Optimal levels of emotional arousal in experiential therapy of depression.
Bibliographic record
Abstract
OBJECTIVE: To determine the relationship between length of time spent expressing highly aroused emotion and therapeutic outcome. METHOD: Thirty-eight clients (14 male, 24 female) between the ages of 22 and 60 years (M = 39.5, SD = 9.71), treated for depression with experiential therapy, were rated on working alliance and expressed emotional arousal (with the Client Expressed Emotional Arousal Scale) in their three highest arousal sessions. Among the clients, 34 were of European ethnicity, 2 were of Asian ethnicity, 1 was of Latino ethnicity, and 1 was of Caribbean-Canadian ethnicity. Clients were administered the short form of the Working Alliance Inventory following their 4th therapy session and also completed, pre- and posttherapy, the Beck Depression Inventory (BDI), the Global Severity Index (GSI) of the Symptom Checklist-90-Revised (SCL-90-R), the Inventory of Interpersonal Problems, and the Rosenberg Self-Esteem Scale. RESULTS: Hierarchical regressions showed that a nonlinear pattern of expressed emotional arousal predicted outcome significantly above the alliance. This combination predicted 30% of outcome variance on the BDI and 24% on the GSI (p < .01). An optimal frequency (25%) of highly aroused emotional expression was found to relate to outcome, with deviation from this optimal frequency predicting poorer outcome. CONCLUSIONS: Too much or too little emotion was found to be not as helpful as a moderate amount. It was concluded that expressed emotional arousal in experiential therapies has a more intricate relationship with therapeutic outcome than has previously been shown and that it is moderate amounts of heightened emotional arousal that improve predictions of therapeutic outcome.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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) | 0.001 | 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".