Distinctions without a difference: Direct comparisons of psychotherapies for alcohol use disorders.
Bibliographic record
Abstract
To estimate the relative efficacy of alcohol use disorder treatments, the authors meta-analyzed studies that directly compared 2 bona fide psychological treatments. The authors accommodated problems with the inclusion of multiple treatment comparisons by randomly assigning a positive/negative sign to the effect size derived from each comparison and then estimating the extent to which effect sizes were heterogeneous. The authors' primary hypothesis was that the variability in effect sizes of bona fide psychological treatments for alcohol use disorders that were directly compared would be zero. For both alcohol measures and measures of abstinence, analyses indicate that effects were homogenously distributed about zero (I(2) = 10.61, 0.00, respectively), indicating that different treatment comparisons yielded a common effect size that was not significantly different from zero. Analyses also indicate that allegiance accounted for a significant portion of variability in differences between treatments. Implications for the treatment of alcohol use disorders as well as research on the mechanisms responsible for the benefit of treatment are discussed.
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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 | 0.037 | 0.106 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".