Assessing a Country's Drink Driving Situation: An Overview of the Method Used in 6 Low- and Middle-Income Countries
Bibliographic record
Abstract
The International Center for Alcohol Policies (ICAP) has developed an international program to reduce drink driving as part of its strategy for Global Actions on Harmful Drinking. The program focuses on capacity building, training, and the implementation, monitoring, and evaluation of regional and local pilot projects in 6 participating low- and middle-income countries. The first step in developing an effective program that addresses specific problems in a region or country is to assess the current drink driving situation. In order to meet this key requirement, ICAP developed a situation assessment approach based largely on the recommendations of Chapter 2 of the good practice manual on drinking and driving produced by the Global Road Safety Partnership (GRSP) under the auspices of the United Nations (UN) Road Safety Collaboration. The aim of the assessment was to provide the foundation for preparing a prioritized and effective suite of projects using the good practice recommended by the GRSP/UN manual. Its output is intended to assist with determining program objectives, design, and evaluation so that the benefits from the investments in drink driving programs can be maximized and data led, focusing on the priorities identified by the assessment. The situation assessment approach was produced as a set of guidelines containing a detailed and structured list of questions. The questions are organized into 6 main groups or elements and they enable a comprehensive and systematic collection of existing information about the extent and nature of the drink driving problem, the strengths and weaknesses of the current prevention practices, and the capacity for improvements. Situation assessments using these guidelines have been completed in the 6 focus countries and the resulting information is now being used for capacity building and developing appropriate and relevant pilot projects, taking into consideration the country's culture with respect to transportation, enforcement, health care, and alcohol consumption.
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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 | 0.025 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 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".