Impact of the 1994 Alcohol Production and Sales Deregulation Policy on Traffic Crashes and Fatalities in Japan
Bibliographic record
Abstract
INTRODUCTION: . Many studies have demonstrated a strong relationship between alcohol availability and traffic crashes involving alcohol-impaired drivers. The present analysis focuses on the evaluation of the impact of alcohol availability on the Japanese population by comparing fatal and nonfatal motor vehicle crash rates before and after implementation of the alcohol deregulation policy in 1994. Participants and method. Poisson regression with robust standard error was used to model the before-to-after change in incidence rate ratios (IRRs) in the population. To control for potential confounders, per capita alcohol consumption, unemployment rate, and vehicle miles travelled (VMT) were also added to the model. The exponents of the fitted coefficients are equivalent to the IRRs. RESULTS: . Implementation of the policy deregulating alcohol sales and production did not appear to increase traffic fatalities and other traffic crashes in Japan. In the overall study results, nighttime fatalities were reduced statistically significantly by 6% since the implementation of the alcohol deregulation policy in 1994. Discussion. Contrary to previous research, the findings of this study demonstrated lower rates of fatalities and higher compliance with alcohol-related driving legislation. Further well-designed, nonaligned studies on alcohol availability and traffic fatalities in other countries are urgently needed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".