The client “experiencing” scale as a predictor of treatment outcomes: A meta-analysis on psychotherapy process
Bibliographic record
Abstract
OBJECTIVE: The experiencing scale (EXP) is an often used measure of client's depth of processing and meaning-making in-session. While research suggests that "client experiencing" predicts psychotherapy outcomes, this relationship has never been summarized in a meta-analysis. We examine this specific client factor as an in-session process predictor of good treatment outcomes. METHOD: A meta-analysis quantified the relationship between client experiencing and therapy outcomes using a total of 10 studies and 406 clients. RESULTS: Analysis indicated that client experiencing is a small to medium predictor of standardized symptom improvements at final treatment outcomes with an effect of r = -.19 (95% CI -.10 to -.29), which we consider a "best estimate" for robustly quantifying the association between EXP and self-reported clinical outcomes. However, effects were higher (i.e., r = -.25) when observational measures of outcome were also included: Subgroup analyses indicated that EXP effects were moderated by the modality of outcome measurement (i.e., symptom reports vs. observational measures). On the other hand, statistical index, treatment phase, or treatment approach did not have significant impacts, which addresses some perennial questions in the EXP literature. CONCLUSIONS: Client experiencing is a small to medium predictor of treatment outcomes and a probable common factor.
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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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.021 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".