Emotional change process in resolving self-criticism during experiential treatment of depression
Bibliographic record
Abstract
OBJECTIVE: This study tested emotion-focused therapy (EFT) theory assumptions concerning optimal emotion schematic processing during experiential psychotherapies. Emotion schematic change was investigated in the particular problem context of resolving self-criticism, an emotion schematic vulnerability to depression identified across all major psychotherapy theories. METHOD: The sample was nine highly self-critical depressed clients who received experiential treatment (n = 5 resolved while n = 4 did not resolve their self-criticism by termination). Emotion episodes (EEs) were exhaustively sampled from five sessions across three therapy phases (early, working phase, and termination) for each client. All their EEs across therapy were coded using a process measure called the Classification of Affective-Meaning States. Three complementary analytic procedures were used to examine emotion schematic changes within and across phases of therapy: graphical/descriptive, linear mixed modelling, and THEME sequential pattern analysis. RESULTS: Convergent evidence from these analyses supported EFT theory. Good resolvers of self-criticism decreased expression of secondary emotions and increased expression of primary adaptive emotions. Good resolvers also exhibited more sequences of EEs consistent with transformation of secondary and maladaptive emotions to adaptive emotions. Future directions of this research are discussed.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".