Prevention of bicycle-related injuries in children and youth: a systematic review of bicycle skills training interventions
Bibliographic record
Abstract
BACKGROUND: Bicycling is a popular means of recreation and transportation for children; however, it is a leading cause of recreational injury. Bicycle skill development and safety education are important methods of bicycle injury prevention. OBJECTIVE: To determine the effectiveness of bicycle skills training programmes in reducing bicycle-related injuries in children and youth. METHODS: Sixteen databases were systematically searched to include studies involving children less than 19 years of age who participated in interventions that targeted bicycle skills and safety education. Outcome measures included injury, behaviour, knowledge and attitudes. Data extraction included study characteristics, intervention and outcomes. Quality of evidence was assessed using the Downs and Black criteria. RESULTS: Twenty-five studies, including both observational (ie, case-control) and experimental (ie, randomised controlled trials) designs met the inclusion criteria. Overall, there was no statistically significant intervention effect on measures of injury. Eight of 16 studies measuring knowledge reported significant knowledge gains as a result of the intervention. Of 13 studies evaluating behavioural and attitude changes, five reported significant improvement. There was no significant difference in quality index scores between studies that showed an improvement in knowledge or behaviour (61%, 95% CI 49% to 74%) and studies that did not (57%, 95% CI 48% to 66%). CONCLUSIONS: There is a paucity of high-quality research in the area of bicycle skills training programmes. Educational and skills training bicycling programmes may increase knowledge of cycling safety, but this does not seem to translate into a decrease in injury rate, or improved bicycle handling ability and attitudes.
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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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".