Extending Motivational Interviewing to the Treatment of Major Mental Health Problems: Current Directions and Evidence
Bibliographic record
Abstract
Motivational interviewing (MI) was originally developed for the treatment of substance abuse but is rapidly expanding to other major mental health populations beyond addictions. This brief review considers the use of MI and related motivational enhancement therapies (METs) in the treatment of anxiety, depression, and eating disorders, and concurrent psychosis and substance use disorders. MI-MET has been added and (or) integrated into treatment for these problems in a wide variety of ways, most commonly as a pretreatment to other therapies (psychosocial treatments and pharmacotherapy) or integrated into standard assessment procedures. In each problem domain, the bulk of the current evidence supports the value of adding MI to existing therapies in increasing engagement with treatment and in improving clinical outcomes. This is particularly encouraging in that many of the populations included in these investigations represent severe and treatment-recalcitrant populations. However, research on the application of MI to other major mental health problems beyond addictions is in the early stages, with existing studies having numerous limitations (for example, small uncontrolled studies or lack of adequate control groups, and failure to establish both MI treatment integrity and the unique contribution of MI in integrated treatments). In short, the substantial body of promising preliminary findings strongly support the continued investigation of MI and related methods for these populations in well-designed clinical trials that examine not only the additive value of MI but also mechanisms underlying these effects and individual differences (moderators) indicating the need for MI.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".