Patient attitudes towards change in adapted motivational interviewing for substance abuse: a systematic review
Bibliographic record
Abstract
Adapted motivational interviewing (AMI) represents a category of effective, directive and client-centered psychosocial treatments for substance abuse. In AMI, patients' attitudes towards change are considered critical elements for treatment outcome as well as therapeutic targets for alteration. Despite being a major focus in AMI, the role of attitudes towards change in AMI's action has yet to be systematically reviewed in substance abuse research. A search of PsycINFO, PUBMED/MEDLINE, and Science Direct databases and a manual search of related article reference lists identified 416 published randomized controlled trials that evaluated AMI's impact on the reduction of alcohol and drug use. Of those, 54 met the initial inclusion criterion by evaluating AMI's impact on attitudes towards change and/or testing hypotheses about attitudes towards change as moderators or mediators of outcome. Finally, 19 studies met the methodological quality inclusion criterion based upon a Newcastle-Ottawa Quality Assessment Scale score ≥ 7. Despite the conceptual importance of attitudes towards change in AMI, the empirical support for their role in AMI is inconclusive. Future research is warranted to investigate both the contextual factors (ie, population studied) as well as deployment characteristics of AMI (ie, counselor characteristics) likely responsible for equivocal findings.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.004 |
| 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) | 0.003 | 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".