PERCEPTION OF RISK AND BEHAVIORS ASSOCIATED WITH DRIVING UNDER THE EFFECTS OF ALCOHOL AND MARIJUANA ON UNIVERSITY STUDENTS OF VENEZUELA
Bibliographic record
Abstract
ABSTRACT Objective: to evaluate the relationship between risk perception and the behaviors associated with driving under the influence of drugs. Method: quantitative cross-sectional study. The sample is composed by university students (n=383, average age 21.2 years). To evaluate the behaviors, items from Ontario Student Drug Use and Health were adapted, and two other instruments were used to measure alcohol and marijuana consumption. Results: it indicates a low risk perception when driving under the influence of drugs. There are no differences between the risk perception of being stopped by the police or being penalized for driving under effects of alcohol and/or marijuana among the students whose report the behavior called driving-under-influence and those without such behavior. However, there were differences between the perception of the risk of involvement in a vehicle accident and the behaviors called driving-under-influence, showing that those who report driving under the influence of alcohol and/or marijuana perceive a lower risk of accidents due to the effects of alcohol X2 (1, N=292)=7,999, p=.005 and of both substances X2 (1, N=35)=6.386, p=.012. Likewise, a lower perception of the risk of accidents was found among the subjects who board a vehicle driven by someone who uses marijuana X2 (1, N=67)=15,087, p=.000 and those who do not report being a passenger of a driver under influence; as well as when under the simultaneous effect of alcohol and marijuana X2 (1, N=366)=8,849, p=.003. Conclusion: it is concluded that the development of preventive programs in the university environment, as well as public policies that include the component of education and compliance with legal regulations, is important.
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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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".