The anger-depression mechanism in dynamic therapy: Experiencing previously avoided anger positively predicts reduction in depression via working alliance and insight.
Bibliographic record
Abstract
A central tenet of psychodynamic theory of depression is the role of avoided anger. However empirical research has not yet addressed the question of for which patients and via what pathways experiencing anger in sessions can help. The therapeutic alliance and acquisition of patient insight are important change processes in dynamic therapy and may mediate the anger-depression association. This study was embedded into a randomized trial testing the efficacy of Intensive Short-Term Dynamic Psychotherapy (ISTDP) for treatment resistant depression. In-session patient affect experiencing (AE) was coded for every available session (475/481) by blinded observers in 27 patients randomized to ISTDP. Dynamic Structural Equation Modeling was used to examine within-person associations between variation in depression scores session-by-session and both patient ratings (alliance) and observer ratings (AE and insight) of the treatment process. Alliance and insight were independent mediators of the effect of anger on next-session depression. However, the relative importance of these two indirect effects of anger on depression was conditional on pretreatment patient personality pathology (PP). In patients with higher PP, in-session anger was negatively related to depressive symptoms next session, with this effect operating through higher alliance. In patients with low PP, in-session anger was negatively related to depressive symptoms next session, with this effect operating through enhanced patient insight. These findings highlight an anger-depression mechanism of change in dynamic therapy. Depending upon patient personality, either an "insight pathway" or a "relational pathway" may promote the effectiveness of facilitating arousal and expression of patients' in-session feelings. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".