Web-Based Alcohol Screening and Brief Intervention for University Students
Bibliographic record
Abstract
IMPORTANCE: Unhealthy alcohol use is a leading contributor to the global burden of disease, particularly among young people. Systematic reviews suggest efficacy of web-based alcohol screening and brief intervention and call for effectiveness trials in settings where it could be sustainably delivered. OBJECTIVE: To evaluate a national web-based alcohol screening and brief intervention program. DESIGN, SETTING, AND PARTICIPANTS: A multisite, double-blind, parallel-group, individually randomized trial was conducted at 7 New Zealand universities. In April and May of 2010, invitations containing hyperlinks to the Alcohol Use Disorders Identification Test-Consumption (AUDIT-C) screening test were e-mailed to 14,991 students aged 17 to 24 years. INTERVENTIONS: Participants who screened positive (AUDIT-C score ≥4) were randomized to undergo screening alone or to 10 minutes of assessment and feedback (including comparisons with medical guidelines and peer norms) on alcohol expenditure, peak blood alcohol concentration, alcohol dependence, and access to help and information. MAIN OUTCOMES AND MEASURES: A fully automated 5-month follow-up assessment was conducted that measured 6 primary outcomes: consumption per typical occasion, drinking frequency, volume of alcohol consumed, an academic problems score, and whether participants exceeded medical guidelines for acute harm (binge drinking) and chronic harm (heavy drinking). A Bonferroni-corrected significance threshold of .0083 was used to account for the 6 comparisons and a sensitivity analysis was used to assess possible attrition bias. RESULTS: Of 5135 students screened, 3422 scored 4 or greater and were randomized, and 83% were followed up. There was a significant effect on 1 of the 6 prespecified outcomes. Relative to control participants, those who received intervention consumed less alcohol per typical drinking occasion (median 4 drinks [interquartile range {IQR}, 2-8] vs 5 drinks [IQR 2-8]; rate ratio [RR], 0.93 [99.17% CI, 0.86-1.00]; P = .005) but not less often (RR, 0.95 [99.17% CI, 0.88-1.03]; P = .08) or less overall (RR, 0.95 [99.17% CI, 0.81-1.10]; P = .33). Academic problem scores were not lower (RR, 0.91 [99.17% CI, 0.76-1.08]; P = .14) and effects on the risks of binge drinking (odds ratio [OR], 0.84 [99.17% CI, 0.67-1.05]; P = .04) and heavy drinking (OR, 0.77 [99.17% CI, 0.56-1.05]; P = .03) were not significantly significant. In a sensitivity analysis accounting for attrition, the effect on alcohol per typical drinking occasion was no longer statistically significant. CONCLUSIONS AND RELEVANCE: A national web-based alcohol screening and brief intervention program produced no significant reductions in the frequency or overall volume of drinking or academic problems. There remains a possibility of a small reduction in the amount of alcohol consumed per typical drinking occasion. TRIAL REGISTRATION: anzctr.org.au Identifier: ACTRN12610000279022.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Randomized trial | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".