Causes and Health-Related Outcomes of Road Traffic Crashes in the United Arab Emirates: Panel Data Analysis of Traffic Fines
Bibliographic record
Abstract
AIMS: To investigate the main causes and consequences of road traffic injuries (RTIs) in the United Arab Emirates (UAE) and the effect of traffic fines on these injuries. METHODS: This cross-sectional study analysed road traffic deaths and injuries and panel data of monthly traffic accident fines from 2012 to 2016 in the UAE. A fixed-effects (FE) model was used to determine the factors associated with RTIs over time. The FE model removes the effect of time-invariant aspects from the independent variables, thus assessing the net impact of the independent variables on the outcome variable. The independent variables were dangerous driving behaviors defined as a sudden turn, neglect and lack of attention, and excess speed. The outcome variables were the total number of road traffic deaths and injuries. RESULTS: Road traffic deaths, severe injuries, and moderate injuries remained constant from 2012 to 2016 but mild injuries decreased starting 2013. Human errors such as a sudden turn (22%), excess speed (12%) and lack of road user appreciation (12%) play a central role in road traffic injuries. The number of fines issued to drivers (66% for speeding) increased by 50% in the five year period. CONCLUSION: Road traffic injuries in the UAE remained stable from 2012 to 2016 despite an increase in traffic fines issued to drivers. Human errors continue to be a major cause of these injuries. Additional enforcement strategies are needed to address this health burden.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".