Comparison of fatalities from work related motor vehicle traffic incidents in Australia, New Zealand, and the United States
Bibliographic record
Abstract
OBJECTIVE: To compare the extent and characteristics of motor vehicle traffic incidents on public roads resulting in fatal occupational injuries in Australia, New Zealand (NZ), and the United States (US). DESIGN AND SETTING: Information came from separate data sources in Australia (1989--92), NZ (1985--98), and the US (1989--92). METHODS: Using data systems based on vital records, distributions and rates of fatal injuries resulting from motor vehicle traffic incidents were compared for the three countries. Common inclusion criteria and occupation and industry classifications were used to maximize comparability. RESULTS: Motor vehicle traffic incident related deaths accounted for 16% (NZ), 22% (US), and 31% (Australia) of all work related deaths during the years covered by the studies. Australia had a considerably higher crude rate (1.69 deaths/100,000 person years; 95% confidence interval (95% CI) 1.54 to 1.83) compared with both NZ (0.99; 95% CI 0.85 to 1.12) and the US (0.92; 95% CI 0.89 to 0.94). Industry distribution differences accounted for only a small proportion of this variation in rates. Case selection issues may have accounted for some of the remainder, particularly in NZ. In all three countries, male workers, older workers, and truck drivers were at higher risk. CONCLUSIONS: Motor vehicle traffic incidents are an important cause of work related death of workers in Australia, NZ, and the US. The absolute rates appear to differ between the three countries, but most of the incident characteristics were similar. Lack of detailed data and inconsistencies between the data sets limit the extent to which more in-depth comparisons could be made.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".