Mindfulness-Based Cognitive Therapy: Theory and Practice
Bibliographic record
Abstract
Mindfulness-based cognitive therapy (MBCT) incorporates elements of cognitive-behavioural therapy with mindfulness-based stress reduction into an 8-session group program. Initially conceived as an intervention for relapse prevention in people with recurrent depression, it has since been applied to various psychiatric conditions. Our paper aims to briefly describe MBCT and its putative mechanisms of action, and to review the current findings about the use of MBCT in people with mood and anxiety disorders. The therapeutic stance of MBCT focuses on encouraging patients to adopt a new way of being and relating to their thoughts and feelings, while placing little emphasis on altering or challenging specific cognitions. Preliminary functional neuroimaging studies are consistent with an account of mindfulness improving emotional regulation by enhancing cortical regulation of limbic circuits and attentional control. Research findings from several randomized controlled trials suggest that MBCT is a useful intervention for relapse prevention in patients with recurrent depression, with efficacy that may be similar to maintenance antidepressants. Preliminary studies indicate MBCT also shows promise in the treatment of active depression, including treatment-resistant depression. Pilot studies have also evaluated MBCT in bipolar disorder and anxiety disorders. Patient and clinician resources for further information on mindfulness and MBCT are provided.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".