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Record W2588670055 · doi:10.1002/cpp.2076

Mindfulness‐based cognitive therapy as an augmentation treatment for obsessive–compulsive disorder

2017· article· en· W2588670055 on OpenAlexaff
Brenda L. Key, Karen Rowa, Peter Bieling, Randi E. McCabe, Elizabeth J. Pawluk

Bibliographic record

VenueClinical Psychology & Psychotherapy · 2017
Typearticle
Languageen
FieldPsychology
TopicObsessive-Compulsive Spectrum Disorders
Canadian institutionsMcMaster UniversitySt. Joseph’s Healthcare Hamilton
Fundersnot available
KeywordsMindfulnessMindfulness-based cognitive therapyPsychological interventionCognitive therapyPsychologyAnxietyClinical psychologyIntervention (counseling)PsychotherapistCognitionRandomized controlled trialCognitive behavioral therapyPsychiatryMedicine

Abstract

fetched live from OpenAlex

A significant number of obsessive-compulsive disorder (OCD) patients continue to experience symptoms that interfere with their functioning following cognitive behavioural therapy (CBT). Providing an additional augmentation treatment following CBT could help reduce these residual symptoms. Mindfulness interventions that facilitate less reactivity to thoughts and feelings may be helpful for patients suffering from residual OCD symptoms. The purpose of the current randomized waitlist control trial was to evaluate the feasibility and impact of providing an 8-week mindfulness-based cognitive therapy (MBCT) intervention following completion of a CBT intervention to OCD patients who continued to suffer from significant symptoms. Results indicated that compared to the waitlist control group, MBCT participants reported decreases in OCD symptoms (d = 1.38), depression symptoms (d = 1.25), anxiety symptoms (d = 1.02), and obsessive beliefs (d = 1.20) along with increases in self-compassion (d = 0.77) and mindfulness skills (d = 0.77). Additionally, participants reported high levels of satisfaction with the MBCT intervention. The results suggest that the use of MBCT for OCD as an augmentation therapy is acceptable to patients who continue to suffer from OCD symptoms after completing CBT and provides some additional relief from residual symptoms. KEY PRACTITIONER MESSAGE: Mindfulness interventions teach skills that facilitate disengaging from cognitive routines and accepting internal experience, and these skills may be valuable in treating obsessive-compulsive disorder (OCD), as individuals describe getting "stuck" in repetitive thoughts and consequent rituals. The results of this study suggest that teaching mindfulness skills using an 8-week mindfulness-based cognitive therapy (MBCT) intervention provides an added benefit (decreases in OCD, depression, and anxiety symptoms) for patients with OCD who have completed a cognitive behavioural therapy intervention and continued to suffer from significant symptoms. Participation in MBCT was also associated with increases in mindfulness skills including increased ability to be nonjudgmental and nonreactive. By fostering a nonjudgmental stance towards intrusive thoughts, mindfulness may discourage suppression and avoidance of thoughts and this could lead to increased habituation and a decreased reliance on compulsions. The use of MBCT as an augmentation treatment should be further explored to elucidate whether this treatment is beneficial for preventing relapse of OCD and could be compared against further cognitive behavioural therapy to see if offering participants a different and theoretically compelling intervention, such as MBCT, would outperform "more of the same" for individuals with OCD.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.129
GPT teacher head0.516
Teacher spread0.387 · 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 designRandomized trial
Domainnot available
GenreEmpirical

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

Citations98
Published2017
Admission routes1
Has abstractyes

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