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Record W2089025218 · doi:10.1080/10503307.2014.958597

Emotion categories and patterns of change in experiential therapy for depression

2014· article· en· W2089025218 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePsychotherapy Research · 2014
Typearticle
Languageen
FieldPsychology
TopicPsychotherapy Techniques and Applications
Canadian institutionsYork University
Fundersnot available
KeywordsPsychologyOperationalizationClinical psychologyPsychotherapistDepressive symptomsSession (web analytics)Depression (economics)AnxietyPsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: This investigation examined the relationship between in-session types of emotional experience operationalized by the emotion category coding system and the reduction of depressive symptoms in emotion-focused therapy (EFT). METHOD: Segments of videotaped sessions were coded and the different emotion categories were related to reduction in depressive symptoms in a sample of 30 clients who received EFT for depression. RESULTS: Both fewer secondary and more primary adaptive emotions, in the working phases of therapy, were found to significantly predict outcome. Moderate levels of primary maladaptive emotion in the middle working session were associated with outcome and the frequency with which clients moved from primary maladaptive to primary adaptive emotions in this session-predicted outcome. CONCLUSIONS: Results of this study support a transformational model of changing emotion with emotion.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.765
Threshold uncertainty score0.609

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.188
GPT teacher head0.497
Teacher spread0.308 · 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