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Record W4220919904 · doi:10.1080/2692398x.2022.2041346

Somatic Interventions and Depth of Experiencing in Emotionally Focused Couple Therapy

2022· article· en· W4220919904 on OpenAlexaff
Sari Kailanko, Stephanie A. Wiebe, Giorgio A. Tasca, Aarno Laitila

Bibliographic record

VenueInternational Journal of Systemic Therapy · 2022
Typearticle
Languageen
FieldPsychology
TopicPsychotherapy Techniques and Applications
Canadian institutionsUniversity of OttawaSaint Paul University
Fundersnot available
KeywordsPsychological interventionPsychologyPsychotherapistAffect (linguistics)Session (web analytics)ArousalClinical psychologySocial psychologyCommunication

Abstract

fetched live from OpenAlex

Emotionally Focused Couple Therapy (EFT), an attachment-based couple therapy, views emotion as being central to the process of therapeutic change. As affect arousal of emotion is a somatic reaction, the purpose of this study was to focus on therapists’ interventions classified as noting and commenting on clients’ somatic cue of emotional experience, such as their facial expression or posture, in relation to depth of emotional experiencing demonstrated by clients in EFT couple therapy sessions. The sample included 13 therapists, each treating one couple during a single EFT training demonstration session. We coded therapists’ interventions (i.e., commenting on one partner’s somatic cue of emotion). Immediately prior to and following such therapist interventions, we rated the partner’s depth of emotional experiencing. The results of multilevel modeling demonstrated a significant linear increase in terms of depth of partner’s experiencing throughout the session. Furthermore, partners demonstrated a significant immediate increase in the depth of experiencing following somatically focused interventions. These findings suggest that interventions focusing on somatic experience of emotion may facilitate deeper experiencing for clients in EFT sessions.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.625
Threshold uncertainty score1.000

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 science0.0010.000
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 head0.063
GPT teacher head0.384
Teacher spread0.321 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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