Driving under the influence of alcohol in Cali, Colombia: prevalence and consumption patterns, 2013
Bibliographic record
Abstract
This study's goal was to establish the prevalence of driving under the influence of alcohol (DUI) and alcohol consumption patterns among drivers in Cali, Colombia, in 2013. A cross-sectional study based on a roadside survey using a stratified and multi-stage sampling design was developed. Thirty-two sites were chosen randomly for the selection of drivers who were then tested for blood alcohol concentration (BAC) and asked to participate in the survey. The prevalence of DUI was 0.88% (95% confidence intervals [95% CI] 0.26%-1.49%) with a lower prevalence when BAC was increasing. In addition, a higher prevalence was found during non-typical checkpoint hours (1.28, 95% CI -0.001%-0.03%). The overall prevalence is considered high, given the low alcohol consumption and vehicles per capita. Prevention measures are needed to reduce DUI during non-typical checkpoints and ongoing studies are required to monitor the trends and enable the assessment of interventions.
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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".