Stages of development and injury patterns in the early years: a population-based analysis
Bibliographic record
Abstract
BACKGROUND: In Canada, there are many formal public health programs under development that aim to prevent injuries in the early years (e.g. 0-6). There are paradoxically no population-based studies that have examined patterns of injury by developmental stage among these young children. This represents a gap in the Canadian biomedical literature. The current population-based analysis explores external causes and consequences of injuries experienced by young children who present to the emergency department for assessment and treatment. This provides objective evidence about prevention priorities to be considered in anticipatory counseling and public health planning. METHODS: Four complete years of data (1999-2002; n = 5876 cases) were reviewed from the Kingston sites of the Canadian Hospitals Injury Reporting and Prevention Program (CHIRPP), an ongoing injury surveillance initiative. Epidemiological analyses were used to characterize injury patterns within and across age groups (0-6 years) that corresponded to normative developmental stages. RESULTS: The average annual rate of emergency department-attended childhood injury was 107 per 1000 (95% CI 91-123), with boys experiencing higher annual rates of injury than girls (122 vs. 91 per 1000; p < 0.05). External causes of injury changed substantially by developmental stage. This lead to the identification of four prevention priorities surrounding 1) the optimization of supervision; 2) limiting access to hazards; 3) protection from heights; and 4) anticipation of risks. CONCLUSION: This population-based injury surveillance analysis provides a strong evidence-base to inform and enhance anticipatory counseling and other public health efforts aimed at the prevention of childhood injury during the early years.
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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 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".