Emotional processing in experiential therapy: Why "the only way out is through."
Bibliographic record
Abstract
The purpose of this study was to examine observable moment-by-moment steps in emotional processing as they occurred within productive sessions of experiential therapy. Global distress was identified as an unprocessed emotion with high arousal and low meaningfulness. The investigation consisted of 2 studies as part of a task analysis that examined clients processing distress in live video-recorded therapy sessions. Clients in both studies were adults in experiential therapy for depression and ongoing interpersonal problems. Study 1 was the discovery-oriented phase of task analysis, which intensively examined 6 examples of global distress. The qualitative findings produced a model showing: global distress, fear, shame, and aggressive anger as undifferentiated and insufficiently processed emotions; the articulation of needs and negative self-evaluations as a pivotal step in change; and assertive anger, self-soothing, hurt, and grief as states of advanced processing. Study 2 tested the model using a sample of 34 clients in global distress. A multivariate analysis of variance showed that the model of emotional processing predicted positive in-session effects, and bootstrapping analyses were used to demonstrate that distinct emotions emerged moment by moment in predicted sequential patterns.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".