Incidence of Binge Drinking in a Cohort of University Students of the South-East Region of Brazil, 2010-2011
Bibliographic record
Abstract
Objectives: The aim of the present study was to evaluate the prevalence and incidence of binge and at-risk alcohol consumption among new-entrant students in a public university in the South-East Region of Brazil. Methods: Longitudinal study undertaken with a random sample of undergraduates (N=1,168) in the first semesters of 2010 (n=256) and 2011 (n=183). In order to evaluate drinking patterns, participants were classified as abstainers, light, moderate, binge or heavy binge drinkers. The Alcohol Use Disorders Identification Test (AUDIT) questionnaire score was used to define the risk categories for consumption: low risk, risk, harmful use and probable dependence. Statistical analysis was undertaken using Stata software, version 11.0. Results: Amongst the 256 students evaluated, 51.6% were women and 64.5% were aged ≥19 years. The prevalence of consumption of alcoholic beverages was 75.8%. The average age of onset of alcohol consumption was 15.7±1.9 years. The incidence of binge + heavy drinkers was 2.6/100 persons per year, with vulnerability shown in individuals of male sex and of age 19 years or more. The incidence of risk level drinkers was 2.0/100 persons per year and greater for individuals of male sex and younger than 19 years. Conclusion: The students of this institution are at high risk for problems associated with alcohol use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".