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Record W1945011203 · doi:10.1002/jclp.22044

Adult Attachment Anxiety: Using Group Therapy to Promote Change

2013· article· en· W1945011203 on OpenAlexafffund
Cheri L. Marmarosh, Giorgio A. Tasca

Bibliographic record

VenueJournal of Clinical Psychology · 2013
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsUniversity of OttawaOttawa Hospital
FundersCanadian Institutes of Health Research
KeywordsPsychologyAnxietyGroup psychotherapyAttachment theoryGroup cohesivenessInterpersonal communicationClinical psychologyPerceptionInterpersonal relationshipPsychotherapistDevelopmental psychologyPsychiatrySocial psychology

Abstract

fetched live from OpenAlex

Group therapy can facilitate changes for members with greater attachment anxiety who tend to struggle with negative self-perceptions, difficulties regulating emotions, poor reflective functioning, and compromised interpersonal relationships. A clinical example of a therapy group with members who had elevated attachment anxiety and who were diagnosed with binge eating disorder demonstrates how attachment theory can be applied to group treatment. The clinical material from the beginning, middle, and end of group is presented to highlight how attachment anxiety influences members' emotional reactions and behaviors in the group, how group factors facilitate change, and how the leader fosters the development of a secure base within the group. Pre- to posttreatment outcomes indicate positive changes in binge eating, depressive symptoms, and attachment avoidance and anxiety. To facilitate change in individuals with greater attachment anxiety, group therapists may foster a secure base in the group through group cohesion, which will facilitate down regulation of emotions, better reflective functioning, and relationships that are less preoccupied with loss and more secure.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.250
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.277
GPT teacher head0.578
Teacher spread0.302 · 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; both teacher heads agree on what is shown here.

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

Citations50
Published2013
Admission routes2
Has abstractyes

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