Dynamic emotional processing in experiential therapy: Two steps forward, one step back.
Bibliographic record
Abstract
The study of dynamic and nonlinear change has been a valuable development in psychotherapy process research. However, little advancement has been made in describing how moment-by-moment affective processes contribute to larger units of change. The purpose of this study was to examine observable moment-by-moment sequences in emotional processing as they occurred within productive sessions of experiential therapy. This article further tested A. Pascual-Leone and L. S. Greenberg's (2007) model of emotional processing through a reanalysis of their data sample of 34 sessions in which clients presented with global distress: 17 that ended in poor in-session events and 17 that ended in good in-session events. Current analyses used univariate and bootstrapping statistical methods to examine how dynamic temporal patterns in clients' emotion accumulated moment-by-moment to produce in-session gains in emotional processing. Results show that effective emotional processing was simultaneously associated with steady improvement according to the model as well as increased emotional range. Consequentially, good events were shown to occur in a 2-steps-forward, 1-step-back fashion. Finally, good events were also shown to have progressively shortened emotional collapses, whereas the opposite was true for poor in-session events.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| 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".