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Record W90528101

Does the depth of client experiencing predict good psychotherapy outcomes? A meta-analysis of treatment outcomes

2012· article· en· W90528101 on OpenAlexaff
Nikita Yeryomenko

Bibliographic record

VenueScholarship at UWindsor (University of Windsor) · 2012
Typearticle
Languageen
FieldPsychology
TopicPsychotherapy Techniques and Applications
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsPsychologyMeta-analysisClinical psychologyPsychotherapistScale (ratio)Medicine
DOInot available

Abstract

fetched live from OpenAlex

The Experiencing Scale (EXP), a measure of client's emotional processing, is often used in psychotherapy process research. While researchers agree that it predicts treatment outcomes, this relationship has not been systematically studied. This meta-analysis quantified the relationship between EXP and therapy outcomes using a total of 11 studies and 458 clients. Analysis indicated that peak EXP measured during the working phase was the strongest predictor of treatment outcomes, r = .236. Subgroup analyses indicated that working phase effects were moderated by the outcome measure modality. Early phase effects were moderated by the type of treatment and the treatment target. In accordance with the literature in the field, working phase EXP was found to be a significant predictor of clinical outcomes, although this relationship was influenced by a number of variables. Further research should look at the moderators between EXP and outcomes, and at processes that increase client experiencing.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.087
GPT teacher head0.351
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2012
Admission routes1
Has abstractyes

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