Seat belt use and alcohol-impaired driving: Behaviour and attitudes in Australia, Canada, the United Kingdom and the United States
Bibliographic record
Abstract
The highway safety problem has similar dimensions in all motorized societies. Two factors that have contributed strongly to motor injuries worldwide are alcohol-impaired driving and failure to use seat belts. While all countries have made substantial efforts to decrease alcohol-impaired driving and increase belt use rates, they have taken somewhat different paths in addressing these common problems, and some have done better than others. Countries such as Australia have achieved remarkable gains in both areas, while other countries have lagged. The United States is a laggard particularly in the belt use area. It may be possible for less successful countries to learn from others how to make greater progress toward their goals. To investigate this possibility, a telephone survey of drivers in four countries was undertaken. This survey obtained information on drivers’ self-reported behavior regarding seat belt use and drinking and driving as well as their attitudes and perceptions about these behaviors and the laws governing them. There are two separate existing publications that present and discuss the survey results [1,2]. This paper summarizes and comments further on information in the prior reports.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".