Pragmatic randomized controlled trial of providing access to a brief personalized alcohol feedback intervention in university students
Bibliographic record
Abstract
BACKGROUND: There is a growing body of evidence indicating that web-based personalized feedback interventions can reduce the amount of alcohol consumed in problem drinking college students. This study sought to evaluate whether providing voluntary access to such an intervention would have an impact on drinking. METHODS: College students responded to an email inviting them to participate in a short drinking survey. Those meeting criteria for risky drinking (and agreeing to participate in a follow-up) were randomized to an intervention condition where they were offered to participate in a web-based personalized feedback intervention or to a control condition (intervention not offered). Participants were followed-up at six weeks. RESULTS: A total of 425 participants were randomized to condition and 68% (n = 290) completed the six-week follow-up. No significant difference in drinking between conditions was observed. CONCLUSIONS: Web-based personalized feedback interventions that are offered to students on a voluntary basis may not have a measurable impact on problem drinking. TRIAL REGISTRATION: ClinicalTrials.gov: NCT01521078.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.013 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 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".