MétaCan
Menu
Back to cohort
Record W2805911591 · doi:10.1521/pedi.2018.32.3.329

Self-Knowledge in Personality Disorders: An Emotion-Focused Perspective

2018· article· en· W2805911591 on OpenAlexaff
Uëli Kramer, Antonio Pascual‐Leone

Bibliographic record

VenueJournal of Personality Disorders · 2018
Typearticle
Languageen
FieldPsychology
TopicPersonality Disorders and Psychopathology
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsPsychologyPerspective (graphical)Meaning (existential)PsychotherapistPersonalityPersonality disordersMental healthMeaning-makingSection (typography)Cognitive psychologySocial psychology

Abstract

fetched live from OpenAlex

Emotional knowledge about one's own and others' emotional experience are central features of mental health and may be characteristic of therapeutic processes leading to good outcome. Clients with personality disorders (PDs) often lack in their ability to access and accept emotional experiences, or to reflect on emotion and use it in adaptive ways. The present theoretical and clinical review discusses self-knowledge, and lack thereof, in personality disorders, from an emotion-focused perspective. A first section differentiates between two fundamental types of meaning construction processing. The second section describes, from an integrative therapy perspective, how self-knowledge may be facilitated in psychotherapy by the client-therapist interaction. A subsequent section discusses lack of awareness of one's own emotions in the construction of meaning associated with PDs. The final section describes initial studies that demonstrate change occurring in constructing emotional self-knowledge as a correlate of treatment. The concepts of the review are illustrated throughout with three clinical cases of PD.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0010.004
Scholarly communication0.0030.003
Open science0.0010.001
Research integrity0.0020.002
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 head0.028
GPT teacher head0.357
Teacher spread0.329 · 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 designTheoretical or conceptual
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

Citations21
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Personality DisordersSame topicPersonality Disorders and PsychopathologyFrench-language works237,207