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Record W2067121749 · doi:10.1002/jclp.20252

Emotion in psychotherapy: A practice‐friendly research review

2006· review· en· W2067121749 on OpenAlexaff
Leslie S. Greenberg, Antonio Pascual‐Leone

Bibliographic record

VenueJournal of Clinical Psychology · 2006
Typereview
Languageen
FieldPsychology
TopicPsychotherapy Techniques and Applications
Canadian institutionsUniversity of WindsorYork University
Fundersnot available
KeywordsPsychologyPsychotherapistSession (web analytics)ArousalMeaning (existential)Experiential learningAnxietySocial psychologyPsychiatry

Abstract

fetched live from OpenAlex

This article reviews the process and outcome research on emotion in psychotherapy. Four distinct types of emotion processes are identified in the literature as useful in therapy, depending on a client's presenting concerns: emotional awareness and arousal; emotional regulation, active reflection on emotion (meaning making), and emotional transformation. Research findings are summarized to highlight the practical implications of these different emotion processes to psychotherapy. A range of selected treatments from different therapeutic orientations are addressed collectively as different types of emotion-focused, experiential therapies and are compared on the basis of how they work with emotion in session.

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)0.006
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.008
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0080.010
Science and technology studies0.0010.002
Scholarly communication0.0040.006
Open science0.0020.002
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0050.003

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 head0.628
GPT teacher head0.726
Teacher spread0.098 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations362
Published2006
Admission routes1
Has abstractyes

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