The Efficacy of Experiential Dynamic Therapies: A 10‐Year Systematic Review and Meta‐Analysis Update
Bibliographic record
Abstract
ABSTRACT There is a growing interest in clinical interventions targeting emotion regulation difficulties across mental health conditions. Experiential dynamic therapies (EDTs) are transdiagnostic, affect‐focused, short‐term psychodynamic therapy models that emphasize in‐session emotional processing. This review provides a 10‐year update on the efficacy of EDTs for mood, anxiety, personality and somatic symptom disorders in adults and children/adolescents. A comprehensive search identified 57 randomized controlled trials ( n = 4330) conducted in Western ( k = 38; n = 3178) and non‐Western countries ( k = 19; n = 1152) between 1978 and 2024. Random‐effects meta‐analyses on primary outcomes indicated large, significant effects for EDTs compared to inactive controls at post‐treatment (Hedge's g = −0.96; k = 41) and follow‐up ( g = −1.11; k = 20). Compared to active controls, effects were small and non‐significant post‐treatment ( g = −0.17; k = 27) but became significant at follow‐up ( g = −0.40; k = 19), suggesting a potential modest long‐term advantage of EDTs. Despite substantial heterogeneity ( I 2 > 75%), results remained robust in sensitivity analyses. Moderator analyses revealed few significant findings, indicating relative consistency across diagnostic groups, treatment formats and active comparators. Non‐Western and lower quality studies reported larger effects compared to inactive, but not active, controls. While cautious interpretation is warranted due to unexplained heterogeneity, findings support EDTs as efficacious transdiagnostic interventions for emotional disorders, with sustained benefits over time. Future research should prioritize large‐scale, methodologically rigorous trials that explore mechanisms of change, optimize treatment delivery and identify moderators of long‐term outcomes.
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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 | 0.026 | 0.058 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.024 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".