Differentiating mindfulness-integrated cognitive behavior therapy and mindfulness-based cognitive therapy clinically: the why, how, and what of evidence-based practice
Bibliographic record
Abstract
It is important to be able to differentiate mindfulness-based programs in terms of their model, therapeutic elements, and supporting evidence. This article compares mindfulness-based cognitive therapy (MBCT), developed for relapse prevention in depression, and mindfulness-integrated cognitive behavior therapy (MiCBT), developed for transdiagnostic applications, on: (1) origins, context and theoretical rationale (why), (2) program structure, practice and, professional training (how), and (3) evidence (what). While both approaches incorporate behavior change methods, MBCT encourages behavioral activation, whereas MiCBT includes various exposure procedures to reduce avoidance, including a protocol to practice equanimity during problematic interpersonal interactions, and a compassion training to prevent relapse. MBCT has a substantial research base, including multiple systematic reviews and meta-analyses. It is an endorsed preventative treatment for depressive relapse in several clinical guidelines, but its single disorder approach might be regarded as a limitation in many health service settings. MiCBT has a promising evidence base and potential to make a valuable contribution to psychological treatment through its transdiagnostic applicability but has not yet been considered in clinical guidelines. While greater attention to later stage dissemination and implementation research is recommended for MBCT, more high quality RCTs and systematic reviews are needed to develop the evidence base for MiCBT.
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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 | 0.100 | 0.285 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".