The impact of integrating emotion focused components into psychological therapy: A randomized controlled trial
Bibliographic record
Abstract
OBJECTIVES: This paper presents a randomized controlled trial on assimilative integration, which is aimed at integrating elements from other orientations within one approach to enrich its conceptual and practical repertoire. Elements from Emotion-Focused Therapy (EFT) were integrated into a form of cognitive behavior therapy: Psychological Therapy (PT). In one treatment condition, EFT was added to PT (+EFT) with the intent to enhance therapists' working with emotions. In the other condition, concepts and interventions based on the socialpsychological self-regulation approach were added to PT (+SR). Our assumption was that the +EFT would lead to greater and deeper change, particularly in the follow-up assessments. METHOD: Patients (n = 104) with anxiety, depression, or adjustment disorders were randomized to the two conditions and treated by 38 therapists who self-selected between the conditions. Primary outcome was symptom severity at 12-month follow-up; secondary outcomes included several measures such as interpersonal problems and quality of life. Variables were assessed at baseline, after 8 and 16 sessions, at posttreatment, and at 6- and 12-month follow-up. RESULTS: Contrary to our hypothesis, no significant between-group effects were found. CONCLUSION: The findings first suggest the difficulty of topping an already very effective approach to psychotherapy. Alternative interpretations were that the EFT training, while corresponding to regular practice in AI, was not sufficient to make a difference in outcome, or that while profiting from the enhancement of abilities for working with emotions, this was outbalanced by negative effects of difficulties related to the implementation of the new elements.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".