A Meta-Analysis of Cognitive-Behavioural Therapy for Adult Depression, Alone and in Comparison with other Treatments
Bibliographic record
Abstract
OBJECTIVE: No recent meta-analysis has examined the effects of cognitive-behavioural therapy (CBT) for adult depression. We decided to conduct such an updated meta-analysis. METHODS: Studies were identified through systematic searches in bibliographical databases (PubMed, PsycINFO, Embase, and the Cochrane library). We included studies examining the effects of CBT, compared with control groups, other psychotherapies, and pharmacotherapy. RESULTS: A total of 115 studies met inclusion criteria. The mean effect size (ES) of 94 comparisons from 75 studies of CBT and control groups was Hedges g = 0.71 (95% CI 0.62 to 0.79), which corresponds with a number needed to treat of 2.6. However, this may be an overestimation of the true ES as we found strong indications for publication bias (ES after adjustment for bias was g = 0.53), and because the ES of higher-quality studies was significantly lower (g = 0.53) than for lower-quality studies (g = 0.90). The difference between high- and low-quality studies remained significant after adjustment for other study characteristics in a multivariate meta-regression analysis. We did not find any indication that CBT was more or less effective than other psychotherapies or pharmacotherapy. Combined treatment was significantly more effective than pharmacotherapy alone (g = 0.49). CONCLUSIONS: There is no doubt that CBT is an effective treatment for adult depression, although the effects may have been overestimated until now. CBT is also the most studied psychotherapy for depression, and thus has the greatest weight of evidence. However, other treatments approach its overall efficacy.
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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 | 0.025 | 0.049 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.075 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| 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".