Risk Perception of Traffic Accidents Due to Alcohol and Marijuana Use in Mexican College Students
Bibliographic record
Abstract
Driving under the influence (DUI) of alcohol and other drugs is a common occurrence in Western societies. Alcohol consumption is related to 15% of fatal injuries in traffic accidents worldwide, with those DUI of alcohol being up to 18 times more likely to be involved in a fatal accident. Evidence for DUI of alcohol or marijuana among the college population in Mexico is scarce. This research estimates the proportion of use of alcohol and marijuana, describes the risk perception of DUI, and evaluates the relationship between risk perception and DUI behaviors in a sample of Mexican college students aged 18 to 29. The study was cross-sectional with a non-probabilistic sample. Risk perception of suffering traffic accidents when DUI or riding with someone DUI of alcohol, marijuana, or both, was high, unlike the risk perception of being detected or sanctioned for a DUI of marijuana. The study provided valuable information on the risk perception of engaging in behaviors related to DUI of alcohol and/or marijuana. It is necessary to undertake research on the subject with probabilistic and representative samples of this population of Mexico.
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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 | 0.000 | 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".