of the most prominent causes of morbidity and mortality, especially among children and young adults. Motorcycle crashes constitute a significant part of RTIs. Policymakers believe that safety helmets are the single most important protection against motorcycle-related injuries. However, motorcyclists are not wearing helmets at desirable rates. This study systematically investigated factors that are positively associated with helmet usage among two-wheeled motorcycle riders. METHODS: We performed a systematic search on PubMed, Scopus, Web of Science, Embase, and Cochrane library with relevant keywords. No language, date of publication, or methodological restrictions were applied. All the articles that had evaluated the factors associated with helmet-wearing behavior and were published before December 31, 2021, were included in our study and underwent data extraction. We assessed the quality of the included articles using the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist for observational studies. RESULTS: A total of 50 articles were included. Most evidence suggests that helmet usage is more common among drivers (compared to passengers), women, middle-aged adults, those with higher educations, married individuals, license holders, and helmet owners. Moreover, the helmet usage rate is higher on highways and central city roads and during mornings and weekdays. Travelers of longer distances, more frequent users, and riders of motorcycles with larger engines use safety helmets more commonly. Non-helmet-using drivers seem to have acceptable awareness of mandatory helmet laws and knowledge about their protective role against head injuries. Importantly, complaint about helmet discomfort is somehow common among helmet-using drivers. CONCLUSIONS: To enhance helmet usage, policymakers should emphasize the vulnerability of passengers and children to RTIs, and that fatal crashes occur on low-capacity roads and during cruising at low speeds. Monitoring by police should expand to late hours of the day, weekends, and lower capacity and less-trafficked roads. Aiming to enhance the acceptance of other law-abiding behaviors (e.g., wearing seat belts, riding within the speed limits, etc.), especially among youth and young adults, will enhance the prevalence of helmet-wearing behavior among motorcycle riders. Interventions should put their focus on improving the attitudes of riders regarding safety helmets, as there is acceptable knowledge of their benefits.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".