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Record W2764335002 · doi:10.1111/jmft.12284

The Impact of Blamer‐softening on Romantic Attachment in Emotionally Focused Couples Therapy

2017· article· en· W2764335002 on OpenAlexaff
Melissa Burgess Moser, Susan M. Johnson, Stephanie A. Wiebe, Giorgio A. Tasca

Bibliographic record

VenueJournal of Marital and Family Therapy · 2017
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsSofteningPsychologyDistressAnxietyAttachment theoryClinical psychologyMultilevel modelPsychotherapistDevelopmental psychologyMaterials sciencePsychiatryComposite material

Abstract

fetched live from OpenAlex

Emotionally Focused Couples Therapy (EFT; Johnson, ) treats relationship distress by targeting couples' relationship-specific attachment insecurity. In this study, we used hierarchical linear modeling (Singer & Willett, ) to examine intercept and slope discontinuities in softened couples' trajectories of change in relationship satisfaction and relationship-specific attachment over the course of therapy from a total sample of 32 couples. Softened couples (n = 16) reported a significant increase in relationship satisfaction and a significant decrease in attachment avoidance at the softening session. Although softened couples displayed an initial increase in relationship-specific attachment anxiety at the softening session, their scores significantly decreased across post-softening sessions. Results demonstrated the importance of the blamer-softening change event in facilitating change in EFT.

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)0.002
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.047
GPT teacher head0.405
Teacher spread0.358 · 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 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

Citations42
Published2017
Admission routes1
Has abstractyes

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