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Record W4232083398 · doi:10.24124/2020/59099

Mentalization and interpersonal problems in borderline personality disorder (BPD) traits

2020· dissertation· en· W4232083398 on OpenAlexaff
Melanie Adamsons

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldPsychology
TopicPersonality Disorders and Psychopathology
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBorderline personality disorderMentalizationPsychologyInterpersonal communicationIntrapersonal communicationMediationInterpersonal relationshipClinical psychologyDevelopmental psychologySocial psychology

Abstract

fetched live from OpenAlex

Difficulties in mentalization may be a developmentally based foundation for interpersonal problems in Borderline Personality Disorder (BPD). Fonagy and colleagues have developed a theoretical framework whereby relationships between difficulties in mentalization and other core characteristics of BPD (i.e., insecure attachment, intrapersonal emotion dysregulation and identity diffusion) may underlie interpersonal problems. However, most of the published work on these aspects of the framework have been theoretical in nature. The aim of the study was to investigate this framework and extend it by including interpersonal emotion dysregulation. Simple and multiple mediation analyses were performed with a convenience sample of 64 undergraduate students. Results indicated that hypomentalizing mediated the relationship between BPD symptoms and interpersonal problems. No significant mediators were found between insecure attachment and interpersonal problems or between mentalization errors and interpersonal problems. Limitations include the sample size and the lack of a negative emotion induction and recommendations for future research are suggested.

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)0.003
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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.324
Teacher spread0.303 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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