INNOVATIONS IN CHILD INJURY PREVENTION: EVIDENCE-BASED STRATEGIES THAT ADDRESS FIRE SAFETY FOR YOUNG CHILDREN AND PLAYGROUND SAFETY FOR OLDER CHILDREN
Bibliographic record
Abstract
Background Unintentional injury is a leading cause of child mortality, and many of these are preventable events. Innovative approaches that change safety attitudes and/or behaviours are recognised as an important aspect of comprehensive injury prevention programming, especially because some injuries cannot be addressed by implementing modifications to the environment and/or product design. Purpose To discuss behavioural research evaluating two child injury prevention strategies that have proven successful: (1) an innovative computer game (The Great Escape) that does not require reading skills and aims to improve young children's (2–5 years) fire safety knowledge and behaviours, and (2) theCool 2 Be SafeProgram, which incorporates four strategies that have been shown to reduce children's risky playground behaviours, into one evidence-based programme that aims to reduce fall-risk behaviours on playgrounds among school-age children (6–12 years)andcan successfully be delivered by laypersons in community organisations (eg, schools, daycares) using the training materials provided. Methods Two studies will be presented, each using experimental pre–post designs with control groups. Measures assess child knowledge, attitudes, and behaviours. Results Both interventions produced significant results, including: (1) increases in young child fire safety knowledge (p<0.05) and behaviours (p<0.05) and (2) positive changes in school-age children's attitudes and risky playground behaviours (p<0.05). Significance Developing innovative approaches to attitude/behaviour change that are based on rigorous scientific evaluation is essential to advancing the field of child injury prevention. These two programmes are unique, address important injury issues, and hold promise for reducing the burden of some types of childhood injuries.
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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".