One minute of grief: Emotional processing in short-term dynamic psychotherapy for adjustment disorder.
Bibliographic record
Abstract
OBJECTIVE: Depth of emotional processing has shown to be related to outcome across approaches to psychotherapy. Moreover, a specific emotional sequence has been postulated and tested in several studies on experiential psychotherapy (Pascual-Leone & Greenberg, 2007). This process-outcome study aims at reproducing the sequential model of emotional processing in psychodynamic psychotherapy for adjustment disorder and linking these variables with ultimate therapeutic outcome. METHOD: In this study, 32 patients underwent short-term dynamic psychotherapy. On the basis of reliable clinical change statistics, a subgroup (n = 16) presented with good outcome and another subgroup (n = 16) had a poor outcome in the end of treatment. The strongest alliance session of each case was rated using the observer-rated system Classification of Affective Meaning States. Reliability coefficients for the measure were excellent (κ = .82). RESULTS: Using 1 min as the fine-grained unit of analysis, results showed that the experience of fundamentally adaptive grief was more common in the in-session process of patients with good outcome, compared with those with poor outcomes (χ2 = 6.56, p = .01, d = 1.23). This variable alone predicted 19% of the change in depressive symptoms as measured by the Beck Depression Inventory at the end of treatment. Moreover, sequences of the original model were supported and related to outcome. CONCLUSIONS: These results are discussed within the framework of the sequential model of emotional processing and its possible relevance for psychodynamic psychotherapy.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".