Intensive Short‐Term Dynamic Psychotherapy for generalized anxiety disorder: A pilot effectiveness and process‐outcome study
Bibliographic record
Abstract
The objective of this study was to evaluate the clinical- and cost-effectiveness of Intensive Short-Term Dynamic Psychotherapy (ISTDP) for generalized anxiety disorder (GAD). We further aimed to examine if a key clinical process within the ISTDP framework, termed the level of mobilization of unprocessed complex emotions (MUCE), was related to outcome. The sample consisted of 215 adult patients (60.9% female) with GAD and comorbid conditions treated in a tertiary mental health outpatient setting. The patients were provided an average of 8.3 sessions of ISTDP delivered by 38 therapists. The level of MUCE in treatment was assessed from videotaped sessions by a rater blind to treatment outcome. Year-by-year healthcare costs were derived independently from government databases. Multilevel growth models indicated significant decreases in psychiatric symptoms and interpersonal problems during treatment. These gains were corroborated by reductions in healthcare costs that continued for 4 years post-treatment reaching normal population means. Further, we found that the in-treatment level of MUCE was associated with larger treatment effects, underlining the significance of emotional experiencing and processing in the treatment of GAD. We conclude that ISTDP appears to reduce symptoms and costs associated with GAD and that the ISTDP framework may be useful for understanding key therapeutic processes in this challenging clinical population. Controlled studies of ISTDP for GAD are warranted.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".