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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".