Prevalence of Alcohol Consumption by Medical Students in Northeast Brazil
Bibliographic record
Abstract
Abstract Objective This research aims to verify the prevalence of alcohol consumption by medical students from the first to the twelfth semester at the Federal University of Paraíba (UFPB) - Campus I. Methods This is an analytical, quantitative, and transversal research. A total of 293 medical students participated in the study, divided into two groups: Group 1 with 155 students, from the 1st to the 6th semester, and Group 2 with 138 students, from the 7th to the 12th semester. Two questionnaires were used: a sociodemographic questionnaire with questions about alcohol preference, family alcohol consumption, and psychoactive substance use. The second questionnaire refers to the Alcohol Use Problems Identification Test (AUDIT). Results The prevalence of alcohol consumption was higher for the second group, with 63.23% and 77.54%, respectively. It was noticed that in the last years of the course there is a more favorable context to continue using alcohol, considering the stress experienced and a greater overload in the last semesters of the undergraduation. However, based on AUDIT, there was a predominance of low risk for both groups, with 90.97% and 86.23%, respectively. Conclusions It was concluded that, although there is a predominance of low risk in the consumption scale, there is a concern that permeates the literature of the area and practice regarding the importance of discussing the topic of alcohol consumption with medical students, since they need to understand the multiple risks present in alcohol abuse as students and future health professionals.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".