Road traffic fatalities in rural and remote Australia from 2006 to 2017: The need for targeted action
Bibliographic record
Abstract
OBJECTIVE: To explore rural motor vehicle collision (MVC) fatalities by trends over time, mode of transport, age, state, sex, and Aboriginal and Torres Strait Islander status. DESIGN: A retrospective total population-based time series was conducted using the Australian Bureau of Statistics (ABS) death registration data. SETTING: All statistical local area (SLA) within Australia from 2006 to 2017. PARTICIPANTS: Australian residents whose deaths were registered with the ABS between 01 January 2006 and 31 December 2017 where the underlying cause of death was related to unintentional transport accidents. MAIN OUTCOME MEASURES: Fatality rates were determined using population data collected from the 2006, 2011 and 2016 census. Trends over time by rurality were analysed by financial year. Rates of transport deaths by vehicle type were determined by rurality. Risk ratios were calculated to compare demographic groups based on sex, Aboriginal and Torres Strait Islander status and age. A 3-year scorecard was organised by state and rurality using 99.7% confidence intervals. RESULTS: Motor vehicle collision fatalities increase with increasing remoteness. Females, children from 0 to 14 years, pedestrians, and Aboriginal and Torres Strait Islander peoples are at a significantly higher risk of fatal MVCs than their respective metropolitan counterparts. The 3-year scorecard indicates that road fatality rates in the NT, WA, and all rural and remote areas required immediate attention and targeted action. CONCLUSIONS: There is a need for investment in MVC fatality prevention in rural Australia from inner regional to remote areas in order to meet the road safety targets established by the National Road Safety Strategy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".