Relative effectiveness of mindfulness and cognitive behavioral interventions for anxiety disorders: Meta-analytic review
Bibliographic record
Abstract
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.
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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 | 0.022 | 0.064 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.033 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".