A RCT Testing If a Storybook Can Teach Children About Home Safety
Bibliographic record
Abstract
OBJECTIVE: Unintentional injuries are the leading cause of death for children under 19 years of age. For preschoolers, many injuries occur in the home. Addressing this issue, this study assessed if a storybook about home safety could be effective to increase preschoolers' safety knowledge and reduce their injury-risk behaviors. METHODS: Applying a randomized controlled trial design, normally developing English speaking preschool children (3.5-5.5 years) in Southwestern Ontario Canada were randomly assigned to the control condition (a storybook about healthy eating, N = 30) or the intervention condition (a storybook about home hazards, N = 29). They read the assigned storybook with their mother for 4 weeks; time spent reading was tracked, and fidelity checks based on home visits were implemented. RESULTS: Comparing postintervention knowledge, understanding score, and risk behaviors across groups revealed that children who received the intervention were able to identify more hazards, provide more comprehensive safety explanations, and demonstrate fewer risky behaviors compared with children in the control group (ηp2 = 0.13, 0.19, and 0.51, respectively), who showed no significant changes over time in safety knowledge, understanding, or risk behaviors. Compliance with reading the safety book and fidelity in how they did so were very good. CONCLUSIONS: A storybook can be an effective resource for educating young children about home safety and reducing their hazard-directed risk behaviors.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".