Efficacy and moderators of cognitive behavioural therapy versus interpersonal psychotherapy for adult depression: study protocol of a systematic review and individual participant data meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Cognitive behavioural therapy (CBT) and interpersonal psychotherapy (IPT) are both efficacious treatments for depression, but it is less clear how both compare on outcome domains other than depression and in the longer term. Moreover, it is unclear which of these two psychotherapies works better for whom. This article describes the protocol for a systematic review and individual participant data (IPD) meta-analysis that aims to compare the efficacy of CBT and IPT for adults with depression on a range of outcomes in both the short and long term, and to explore moderators of the treatment effect. This study can enhance our understanding of treatments for depression and inform treatment personalisation. METHODS AND ANALYSIS: Systematic literature searches will be conducted in PubMed, PsycINFO, EMBASE and the Cochrane Library from inception to 1 January 2026, to identify randomised clinical trials (RCTs) comparing CBT and IPT for adult depression. Researchers of eligible studies will be invited to contribute their participant-level data. One-stage IPD meta-analyses will be conducted with mixed-effects models to examine (a) treatment efficacy on all outcome measures that are assessed at post-treatment or follow-up in at least two studies, and (b) various baseline participant characteristics as potential moderators of depressive symptom level at treatment completion. ETHICS AND DISSEMINATION: Ethical approval is not required for this study since it will be based on anonymised data from RCTs that have already been completed. The findings of the present study will be disseminated through a peer-reviewed journal or conference presentation.
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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 | 0.086 | 0.136 |
| Meta-epidemiology (narrow) | 0.007 | 0.005 |
| Meta-epidemiology (broad) | 0.027 | 0.038 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.037 | 0.005 |
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".