Managing resistance in cognitive behavioural therapy: The application of motivational interviewing in mixed anxiety and depression
Bibliographic record
Abstract
While cognitive behavioural therapy is highly effective in the treatment of anxiety and depression, a substantive number of individuals either refuse treatment, fail to respond to treatment or respond only partially. Arguably, ambivalence about change or about engaging in treatment tasks may in part be related to incomplete recovery rates in cognitive behavioural therapy. Motivational interviewing is a client-centred, directive treatment originally developed in the addictions domain whose goal is to enhance motivation for change by understanding and resolving ambivalence. This method has consistently received support for enhancing outcomes in the addictions domain, particularly when used as an adjunct to further treatment. As yet, motivational methods have not been generalized to the treatment of prevalent mental health problems, such as anxiety and depression. The present paper presents the application of a treatment targeting motivation (motivational interviewing adapted for anxiety and depression) to the management of resistance in cognitive behavioural therapy for 3 clients with mixed anxiety and depression. Motivational interviewing is conceived as an adjunct to highly effective traditional cognitive behavioural therapy methods, which is indicated for use with clients resistant to and significantly ambivalent about change-based techniques for managing anxiety or alleviating depression.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".