Mitigating the risk of alcohol use among university students
Bibliographic record
Abstract
Background: The rising prevalence of alcohol use among youths in low resource settings is a major public health issue of concern, especially as alcohol use remains a leading contributor to deaths and disability globally. This study aimed to evaluate the effects of screening and brief intervention (SBI) on alcohol use risk among university students. Methods: In this quasi-experimental study, a total of 636 students were screened for alcohol use risk with the World Health Organization Alcohol, Smoking, and Substance Involvement Screening Test (WHO-ASSIST) version 3.1. All participants with moderate and high risk of alcohol use were administered brief intervention (BI) delivered by trained students at baseline, 1 month, and 3 months, with a final assessment in 6 months. Longitudinal data on their alcohol use risk were analyzed. Results: The mean age (standard deviation) of the participants was 21.13 (3.05) years and 44.5% were female. The prevalence of the current alcohol use based on the WHO-ASSIST was 49.2% ( n = 315). Following three sessions of BI, the repeated measures ANOVA indicated that the WHO-ASSIST mean score for high-risk alcohol users ( n = 44) fell from 33.23 (3.82) at baseline to 18.3 (9.84) at 6 th month. This difference was statistically significant. Similarly, the mean score for moderate alcohol users fell from 19.62 (2.97) at baseline to 11.31 (5.52) at 6 months. The difference was statistically significant. There were significant group-level differences in the risk score over the study period, for the low risk, moderate risk, and high-risk users at the end of the study. Conclusion: Screening and BI showed significant benefits on alcohol use risk. Our findings suggest SBI as a feasible and effective intervention for mitigating the risk of alcohol use among young students in resource-restricted settings. Further research using a robust sample to reflect differences in setting and student characteristics is warranted.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".