Efficacy of interventions targeting alcohol, drug and smoking behaviors in university and college students: A review of randomized controlled trials
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effectiveness of interventions targeting alcohol consumption, drug use and smoking for college/university students. PARTICIPANTS: College/University students. METHODS: Studies were eligible if: (1)included students attending universities/colleges; (2)implemented in a university/college setting; (3)aimed to improve at least one of the following behaviors: alcohol and/or drug use and/or smoking; (4)were RCTs. The effect of the interventions on behaviors was determined by the percentage of studies that reported an effect. Due to the heterogeneity of outcomes meta-analysis was not conducted. RESULTS: 88 studies met criteria. University-based interventions were effective for reducing alcohol-related outcomes (drinking patterns, BAC, consequences, problem drinking). Inconsistent findings for drug and smoking were observed. CONCLUSIONS: University-based interventions have the potential to improve health for students. While there is a breadth of research examining the efficacy of interventions to reduce alcohol consumption, further research is needed to determine the best approach for addressing smoking and drug use among students.
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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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.011 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".