Treatment-specific changes in decentering following mindfulness-based cognitive therapy versus antidepressant medication or placebo for prevention of depressive relapse.
Bibliographic record
Abstract
OBJECTIVE: To examine whether metacognitive psychological skills, acquired in mindfulness-based cognitive therapy (MBCT), are also present in patients receiving medication treatments for prevention of depressive relapse and whether these skills mediate MBCT's effectiveness. METHOD: This study, embedded within a randomized efficacy trial of MBCT, was the first to examine changes in mindfulness and decentering during 6-8 months of antidepressant treatment and then during an 18-month maintenance phase in which patients discontinued medication and received MBCT, continued on antidepressants, or were switched to a placebo. In total, 84 patients (mean age = 44 years, 58% female) were randomized to 1 of these 3 prevention conditions. In addition to symptom variables, changes in mindfulness, rumination, and decentering were assessed during the phases of the study. RESULTS: Pharmacological treatment of acute depression was associated with reductions in scores for rumination and increased wider experiences. During the maintenance phase, only patients receiving MBCT showed significant increases in the ability to monitor and observe thoughts and feelings as measured by the Wider Experiences (p < .01) and Decentering (p < .01) subscales of the Experiences Questionnaire and by the Toronto Mindfulness Scale. In addition, changes in Wider Experiences (p < .05) and Curiosity (p < .01) predicted lower Hamilton Rating Scale for Depression scores at 6-month follow-up. CONCLUSIONS: An increased capacity for decentering and curiosity may be fostered during MBCT and may underlie its effectiveness. With practice, patients can learn to counter habitual avoidance tendencies and to regulate dysphoric affect in ways that support recovery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".