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Record W3156236878 · doi:10.1111/jmft.12503

A latent profile analysis of romantic attachment anxiety and avoidance

2021· article· en· W3156236878 on OpenAlexafffund
Marie‐Pier Vaillancourt‐Morel, Chloé Labadie, Véronique Charbonneau‐Lefebvre PhD candidate, Stéphane Sabourin, Natacha Godbout

Bibliographic record

VenueJournal of Marital and Family Therapy · 2021
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsUniversité du Québec à MontréalUniversité de MontréalUniversité LavalUniversité du Québec à Trois-Rivières
FundersSocial Sciences and Humanities Research CouncilSocial Sciences and Humanities Research Council of CanadaFonds de Recherche du Québec-Société et Culture
KeywordsPsychologyAnxietyDistressClinical psychologyPsychological distressScale (ratio)Structural equation modelingRomanceDevelopmental psychologyPsychiatry

Abstract

fetched live from OpenAlex

We conducted latent profile analyses on community (n = 1663) and clinical (n = 575) samples to determine whether continuous scores of attachment anxiety and avoidance would lead to the identification of theoretically consistent and clinically useful profiles. We then compared these profiles according to gender, relationship status, psychological distress, and relationship satisfaction. Analysis on the community sample yielded four profiles: secure, preoccupied, dismissive, and fearful individuals; whereas, the clinical sample yielded three profiles: secure, preoccupied, and fearful individuals. In the community sample, there was a higher proportion of women under the preoccupied profile and a higher proportion of men under the dismissive profile compared with the other profiles. Overall, insecure individuals reported higher levels of relationship dissatisfaction and psychological distress, and a relationship status reflecting lower commitment. Our findings suggest that the Experiences in Close Relationships scale could be useful in assisting therapists in conceptualizing their cases according to their patients' attachment profile.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.274

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)0.0000.000

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 head0.023
GPT teacher head0.340
Teacher spread0.317 · 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.

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

Citations15
Published2021
Admission routes2
Has abstractyes

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