Long-term effects of transference interpretation in dynamic psychotherapy of personality disorders
Bibliographic record
Abstract
BACKGROUND: Only a few treatment studies of personality disorders (PD) patients are on longer-term psychotherapy, general outcome measures are used, and follow-up periods are usually short. More studies of long-term therapies, using outcome measures of core psychopathology, are needed. METHOD: This study is a dismantling randomized controlled clinical trial, specifically designed to study long-term effects of transference interpretation. Forty-six patients with mainly cluster C personality disorders were randomly assigned to 1 year of dynamic psychotherapy with or without transference interpretations. The outcome measures were remission from PD, improvement in interpersonal functioning, and use of mental health resources in the 3-year period after treatment termination. RESULTS: After therapy with transference interpretation PD-patients improved significantly more in core psychopathology and interpersonal functioning, the drop-out rate was reduced to zero, and use of health services was reduced to 50%, compared to therapy without this ingredient. Three years after treatment termination, 73% no longer met diagnostic criteria for any PD in the transference group, compared to 44% in the comparison group. CONCLUSIONS: PD-patients with co-morbid disorders improved in both treatment arms in this study. However, transference interpretation improved outcome substantially more. Long-term psychotherapy that includes transference interpretation is an effective treatment for cluster C personality disorders and milder cluster B personality disorders.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".