Mindfulness‐based cognitive therapy for residual depressive symptoms
Bibliographic record
Abstract
OBJECTIVES: Mindfulness-based cognitive therapy (MBCT) is a new group-based intervention for prevention of relapse in recurrent depression which has not been scientifically evaluated regarding its clinical effectiveness for ameliorating residual depressive symptoms following a depressive episode. The aim of this study was to assess the efficacy of MBCT in reducing residual depressive symptoms in psychiatric outpatients with recurrent depression, and to particularly explore the effects of mindfulness techniques on rumination. DESIGN: The design of this study was a mixed model complex design. Design 1 consisted of a consecutive series of patients. They were assigned to either MBCT or TAU. The independent variables were time and group allocation, and dependent variables were Beck Depression Inventory (BDI) and Rumination Scale. In Design 2, the TAU group proceeded to complete an MBCT group, and the BDI and Rumination Scale results of the two groups were collapsed. METHOD: Nineteen patients with residual depressive symptoms following a depressive episode, and who were attending outpatient clinic, were assigned to either MBCT or treatment as usual (TAU), with the TAU group then proceeding to complete an MBCT group. Depressive and ruminative symptoms were assessed before, during, and after treatment, and at one-month follow-up. RESULTS: A significant reduction in depressive symptoms was found at the end of MBCT, with a further reduction at one-month follow-up. A trend towards a reduction in rumination scores was also observed. CONCLUSIONS: Group MBCT has a marked effect on residual depressive symptoms, which may be mediated through the mindfulness-based cognitive approach towards excessive negative ruminations in patients with residual depressive symptoms following a depressive episode.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".