EMDR for Depression: A Meta-Analysis and Systematic Review
Bibliographic record
Abstract
The literature on the efficacy of eye movement desensitization and reprocessing (EMDR) for treating depression is heterogeneous due to research design, quality issues, and trials methodology. The current meta-analysis seeks to examine EMDR for depression with the aim of answering the aforementioned limitations. Thirty-nine studies were included for analysis after a review of the relevant literature. Univariate meta-regressions were run to examine dose-response and the effect of moderating variables. Subanalysis for primary and secondary depression showed a large, significant, and heterogeneous effect-size estimates, where EMDR significantly improved symptoms of depression in contrast to all control types. At post hoc, data were reexamined and a significant and large, yet heterogeneous, effect-size estimate emerged between the EMDR and control arm after the removal of two outliers [Hedges' g = 0.70, 95% CI =0.50–0.89, p -value < .01, I 2 = 70%, K = 37]. This is the first meta-analysis examining for the effect of EMDR comparing to various control modalities on depression with dose-response. We found (a) that studies were balanced at onset in terms of depression severity, and (b) a large and significant effect of EMDR on depression at the end of trials. Additionally, the significance of the aggregate effect-size estimate at the end of trials was unchanged by the intake of psychotropic medications, reported demographic variables, or EMDR methodology.
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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 | 0.016 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.038 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 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".