Maternal reports of child injuries in Canada: trends and patterns by age and gender
Bibliographic record
Abstract
OBJECTIVES: This study examines gender and age differences in maternal reports of injuries in a cross sectional group of children aged 0-11 years. The cause, nature, body part injured, and location of injury are explored, as are the associations with family socioeconomic indicators and associations with limitations in activities. METHODS: Data for 22831 children and their families come from cycle 1 of the Canadian National Longitudinal Survey of Children and Youth collected in 1995. Descriptive analyses and chi2 tests for trends are used to examine injury variations by child gender and age. Logistic regressions are used to examine the relationship between socioeconomic indicators and injury and the associations between injury and limitations in activities. RESULTS: Consistent with findings from hospital data, boys experience more injuries than girls, and injuries increase with child age. Falls are the most common sources of maternally reported injuries, followed by scalds/poisonings for young children and sports injuries for school aged children. The majority of injuries occur in or around the home for young children, but at school for older children. For maternal reports of childhood injuries, single marital status is a risk factor for boys. CONCLUSIONS: Maternally reported injuries occur in 10% of Canadian children and many of these are associated with limitations in activities. Preventative strategies should take both child age and gender into consideration.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".