Effectiveness of emotion-focused therapy: Main results of a practice–research network study
Bibliographic record
Abstract
Background Emotion-Focused Therapy is one of the evidence-based psychotherapies for a range of psychological problems. While most evidence was gained from randomized controlled studies or process research at university settings, there is a need for more outcome research conducted within practitioner–researcher networks assessing the effectiveness of Emotion-Focused Therapy in private practice.Methods A total of N = 70 clients with a variety of psychiatric disorders have been included in the bi-national practitioner researcher network for Emotion-focused Therapy Switzerland–Germany. Symptom change was assessed using CORE-OM, BDI-II, IIP and WSAS. Therapist adherence was assessed by the self-reported version of the PCEPS. Pre–post-follow-up analyses were conducted using paired sample t-tests and hierarchical linear modeling.Results Pre–post-assessment shows small to medium effect sizes for all outcome measures (except for interpersonal problems which did not change). Symptom reduction is maintained for CORE, BDI and WSAS, and increased for IIP at 3 and 6 months follow-up. Therapist adherence is good to excellent in this sample.Conclusions Emotion-Focused Therapy is effective in the real-world practice, as suggested by the small to moderate effect sizes. The smaller effects found in the current study as compared to randomized controlled trials are consistent with the literature and suggest that EFT remains moderately effective when applied in a naturalistic context.
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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 | 0.076 | 0.168 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".