Injury prevention in child death review: child pedestrian fatalities
Bibliographic record
Abstract
OBJECTIVE: This article describes the epidemiology of child pedestrian fatalities in British Columbia using data generated by the province's Child Death Review Unit, to demonstrate the unique capacity of child death review to provide an ecological understanding of child mortality and catalyse evidence based, multi-level prevention strategies. METHODS: All child pedestrian fatalities in British Columbia from 1 January 1 2003 to 31 December 2008 were reviewed. Data on demographics, circumstance of injury, and risk factors related to the child, driver, vehicle, and physical environment were extracted. Frequency of sociodemographic variables and modifiable risk factors were calculated, followed by statistical comparisons against the general population for Aboriginal ancestry, gender, ethnicity, income assistance and driver violations using z and t tests. RESULTS: Analysis of child pedestrian fatalities (n=33) found a significant overrepresentation of Aboriginal children (p=0.06), males (p<0.01), and children within low income families (p<0.01). The majority of incidents occurred in residential areas (51.5%), with a speed limit of 50 kph or higher (85.7%). Risky pedestrian behaviour was a factor in 56.7% of cases, and 33% of children under 10 years of age were not under active supervision. Drivers had significantly more driving violations than the comparison population (p<0.01). CONCLUSION: Child pedestrian fatalities are highly preventable through the modification of behavioural, social, and environmental risk factors. This paper illustrates the ability of child death review to generate an ecological understanding of injury epidemiology not otherwise available and advance policy and programme interventions designed to reduce preventable child mortality.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".