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Record W2758627751 · doi:10.1080/15332985.2017.1373266

Relative effectiveness of mindfulness and cognitive behavioral interventions for anxiety disorders: Meta-analytic review

2017· article· en· W2758627751 on OpenAlexaff
Samina K. Singh, Kevin M. Gorey

Bibliographic record

VenueSocial Work in Mental Health · 2017
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsMindfulnessAnxietyPsychological interventionMeta-analysisPsychologyClinical psychologyIntervention (counseling)CognitionRandomized controlled trialPsychotherapistCognitive therapyPsychiatryMedicine

Abstract

fetched live from OpenAlex

Increasingly popular mindfulness intervention innovations seem demonstrably effective in alleviating anxiety among people with anxiety disorders. However, the basis of such primary and synthetic evidence has, for the most part, been comparisons with non-active comparison conditions such as waiting lists. The longest-standing and strongest evidence-informed practices in this field have been cognitive behavioral interventions (CBI). This meta-analysis synthesized evidence from nine randomized trials of the relative effectiveness of mindfulness interventions compared to CBIs (i.e., active control groups) in treating anxiety disorders. The sample-weighted synthesis found no statistically or practically significant differences between the two groups on anxiety alleviation: Cohen’s d = - 0.02 (95% confidence interval = - 0.16, 0.12). Both groups enjoyed large clinical benefits. However, because mindfulness methods may require less professional training and take less time for both workers and clients to master, they are probably less expensive to provide. As they are probably less expensive, but equally effective, it seems that, in a cost-beneficial sense, mindfulness interventions may be more practically effective. These review-generated meta-analytic findings and inferences may be best thought of as developed hypotheses for future research testing. These and other future research needs are discussed.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.715
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.158
GPT teacher head0.497
Teacher spread0.339 · 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 teacher head, not a consensus.

Study designObservational
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

Citations31
Published2017
Admission routes1
Has abstractyes

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