An Evaluation of Graduated Driver Licensing Effects on Fatal Crash Involvements of Young Drivers in the United States
Bibliographic record
Abstract
OBJECTIVE: Graduated driver licensing (GDL) systems are designed to reduce the high crash risk of young novice drivers. Almost all states in the United States have some form of a 3-phase GDL system with various restrictions in the intermediate phase. Studies of the effects of GDL in various states show significant reductions in fatal crash involvements of 16- and 17-year-old drivers; however, only a few national studies of GDL effects have been published. The objective of this national panel study was to evaluate the effect of GDL laws on the fatal crash involvements of novice drivers while controlling for possible confounding factors not accounted for in prior studies. METHODS: The Fatality Analysis Reporting System (FARS) was used to examine 16- and 17-year-old driver involvement in fatal crashes (where GDL laws are applied) relative to 2 young driver age groups (19-20, 21-25) where GDL would not be expected to have an effect. Dates when various GDL laws were adopted in the states between 1990 and 2007 were coded from a variety of sources. Covariates in the longitudinal panel regression analyses conducted included 4 laws that could have an effect on 16- and 17-year-old drivers: primary enforcement seat belt laws, zero-tolerance (ZT) alcohol laws for drivers younger than age 21, lowering the blood alcohol concentration limit for driving to 0.08, and so-called use and lose laws where drivers aged 20 and younger lose their licenses for underage drinking violations. RESULTS: The adoption of a GDL law of average strength was associated with a significant decrease in fatal crash involvements of 16- and 17-year-old drivers relative to fatal crash involvements of one of the 2 comparison groups. GDL laws rated as "good" showed stronger relationships to fatal crash reductions, and laws rated as "less than good" showed no reductions in crash involvements relative to the older driver comparison groups. CONCLUSIONS: States that adopt a basic GDL law can expect a decrease of 8 to 14 percent in the proportion of 16- and 17-year-old drivers involved in fatal crashes (relative to 21- to 25-year-old drivers), depending upon their other existing laws that affect novice drivers, such as those used in these analyses. This finding is consistent with recent national studies that used different outcome measures and covariates. The results of this study provide additional support for states to adopt, maintain, and upgrade GDL systems to reduce youthful traffic crash fatalities.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".