Effectiveness of child restraints and booster legislation in Israel
Bibliographic record
Abstract
INTRODUCTION: 96 countries in the world have enacted child restraints and booster legislation (CRBL). Yet, findings regarding the effectiveness of CRBLs are mixed. The current study is the first to examine the association between Israel's CRBL, implemented in November 2004, and the traffic injury and fatality rates among children aged 0-9 years. We extend on previous studies by accounting for risk exposure and by comparing populations of children affected by the legislation to those who were not. METHODS: We used an interrupted time series design of kilometre driven-based traffic injury rates for children aged 0-4 years and children aged 5-9 years using childred aged 10-14 years as a comparison group. We estimated the effects of Israel's CRBL using monthly injury and fatality count data from the Israeli Central Bureau of Statistics. The sample includes all child vehicle occupants injured and killed in crashes in Israel between January 2003 and December 2011. RESULTS: Children aged 0-4 years experienced a 5.17% yearly reduction in traffic injury rate (incidence rate ratio (IRR): 0.94(95% CI 0.92 to 0.96); p=0.000), and the injury rate for children aged 5-9 years was associated with a 4.10% yearly reduction (IRR: 0.95(95% CI 0.93 to 0.98); p=0.001). The comprehensive CRBL implemented in Israel was associated with a 6.3% (95% CI -7.2% to5.5%; p=0.001) reduction in traffic injuries and fatalities for children aged 0-9 years. CONCLUSION: This is the first study comparing traffic injury rates per kilometre driven for motor vehicle-occupant children before and after the implementation of the CRBL in Israel.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".