Child Injury at home: exploring a connection between household rules about safety, sibling harm and child injury
Bibliographic record
Abstract
Child injury is the leading cause of child mortality and morbidity in Canada. Several factors that increase a child's risk of injury have been identified, including but not limited to: age, gender, type of activity and type of supervision. The present study focused on adult supervision at home. When parents are asked about commonly used injury prevention strategies, they identify teaching as a preferred tactic, yet few parents actually rely on this strategy. In addition, little is known about the role siblings play in child injury in the home. The goal of the present poster was to explore whether children's knowledge of rules pertaining to household safety and sibling harm was associated with reduced injury risk. We also investigated whether children's knowledge of household safety rules was related to their parents knowledge of these rules, and if this association was related to child injury risk. Eighty-five Canadian families with at least one child aged 6–10 years and one sibling were visited at home. Both children and one parent were interviewed. Preliminary results indicated that children endorsed 68% of household safety rules as present in their home, and that their knowledge of safety rules was significantly related to fewer minor injuries. In addition, in 66% of families children identified rules restricting or forbidding sibling harm and in these families, children experienced significantly fewer minor injuries. Directions for future research and family-based injury prevention strategies were discussed.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".