Brief Alcohol Intervention in the Emergency Department: Moderators of Effectiveness
Bibliographic record
Abstract
OBJECTIVE: Prior research supports the effectiveness of brief interventions for reducing alcohol misuse among patients in the emergency department (ED). However, limited information is available regarding the mechanisms of change, which could assist clinicians in streamlining or amplifying these interventions. This article examines moderators of outcomes among ED patients, ages 19 and older, who participated in a randomized controlled trial of a brief intervention for alcohol misuse. METHOD: Injured patients (N= 4,476) completed a computerized survey; 575 at-risk drinkers were randomly assigned to one of four brief intervention conditions, and 85% were interviewed again at 3-month and 12-month follow-ups. RESULTS: Regression models using the generalized estimating equations approach examined interaction effects between intervention condition (advice/no advice) and hypothesized moderator variables (stage of change, self-efficacy, acute alcohol use, attribution of injury to alcohol) on alcohol outcomes over time. Overall, participants who reported higher levels of self-efficacy had lower weekly consumption and consequences, whereas those with higher readiness to change had greater weekly consumption and consequences. Furthermore, individuals who attributed their injury to alcohol and received advice had significantly lower levels of average weekly alcohol consumption and less frequent heavy drinking from baseline to 12-month follow-up compared with those who attributed their injury to alcohol but did not receive advice. CONCLUSIONS: This study provides novel data regarding attribution for alcohol-related injury as an important moderator of change and suggests that highlighting the alcohol/injury connection in brief, ED-based alcohol interventions can augment their effectiveness.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".