Explaining Chile's Traffic Fatality and Injury Reduction for 2000–2012
Bibliographic record
Abstract
OBJECTIVES: The objective of the current study is to determine the contribution of Chile's 2005 traffic law reform, police enforcement, and road investment infrastructure to the reduction of traffic fatalities and severe injuries from 2000 to 2012. METHODS: Analyses based on structural equation models were carried out using a unique database merging aggregate administrative data from several Chilean public institutions. The sample was balanced (13 regions, over 13 years; N=169). Dependent variables were rates of traffic fatality (total, drivers, passengers, and pedestrians), severe injuries, and total number of crashes per vehicle fleet. Independent variables were (1) traffic law reform, (2) police enforcement, and (3) road infrastructure investment. Oil prices, alcohol consumption, proportion of male population 15-24 years old, unemployment, years' effects and regions' effects, and lagged dependent variables were entered as control variables. RESULTS: Empirical estimates from the structural equation models suggest that the enactment of the traffic law reform is significantly associated with a 7% reduction of pedestrian fatalities. This association is entirely mediated by the positive association the law had with increasing police enforcement and reducing alcohol consumption. In turn, police enforcement is significantly associated with a direct decrease in total fatalities, driver fatalities, passenger fatalities, and pedestrian fatalities by 17%, 18%, 8%, and 60%, respectively. Finally, road infrastructure investment is significantly associated with a direct reduction of 11% in pedestrian fatalities, and the number of total crashes significantly mediates the effect of road infrastructure investment on the reduction of severe injuries. Tests of sensitivity indicate these effects and their statistical significance did not vary substantively with alternative model specifications. CONCLUSIONS: Results suggest that traffic law reform, police enforcement, and road infrastructure investment have complex interwoven effects that can reduce both traffic fatalities and severe injuries. Though traffic reforms are ultimately designed to change road user behaviors at large, it is also important to acknowledge that legislative changes may require institutional changes--that is, intensification of police enforcement--and be supported by road infrastructure investment, in order to effectively decrease traffic fatalities and injuries. Furthermore, depending on how road safety measures are designed, coordinated, and implemented, their effects on different types of road users vary. The case of Chile illustrates how the diffusion of road safety practices globally promoted by the World Health Organization and World Bank, particularly in 2004, can be an important influence to enhance national road safety practices.
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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 | 0.000 |
| 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".