QUANTITY AND PREVALENCE WITH GENDER COMPARISON FOR ALCOHOL CONSUMPTION AMONG COLLEGE STUDENTS
Bibliographic record
Abstract
Abstract: Introduction: Alcoholism is the recent trend among college students. Various studies conducted in India as well as in the West show clear indications of increasing prevalence of alcohol consumption among youngsters. Studies conducted abroad also revealed that the gap between males and females consuming alcohol is narrowing Objective: Our study was aimed at measuring the prevalence of alcohol consumption among college students and analyzing its variation with respect to gender. Materials and Methods: A survey was carried out in the colleges under NITTE University. Students present at the day and time of visit were covered. A questionnaire was prepared comprising of a set of 14 questions covering the main objective of the survey. The students were asked to fill the questionnaire irrespective of their gender or whether they consume alcohol or not. Results: We have conducted our survey in 1150 college students.713 females and 337 males participated in the survey. Out of 1150 students, 304(26.4%) consumes alcohol.167 (54.9%) were males and 137 (45.1%) were females. Most of them were social drinkers . Majority of them consumed 2-3 drinks in one sitting. Tendency of binge drinking is high. Conclusions: The prevalence of alcohol consumption comes up to a quarter of the total population surveyed most of them being social drinkers with males outnumbering females but with a visible reduction in the observed gap.. A survey was conducted among students of 3 colleges to acquire the necessary data which were analyzed to form the conclusions.
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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 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.006 | 0.001 |
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".