PERCEPTION OF RISK AND DRIVING UNDER THE EFFECTS OF ALCOHOL AND MARIJUANA ON UNIVERSITY STUDENTS IN A MULTICENTER STUDY: COLOMBIA
Bibliographic record
Abstract
ABSTRACT Objective: analyze the relationship between the perception of risk and the behavior of driving under the influence of alcohol or marijuana or getting into a vehicle driven by someone under the effects of these substances in order to identify risk factors and protective factors. Method: multicenter study cross sectional survey with students from a University in Colombia (n = 493) completed a survey prepared during the International Program of Training in Research for Health Professionals and Related Areas to Study the Drug Phenomenon in Latin America and the Caribbean. Results: an inverse relationship was observed between each of the three levels of risk perception: detection (p<.001), punishment (p<.05) and harm (p<.001), and driving behaviors with alcohol. This same type of relationship is observed with marijuana in terms of perceived risk of being involved in an accident (p<.05). However, regarding to marijuana, there is not enough evidence of an association with the perceived risk of being arrested or punished. The results show that there is an inverse relationship between what the students' relatives and friends think and do and the perception of risk of being arrested (p<.001), punished (p<.001) or of being involved in an accident (p<.001) for driving under the influence of alcohol and marijuana at the same time. Conclusion: the results suggest that there are risk factors and protective factors that can be intervened to prevent injuries or fatal events associated with driving under the influence of alcohol or marijuana.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".