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Record W2399513922 · doi:10.2147/prbm.s63949

Mindfulness-based cognitive therapy for depression: trends and developments

2016· review· en· W2399513922 on OpenAlexaff
Nancy L. Kocovski, Meagan B. MacKenzie

Bibliographic record

VenuePsychology Research and Behavior Management · 2016
Typereview
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsToronto Metropolitan UniversityWilfrid Laurier University
Fundersnot available
KeywordsMindfulnessMindfulness-based cognitive therapyDepression (economics)PsychotherapistPsychologyCognitive therapyCognitionClinical psychologyMedicinePsychiatry

Abstract

fetched live from OpenAlex

Mindfulness-based cognitive therapy (MBCT) was developed as a psychological intervention for individuals at risk of depressive relapse. Possible mechanisms of change for this intervention are in line with its theoretical underpinnings, and include increases in mindfulness and/or decreases in negative repetitive thoughts. This review provides an overview of current trends in MBCT research, including efficacy and questions regarding the specific effects of MBCT in light of recent comparisons with structurally equivalent control conditions, mechanisms of change, and moderators of treatment outcome. In addition, future directions are discussed, such as challenges with training an adequate number of therapists and disseminating this therapy.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.250
GPT teacher head0.542
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations70
Published2016
Admission routes1
Has abstractyes

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