Assessing the impact of road safety agencies and health systems in traffic outcomes across 146 countries, 1994–2012
Bibliographic record
Abstract
BACKGROUND: Road safety policies (RSPs) have emerged worldwide. Yet, while an important group of RSPs have been regarded as necessary to reduce traffic crashes and their consequences, the impact of others remain inconclusive. To advance knowledge on this debate, this article focuses on the potential effects of two RSPs: i) road safety agencies (RSAs) and ii) health systems (HS). METHODS AND DATA SOURCES: Cross-sectional longitudinal data corresponding to 146 countries from 1994 to 2012 are analyzed using regression models to account for the endogeneity of RSA formation, including instrumental variable and fixed effects designs. A global dataset compiling information from multiple sources, including the World Bank, and the World Health Organization is built. RESULTS: RSAs are associated with a decrease of traffic injuries in the long-term. This trend is observed in Organisation for Economic Co-operation and Development (OECD) countries only. Potential data reporting differences between countries could not be accounted for, and therefore it is unclear whether the observation for non-OECD countries is due to an actual difference or due to these reporting differences. HSs decrease traffic fatalities by 5% (95% Confidence interval (CI) 3% to 7%). Across (OECD) countries, HS is not associated with traffic injury variation. CONCLUSION: While some authors have theorised that RSA institutions may fail to reduce either traffic injuries or fatalities, our work however captured a long-term effect in RSAs performance when targeting traffic injury outcomes. That well-developed HSs have been effective in decreasing traffic fatalities, and ineffective in decreasing injuries, is consistent with the overall function that this type of policies fulfils. Results call for revisiting the specific mechanisms which explain why RSAs and HSs seem effective in decreasing different traffic outcomes.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".