The introduction of booster seat legislation in Canada: is it working?
Bibliographic record
Abstract
Background Booster seat legislation may decrease the burden of motor vehicle occupant injury in children 4–9 years old. Methods We examined rates of hospitalisations for motor vehicle (MV) occupant-related injuries, seatbelt-related injuries and MV occupant-related fatalities in Canadian children, comparing provinces with legislation (Ontario 2005, Quebec 2003) to those without. Hospital discharge data was obtained for 1994–2005 from the Discharge Abstract Database (DAD) and the Hospital Morbidity Database (HMDB). Death data was obtained from the Traffic Accident Information Database (TRAID). Population based injury and death rate ratios were calculated and changes from 1994 to 2005 were determined. Results There were a total of 3920 MV occupant-related hospitalisations and 358 MV occupant-related fatalities in Canadian children aged 4–9. In 2005, after legislation, Ontario had a significantly lower rate of hospitalisation for MV occupant-related injuries (RR=0.49 (95% CI 0.35 to 0.63)). Rates of hospitalisations and fatalities declined from 1994 to 2005 across all provinces regardless of legislation status. Ontario had significantly higher rate reductions and a consistently lower annual rate of MV occupant-related hospitalisations than other provinces. Injury and death rates were higher in Quebec and increased after legislation was introduced. Discussion Results indicate heterogeneity between the two provinces with booster seat legislation. The simple presence of booster seat legislation is not sufficient to decrease the burden of motor vehicle occupant injuries.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".