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Emotionally Focused Therapy ( <scp>EFT</scp> )

2015· other· en· W1501214715 on OpenAlexaff
Jennifer Fitzgerald, Susan M. Johnson, Joel G. Thomas

Bibliographic record

VenueThe Encyclopedia of Clinical Psychology · 2015
Typeother
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsPsychologyAttachment theoryPsychotherapistInterpersonal communicationExperiential learningEmpathyMeaning (existential)CognitionSocial psychologyNeuroscience

Abstract

fetched live from OpenAlex

Abstract Emotionally focused therapy ( EFT ) is an empirically based therapy for individuals, couples and families, which has been developed from humanistic‐experiential models of psychotherapy. In this approach, emotion is regarded as a source of meaning and direction and people are regarded as capable of self‐determination with an innate tendency to grow. Individuals, supported by an empathic relationship with the therapist, are helped to access deeper emotional experience and to explore blocks to engaging with problems in functional ways. Research suggests that EFT for individuals with depression may be as effective as cognitive behavioral therapy. Attachment theory has guided the development of EFT with couples, emphasizing the need for partners to be accessible and responsive to each other to build a secure emotional bond. The EFT couples therapist works with both intrapsychic and interpersonal processes to assist partners to change their interactional positions and to access and disclose deeper emotional experience and attachment needs.

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)0.000
metaresearch head score (Gemma)0.001
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: Other · Consensus signal: Other
Teacher disagreement score0.061
Threshold uncertainty score0.203

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0610.008

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 head0.134
GPT teacher head0.509
Teacher spread0.375 · 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
GenreOther

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

Citations1
Published2015
Admission routes1
Has abstractyes

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