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Record W2054158486 · doi:10.1037/0022-006x.74.1.152

Clients' emotional processing in psychotherapy: A comparison between cognitive-behavioral and process-experiential therapies.

2006· article· en· W2054158486 on OpenAlexafffund
Jeanne C. Watson, Danielle L. Bedard

Bibliographic record

VenueJournal of Consulting and Clinical Psychology · 2006
Typearticle
Languageen
FieldPsychology
TopicPsychotherapy Techniques and Applications
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyExperiential learningCognitionPsychotherapistClinical psychologyOutcome (game theory)Cognitive therapyCognitive behavioral therapyPsychiatry

Abstract

fetched live from OpenAlex

The authors compared clients' emotional processing in good and bad outcome cases in cognitive behavioral therapy (CBT) and process-experiential therapy (PET) and investigated whether clients' emotional processing increases over the course of therapy. Twenty minutes from each of 3 sessions from 40 clients were rated on the Experiencing Scale. A 2x2x3 analysis of variance showed a significant difference between outcome and therapy groups, with clients in the good outcome and PET groups showing significantly higher levels of emotional processing than those in the poor outcome and CBT groups, respectively. Clients' level of emotional processing significantly increased from the beginning to the midpoint of therapy. The results indicate that CBT clients are more distant and disengaged from their emotional experience than clients in PET.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.117
GPT teacher head0.523
Teacher spread0.406 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations117
Published2006
Admission routes2
Has abstractyes

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