Behavioral economic predictors of brief alcohol intervention outcomes.
Bibliographic record
Abstract
OBJECTIVE: The present study attempted to determine whether behavioral economic indices of elevated alcohol reward value, measured before and immediately after a brief alcohol intervention, predict treatment response. METHOD: Participants were 133 heavy drinking college students (49.6% female, 51.4% male; 64.3% Caucasian, 29.5% African American) who were randomized to 1 of 3 conditions: motivational interviewing plus personalized feedback (brief motivational interventions; BMI), computerized personalized feedback intervention (electronic check-up to go; e-CHUG), and assessment only. RESULTS: Baseline level of alcohol demand intensity (maximum consumption) significantly predicted drinks per week and alcohol problems at 1-month follow-up and baseline relative discretionary expenditures on alcohol significantly predicted drinks per week and alcohol problems at 6-month follow-up. BMI and e-CHUG were associated with an immediate postsession reduction in alcohol demand (p < .001, ηp2 = .29) that persisted at the 1-month follow-up, with greater postsession reductions in the BMI condition (p = .02, ηp2 = .06). Reductions in demand intensity and Omax (maximum expenditure) immediately postintervention significantly predicted drinking reductions at 1-month follow up (p = .04, ΔR2 = .02, and p = .01, ΔR2 = .03, respectively). Reductions in relative discretionary expenditures on alcohol at 1-month significantly predicted drinking (p = .002, ΔR2 = .06,) and alcohol problem (p < .001, ΔR2 = .13) reductions at the 6-month follow-up. CONCLUSIONS: These results suggest that behavioral economic reward value indices may function as risk factors for poor intervention response and as clinically relevant markers of change in heavy drinkers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".