Motivational Interviewing and Clinical Psychiatry
Bibliographic record
Abstract
OBJECTIVES: Our objectives were as follows: (1) to survey the literature on motivational interviewing (MI), "a client-centered yet directive method for enhancing intrinsic motivation to change by exploring and resolving client ambivalence" and a well-established method of brief intervention, especially in the field of addictions treatment; (2) to review hypotheses about its mode of action; and (3) to discuss its possible impact on clinical psychiatry, in particular, on teaching communications skills. METHOD: Literature reviews and metaanalyses of numerous clinical trials of MI for addictions treatment have already been published and are briefly summarized. So far, no literature survey exists for MI applied to psychiatric patients. This review is limited to a synthesis of the articles relevant to psychiatry and to comments based on our team's experiences with MI. RESULTS: There is no evidence that MI achieves better results than other established techniques for treating addictions; it may simply work faster. The explanation for the method's rapid effectiveness remains speculative. Outcomes concerning the application of MI to psychiatric patients, although preliminary, are promising. Methods of assessing the integrity of MI treatment are more developed than in most psychotherapies, which permits the learning progress of trainees to be measured. CONCLUSIONS: MI offers a complement to usual psychiatric procedures. It may be worthwhile to teach it, not only for addictions but also for other broad treatment issues, such as enhancing patients' medication compliance and professionals' communication skills. Questions remain concerning MI's feasibility in psychiatry settings.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".