Brief Physician Advice for Heavy Drinking College Students: A Randomized Controlled Trial in College Health Clinics
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to test the efficacy of brief physician advice in reducing alcohol use and related harm in college students. METHOD: The College Health Intervention Projects (CHIPs) is a randomized, controlled clinical trial with 12-month follow-up conducted in five college health clinics in Wisconsin; Washington state; and Vancouver, Canada. Of the 12,900 students screened for high-risk drinking, 484 men and 502 women met inclusion criteria and were randomized into a control (n = 493) or intervention (n = 493) group. Ninety-six percent of students participated in the follow-up procedures. The intervention consisted of two 15-minute counseling visits and two follow-up phone calls, and used motivational interviewing, contracting, diary cards, and take-home exercises. RESULTS: No significant differences were found between groups at baseline on alcohol use, age, socioeconomic or smoking status, rates of depression, or measures of alcohol-related harm. At 12 months, the experimental subjects reduced their 28-day drinking totals by 27.2%, and the control group reduced their totals by 21%. A mixed effects repeated measures model found a statistical difference in favor of the brief-intervention group (beta = 4.7, SE = 2.0, p = .018) in 28-day drinking totals. The total Rutgers Alcohol Problem Index score was also significantly different during the 12-month follow-up period (beta = 0.8, SE = 0.4, p = .033). There was no difference on the other outcome measures of interest, such as frequency of excessive heavy drinking, health care utilization, injuries, drunk driving, depression, or tobacco use. CONCLUSIONS: The study supports resource allocation and implementation of alcohol screening and brief physician advice in primary care-based college health clinics.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 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 | 0.003 |
| 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, 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".