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Record W4293851137 · doi:10.1097/nmd.0000000000001569

Exploring the Relationship Between Attachment and Pathological Personality Trait Domains in an Outpatient Psychiatric Sample

2022· article· en· W4293851137 on OpenAlexaff
Phillip Radetzki, Andrew J. Wrath, Lachlan A. McWilliams, Trevor R. Olson, Stephen Adams, Dawn De Souza, Bienca Lau, G. Camelia Adams

Bibliographic record

VenueThe Journal of Nervous and Mental Disease · 2022
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsSaskatchewan Health AuthoritySaskatchewan HealthUniversity of Saskatchewan
Fundersnot available
KeywordsInsecure attachmentPersonalityPsychologyAttachment theoryClinical psychologyTraitAnxietyPathologicalPersonality pathologyPositive affectivityBig Five personality traitsNegative affectivityPersonality disordersPsychiatryMedicineSocial psychologyPathology

Abstract

fetched live from OpenAlex

ABSTRACT: The current study investigates the relationship between insecure attachment and pathological personality trait domains in a sample of psychiatric outpatients. Participants ( N = 150) completed measures for attachment and personality. Bivariate correlations and multiple regression analyses investigated the extent to which insecure attachment and personality pathology were associated. Insecure attachment positively correlated with overall personality pathology, with attachment anxiety having a stronger correlation than attachment avoidance. Distinct relationships emerged between attachment anxiety and negative affectivity and attachment avoidance and detachment. Insecure attachment and male sex predicted overall personality pathology, but only attachment anxiety predicted all five trait domains. Insecure attachment might be a risk factor for pathological personality traits. Assessing attachment in clinical contexts and offering attachment-based interventions could benefit interpersonal outcomes.

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)0.001
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
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 head0.142
GPT teacher head0.389
Teacher spread0.247 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

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