Evaluating an in-school injury prevention programme's effect on children's helmet wearing habits
Bibliographic record
Abstract
PRIMARY OBJECTIVE: To evaluate the effectiveness of the Bikes, Blades and Boards (BB&B) programme. It was hypothesized that children who participated in the BB&B programme would demonstrate greater knowledge of how to wear their helmets safely than a control group who did not participate in the programme and retain their skills when assessed 1 year later. RESEARCH DESIGN: Single blind cluster randomized design. METHODS AND PROCEDURES: Twelve classes of grade 2 students (n = 162) participated; six classes were assigned to an experimental or control group. A blinded research assistant, taking 3-5 minutes per child, completed the Helmet Checklist with each group on two occasions and scores of the experimental group (post-BB&B programme) were compared to the control group. The experimental group was reassessed using the Helmet Checklist, 1 year later. EXPERIMENTAL INTERVENTIONS: The BB&B programme consisted of a presentation, bicycle helmet checklist, demonstration and individual practice and feedback. MAIN OUTCOMES AND RESULTS: Children in the experimental group showed a better knowledge of how to wear their helmets safely compared to the control group (F = 51.84, CI = 9.11-9.71) and retained this knowledge 1 year after participating in the BB&B programme. CONCLUSIONS: The BB&B programme is effective in teaching grade 2 children how to wear their helmets correctly, which is knowledge they retain for at least 1 year.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".