Childhood Behavior Problems and Unintentional Injury: A Longitudinal, Population-Based Study
Bibliographic record
Abstract
OBJECTIVE: Although an association between behavior problems and childhood injuries has been established, the majority of studies have been cross-sectional and comorbidity has not been taken into account. The purpose of this study was to prospectively assess the relationship between behavior problems and the risk of unintentional injury in a population-based sample of Canadian children aged 4 to 11 years. METHOD: This prospective cohort study considered data from Cycles 1 and 2 of the Canadian National Longitudinal Survey of Children and Youth. The outcome was injury in Cycle 2 (2 years later). The exposure was the presence of behavior problems in Cycle 1, defined as children with hyperactivity only, aggression only, anxiety only, hyperactivity with aggression, hyperactivity with anxiety, aggression with anxiety, and hyperactivity with aggression and anxiety. All groups were compared to children with no behavior problems. Covariates included child-related, parental, and social-environmental factors. RESULTS: The final weighted longitudinal sample included 2,209,886 children, of which 11.4% were injured in Cycle 2. None of the behavior groups were at significant risk of injury 2 years later. However, children who lived with a single/no parent and children who did not live with a biological parent had a significantly greater risk of injury. After controlling for confounders, children who lived with a single/no parent had more than twice the risk of having an injury. CONCLUSION: We need to look beyond behavior problems, and possibly at family and environmental factors, to reduce the burden of injuries in the Canadian population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".