DRUG CONSUMPTION, KNOWLEDGE ON THE CONSEQUENCES OF CONSUMPTION AND ACADEMIC PERFORMANCE AMONG COLLEGE STUDENTS IN SAN SALVADOR, EL SALVADOR
Bibliographic record
Abstract
ABSTRACT Objective: determine the relationship among drug consumption, knowledge on the consequences of consumption and academic performance, for alcohol cocaine and marijuana, among undergraduate students of social sciences and health of San Salvador, El Salvador. Method: the used method was a cross-sectional survey, with a convenience sample of 250 university students. A modified version of the combination of two instruments was applied evaluating the variables for the knowledge on the consequences, pursuing the knowledge of a student about the adverse effects of the biological, psychological and social categories related to consumption of the drugs under study. Drug consumption was evaluated by consulting the student whether or not they used drugs at any time or in the last 3 months. Academic performance was evaluated by consulting students on the average in which they are applied on a scale of 1 to 10. Results: the results showed that 88.1% of the survey participants have a broad knowledge on the consequences of consuming alcoholic beverages; 45.5% on the consequences of marijuana use and 55.7% know the consequences of cocaine consumption. While 28.4% have consumed alcohol in the last year, 6.5% have consumed marijuana and 1.7% cocaine. The relationship of alcohol consumption with the knowledge on each of the consequences reflected a very low influence, while the larger is the knowledge obtained from these consequences caused by the use of the drugs under study, the lower is the consumption. Conclusion: the use of alcohol, cocaine and marijuana is not related to academic performance, indicating very low positive and negative correlations according to each case.
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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 | 0.000 | 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) | 0.002 | 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".