Internet‐based brief intervention for young men with unhealthy alcohol use: a randomized controlled trial in a general population sample
Bibliographic record
Abstract
AIM: To test the efficacy of an internet-based brief intervention (IBI) in decreasing alcohol use among young Swiss men aged 21 years on average. DESIGN: Two parallel-group randomized controlled trial with a 1 : 1 allocation ratio containing follow-up assessments at 1 and 6 months post-randomization SETTING: Internet-based study in a general population sample. PARTICIPANTS: Twenty-one-year-old men from Switzerland with unhealthy alcohol use (> 14 drinks/week or ≥ 6 drinks/occasion at least monthly or Alcohol Use Disorders Identification Test (AUDIT) scores ≥ 8) INTERVENTION: IBI consisting of (1) normative feedback, (2) feedback on consequences of alcohol use, (3) calorific value of reported consumption, (4) computed blood alcohol concentration for reported consumption, (5) indication of risk, (6) information on alcohol and health and (7) recommendations indicating low-risk drinking limits. Control condition: no intervention (assessment only). MEASUREMENTS: At 1 and 6 months: quantity/frequency questions on alcohol use (primary outcome: number of drinks/week) and binge drinking prevalence; at 6 months: AUDIT score, consequences of drinking (range = 0-12). FINDINGS: Follow-up rates were 92% at 1 month and 91% at 6 months. At 6 months, participants in the intervention group (n = 367) reported greater reductions in the number of drinks/week than participants in the control group (n = 370) [treatment × time interaction, incidence rate ratio (RR) = 0.86, 95% confidence interval (CI) = 0.78; 0.96], but no significant differences were observed on binge drinking prevalence. There was a favourable intervention effect on AUDIT scores (IRR = 0.93, 95% CI = 0.88; 0.98), but not on the number of consequences (IRR = 0.93, 95% CI = 0.84; 1.03). CONCLUSIONS: An internet-based brief intervention directed at harmful alcohol use among young men led to a reduction in self-reported alcohol consumption and AUDIT scores compared with a no-intervention control condition (assessment only).
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".