Bibliographic record
Abstract
BACKGROUND: Alcohol is the leading risk factor for severe injury. This study examined whether patients hospitalized after an alcohol-related injury are motivated to change alcohol use, thus making them potential candidates for brief motivational interventions. METHODS: Fifty patients hospitalized in a Level I trauma center, admitted with a positive blood alcohol concentration, were assessed for motivation to change alcohol-related behavior using validated questionnaires. Information was gathered regarding level of alcohol use, consequences of use, and motivation to change drinking habits. Demographic variables, alcohol use measures, perception of alcohol's contribution to the current injury, and negative consequences of use were evaluated by linear regression to predict readiness to change drinking. RESULTS: Mean blood alcohol concentration was 197 mg/dL at admission. Patients reported a pattern of binge drinking, with 86% reporting at least one binge-drinking episode in the past month, and a mean of 3.4 days of binge drinking per month. Most patients (84%) reported considering making a change (cutting down or quitting) in their drinking. Finally, patients reported experiencing an average of 22.5 negative lifetime consequences to their drinking. Having more negative consequences was found to significantly predict readiness to change drinking (p < 0.001). CONCLUSION: In this study, most patients were motivated to change their drinking. An increased number of negative consequences of alcohol use before admission predicted readiness to change drinking habits. Brief motivational interventions would be a reasonable option in this group of patients.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".