Intensive Short-Term Dynamic Psychotherapy for depression: Treatment effectiveness and effects of unlocking the unconscious
Bibliographic record
Abstract
Intensive Short-Term Dynamic Psychotherapy (ISTDP) has an increasing amount of evidence regarding its efficacy across various psychiatric conditions and specifically with depression. The aim of this study is to replicate the findings of controlled research by examining the effects of ISTDP in the treatment of depression in a large naturalistic sample, and also to explore the mediating role of unlocking the unconscious in this treatment. Healthcare costs were also explored. Data were collected from a naturalistic study conducted at the Centre for Emotions and Health, Halifax, Nova Scotia, Canada, between 1999 and 2007. A sample of 195 patients' self-reported levels of depression, measured by the depression subscale of the Brief Symptom Inventory (BSI), and interpersonal problems, measured by the Inventory of Interpersonal Problems-32 (IIP-32), were analyzed using mixed-effects models. The analysis revealed a significant and large effect of ISTDP on both depression (within-group Cohen's d = 1.02, 95% CI [0.75, 1.26]) and interpersonal problems (within-group Cohen's d = 1.17, 95% CI [0.89, 1.46]). The process of unlocking the unconscious emerged as a significant mediator of treatment outcomes for both depression (between-group Cohen’s d = 0.60, 95% CI [0.16, 1.07]) and interpersonal problems (between-group Cohen’s d = 0.47, 95% CI [-0.05, 0.95]). Reductions in costs regarding physician (p = 0.07) and hospital costs (p < 0.05) were observed. These findings support the efficacy of ISTDP in treating depression and highlight the importance of unlocking the unconscious among patients with depression.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".