RISK PERCEPTION AND DRIVING A MOTOR VEHICLE UNDER THE INFLUENCE OF CANNABIS: A STUDY WITH COLLEGE STUDENTS OF A PRIVATE INSTITUTION
Bibliographic record
Abstract
ABSTRACT Objective: to analyze the relationship between risk perception and behaviors related to driving a motor vehicle under the influence of cannabis. Method: The research was carried out through a cross-sectional survey. 382 undergraduate students between the ages of 17 and 29 were interviewed at a private higher educational institution in the Federal District, Brazil. Descriptive and inferential statistics (cross tabulations and chi-square) were used to analyze the data. Results: they indicate that more than 1/3 of the participants used cannabis in the past 12 months, and 36.4% reported problematic use. It was possible to establish a relationship between the behaviors of perception of risk and driving a motor vehicle under the influence of cannabis: 1) the perception of being sanctioned as a driver and driving a motor vehicle under the influence of cannabis (χ2(1) = 3.96, p=≤0); 2) to perceive damages as driver and driving a motor vehicle under the influence of cannabis (χ2(1)=3.96, p = ≤05); 3) perception of damages as passenger and driving a motor vehicle under the influence of cannabis (χ2(1)=3.96, p=≤5.0). Conclusion: damages caused by cannabis are underestimated by university students, since they have a very low risk perception, especially when compared to alcohol. In Brazil, there is also a lack of regulation and sanctions with respect to driving a motor vehicle under the influence of cannabis, which may contribute to an important risk among this population.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".