Active Ingredients: How and Why Evidence-Based Alcohol Behavioral Treatment Interventions Work
Bibliographic record
Abstract
This article summarizes the proceedings of a symposium that was organized and chaired by Richard Longabaugh and presented at the 2004 Research Society on Alcoholism meeting in Vancouver, British Columbia, Canada. The aim of the presentation was to focus on evidence for the active ingredients of behavioral therapies for patients with alcohol use disorders. Dennis M. Donovan, PhD, reviewed evidence for the active ingredients of cognitive behavioral therapy. Barbara S. McCrady, PhD, presented a conceptual model for mechanisms of change in alcohol behavior couples therapy and reviewed evidence for this model. J. Scott Tonigan, PhD, presented data testing three hypothesized mechanisms of change in twelve-step facilitation treatment. Mitchell P. Karno, PhD, presented therapy process data that tested whether matching therapist behaviors to client attribute across three therapies affected drinking outcomes. Jon Morgenstern served as discussant.
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 | 0.166 | 0.308 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.021 | 0.031 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.015 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".