Novice Drivers’ Exposure to Known Risk Factors During the First 18 Months of Licensure: The Effect of Vehicle Ownership
Bibliographic record
Abstract
OBJECTIVE: Though there is ample research indicating that nighttime, teen passengers, and speeding increase the risk of crash involvement, there is little research about teen drivers' exposure to these known risk factors. Three research questions were assessed in this article: (1) Does exposure to known risk factors change over time? (2) Do teenage drivers experience higher rates of exposure to known risk factors than adult drivers? (3) Do teenage drivers who own a vehicle experience higher rates of exposure to risk factors than those who share a family vehicle? METHODS: Forty-one newly licensed teenage drivers and at least one parent (adult) were recruited at licensure. Driving data were recorded for 18 months. RESULTS: Average vehicle miles traveled (VMT) or average nighttime VMT for teens did not increase over time. Teenagers consistently drove 24 percent of VMT at night, compared with 18 percent for adults. Teenagers drove 62 percent of VMT with no passengers, 29 percent of VMT with one passenger, and less than 10 percent of VMT with multiple passengers. Driving with no passengers increased with driving experience for these teens. Teenage drivers who owned their vehicles, relative to those who shared a vehicle, sped 4 times more frequently overall and more frequently at night and with multiple teen passengers. CONCLUSION: These findings are among the first objective data documenting the nature of teenage driving exposure to known risk factors. The findings provide evidence that vehicle access is related to risk and suggest the potential safety benefit of parental management of novice teenage driving exposure.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".