Client emotional productivity—optimal client in-session emotional processing in experiential therapy
Bibliographic record
Abstract
OBJECTIVE: The goal of this investigation was to examine the predictive validity of Client Emotional Productivity (CEP), an operationalization of optimal client in-session emotional processing, possessing seven features: Attending, symbolization, congruence, acceptance, regulation, agency and differentiation. METHOD: CEP was related to improvement in depressive and general symptoms, in 74 clients (66% female, 34% male) who received experiential therapy of depression and this was compared to the relationship between client high expressed emotional (CHEEA) arousal and the working alliance (WAI) and outcome. RESULTS: Hierarchical regression analyses revealed that working phase CEP predicted significant reduction of depressive and general symptoms over and above that predicted by beginning phase CEP, the working alliance and working phase CHEEA. Working phase CEP emerged as the sole, independent predictor of outcome for both depressive and general symptoms. CONCLUSION: Productive emotional processing, thus, mediates the relationship between the alliance and outcome and seems to go beyond mere activation and expression of emotional experience. It rather seems to involve an increase in the ability to process activated primary emotion in a productive manner specified by CEP.
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 | 0.002 | 0.014 |
| 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 | 0.001 | 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".