Recent developments in behavioural research and prevention of childhood injuries
Bibliographic record
Abstract
In most developed nations, unintentional injuries are the leading cause of death for children. In this symposium, an international panel of speakers will present behavioural research designed to promote safe environments and equitable communities for children by modifying environments and/or care giver behaviours to reduce children's exposure to injury risks. Denise Kendrick (UK) will overview evidence relating the effectiveness of home safety education and the provision of safety equipment to care giver safety practices and reductions in children's risk of injury, including mention of factors associated with effectiveness of these interventions. Working at the community level, Andrea Gielen (USA) will present data from intervention research involving partnership of university and community groups, with the goal of preventing home injuries to children in low income communities by promoting the adoption of evidence-based home-safety practices. Barbara Morrongiello (Canada) will present research addressing care giver supervision as an injury risk-reducing strategy. She will present results from an randomised controlled trial designed to increase parental home supervision of young children by targeting pertinent beliefs and attitudes regarding childhood injuries, children's behavioural development and care giver supervision. Finally, David Schwebel (USA) will present research regarding the use of simulated drowning audits to improve lifeguard surveillance at community swimming pools in both low and upper income neighbourhoods. Elizabeth Towner (UK) will serve as Discussant. In that role, she will provide an integrative framework for behavioural approaches in child injury prevention and identify future research needs in the area.
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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 | 0.026 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".