Mindfulness-based cognitive therapy for residual depressive symptoms and relapse prophylaxis
Bibliographic record
Abstract
PURPOSE OF REVIEW: The article reviews the recent evidence for mindfulness-based cognitive therapy (MBCT) for patients with residual depressive symptoms or in remitted patients at increased risk for relapse. RECENT FINDINGS: Randomized controlled trials have shifted focus from comparing MBCT with treatment-as-usual to comparing MBCT against interventions. These studies have provided evidence for the efficacy of MBCT on par with maintenance antidepressant pharmacotherapy and leading to a relative reduction of risk on the order of 30-40%. Perhaps fuelled by these data, recent efforts have focused on extending MBCT to novel populations, such as acutely depressed patients, those diagnosed with health anxiety, social anxiety, fibromyalgia, or multiple chemical sensitivities as well migrating MBCT to online platforms so that it is more widely available. Neuroimaging studies of patients in structured therapies which feature mindfulness meditation, have reported findings that parallel behavioral changes, such as increased activation in brain regions subsuming self-focus and emotion regulation (prefrontal cortex) and interoceptive awareness (insula). SUMMARY: The current evidence base for MBCT is strongest for its application as a prophylactic intervention or for residual depressive symptoms, with early data suggesting additional indications outside the mood disorders. Future work will need to address dose-effect relationships between mindfulness practice and clinical benefits, as well as establishing the rates of uptake for online MBCT so that its benefits can be compared to in-person groups. Additionally, validating current or novel neural markers of MBCT treatment response will allow for patient matching and optimization of treatment response.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".