The impact of child safety restraint legislation on child injuries in police-reported motor vehicle collisions in British Columbia: An interrupted time series analysis
Bibliographic record
Abstract
BACKGROUND/OBJECTIVE: Motor vehicle collisions (MVCs) remain a leading cause of death and serious injury in Canadian children. In July 2008, British Columbia introduced child safety seat legislation that aimed to reduce the number of children killed or injured in MVCs. This legislation upgraded previous child seat legislation (introduced in 1985) and affected children zero to three and those four to eight years of age. The objective of the present study was to evaluate the effectiveness of this legislation. METHODS: Deidentified police reports for all MVCs involving zero- to 14-year-olds (2000 to 2012) were used to compare injury rates, booster seat use, and seating position among children before and after booster seat laws. An interrupted time series design was used to estimate the effect of the new law on injuries among children zero to three and four to eight years of age. Estimates were adjusted using children nine to 14 years of age as controls. RESULTS: The booster seat law was associated with a 10.8% (95% CI 2.7% to 18.9%) reduction in the monthly rate of injuries in four- to eight-year-old children (P=0.01). This was equivalent to a decrease of 14.3 injuries per 1,000,000 children. Similarly, the monthly injury rate among children zero to three years of age decreased by 13.0% (95% CI 1.5% to 24.6% [9.8 injuries per 1,000,000]; P=0.03). CONCLUSION: The results provide evidence that British Columbia's new child safety restraint law was associated with fewer injuries among children covered by the new laws.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".