Evaluating performance of a Lead Road Safety Agency (LRSA) in a low-income country: a case study from Pakistan
Bibliographic record
Abstract
The World Health Organization recommends identifying a Lead Road Safety Agency (LRSA) within the government to coordinate preventive interventions. As LRSAs in developing countries have rarely been evaluated, this case study describes the performance of the LRSA of Pakistan with respect to the World Bank criteria. The designated LRSA, the National Road Safety Secretariat, was put into operation in 2006 and worked for about two years with World Bank funding. The agency had a stand-alone structure headed by an experienced road safety specialist during the first year only and faced difficulty in recruiting other required experts. The LRSA drafted the first National Road Safety Plan, including strategic review of road safety and existing legislation, articulated multisectorial collaboration nationally and provincially, and collected traffic injury data in some districts. Its progress was halted by its dissolution because of funding problems. Currently, two agencies specialising in traffic enforcement and transport research respectively are fulfilling LRSA functions on an ad-hoc basis. Results suggest that sustainability and consistency of LRSAs in developing countries like Pakistan may only be ensured if they are legally protected, inter-ministerial, have permanent funding and are provided with the required expertise through international cooperation, so they can perform their required functions effectively.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".