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Record W2049171465 · doi:10.1017/s1352465810000895

Does CBT Facilitate Emotional Processing?

2011· article· en· W2049171465 on OpenAlexaboutno aff
Roger Baker, Matthew Owens, Sarah Thomas, Anna Whittlesea, Gareth Abbey, Phil Gower, Lara Tosunlar, Eimear Corrigan, Peter Thomas

Bibliographic record

VenueBehavioural and Cognitive Psychotherapy · 2011
Typearticle
Languageen
FieldMedicine
TopicPsychosomatic Disorders and Their Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsAlexithymiaToronto Alexithymia ScalePsychologyCognitionClinical psychologyPsychotherapistPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Cognitive Behavioural Therapy (CBT) is not primarily conceptualized as operating via affective processes. However, there is growing recognition that emotional processing plays an important role during the course of therapy. AIMS: The Emotional Processing Scale was developed as a clinical and research tool to measure emotional processing deficits and the process of emotional change during therapy. METHOD: Fifty-five patients receiving CBT were given measures of emotional functioning (Toronto Alexithymia Scale [TAS-20]; Emotional Processing Scale [EPS-38]) and psychological symptoms (Brief Symptom Inventory [BSI]) pre- and post-therapy. In addition, the EPS-38 was administered to a sample of 173 healthy individuals. RESULTS: Initially, the patient group exhibited elevated emotional processing scores compared to the healthy group, but after therapy, these scores decreased and approached those of the healthy group. CONCLUSIONS: This suggests that therapy ostensibly designed to reduce psychiatric symptoms via cognitive processes may also facilitate emotional processing. The Emotional Processing Scale demonstrated sensitivity to changes in alexithymia and psychiatric symptom severity, and may provide a valid and reliable means of assessing change during therapy.

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)0.000
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 score0.192
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.064
GPT teacher head0.300
Teacher spread0.236 · 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

Citations59
Published2011
Admission routes1
Has abstractyes

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