Addressing Road Traffic Injuries in Low- and Middle-Income Countries: A Kingdon Policy Analysis
Bibliographic record
Abstract
Background: Road traffic injuries (RTIs) are a leading cause of morbidity and mortality worldwide. Unfortunately, this burden disproportionately affects Low and Middle-Income Countries (LMICs) due to inadequate institutional capacity development for road safety. Despite global initiatives for reducing RTIs, two nations that continue to suffer most are Nepal and Uganda. Objective: To identify the tools necessary to get RTI prevention/road safety on the policy agenda of LMICs. Methods: The Kingdon Multiple Streams Framework is applied to Nepal and Uganda to identify successful and damaging elements to getting RTI prevention/road safety on the policy agenda. Results: Nepal lacks RTI evidence, limiting its ability to define a prominent road safety issue. Accordingly, governmental efforts have been minimal, and the issue is largely being addressed by non-governmental organizations. The introduction of the Decade of Action for Road Safety 2011-2020 functioned as a brief policy window for Nepal, but due to political instability and absence of a policy entrepreneur, the streams have not aligned, and road safety remains a low priority. Akin to Nepal, Uganda’s RTI evidence is insufficient. However, the 2018 Road Safety Performance Review can be understood as strengthening Uganda’s problem definition and opening a policy window. The problem, defined as the worst RTI fatality rate in the African region, converged with existing national and international policy solutions. Some of these solutions are easy to implement, and considering the current favorable political climate as well as the presence of a pivotal policy entrepreneur, efforts are underway to improve Uganda’s road safety. Conclusions: Political stability is primarily needed before any progress can be made for agenda item prioritization. Secondly, the problem must be well-defined as well as feasible and valuable solutions must be available to address the issue. Above all, the three streams, problem, policies and politics must align, and there is greater likelihood of this occurring if a LMIC has a prominent policy entrepreneur.
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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 | 0.033 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".