Bibliographic record
Abstract
Background and aims: Motor vehicle crashes(MVCs) are the most common cause of death in the United States for adolescents. Since 1998, Massachusetts has implemented a Graduated Driver Licensing (GDL) system requiring teenagers to gain experience under conditions of low crash risk before gaining full privileges. Aims: To evaluate the impact of changes to strengthen Massachusetts’ GDL law on MVCs in 16-18 year olds, and to assess whether these effects persist into young adulthood. Methods: Massachusetts MVC rates were analyzed for drivers aged 16-24 years during two time periods: 2002-2006, before the GDL law was strengthened, and 2007(fourth quarter)-2010, after implementation. Piecewise regression was performed to test whether the rate of change in crash rates by quarter was different for the two time periods, after controlling for age and sex of the driver. Results: MVC rates for drivers aged 16-24 declined by 0.5% per quarter prior to implementation of changes to GDL in 2007, with an accelerated drop of 3.7% per quarter after 2007, a difference of 3.5% (p< 0.001). Results: showed a significant difference in hour of crash and age group of driver. For those 16-18, the percentage of crashes occurring between 12-5am dropped from 7.2% before 2007 to 6.3% after 2007 (p<0.001). For 19-24-year olds there was a similar decrease during those hours from 12.1% before 2007 to 11.2% after 2007. Conclusions: Changes to the Massachusetts GDL law in 2007 to enhance driver education, as well as to enact stricter penalties for violations have contributed to a significant decline in crash rates for Massachusetts youth. These effects persist into young adulthood.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.801 | 0.693 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".