MétaCan
Menu
Back to cohort
Record W3087179581 · doi:10.1097/nmd.0000000000001230

Defense Mechanisms, Gender, and Adaptiveness in Emerging Personality Disorders in Adolescent Outpatients

2020· article· en· W3087179581 on OpenAlexaff
Mariagrazia Di Giuseppe, J. Christopher Perry, Ciro Conversano, Omar Carlo Gioacchino Gelo, Alessandro Gennaro

Bibliographic record

VenueThe Journal of Nervous and Mental Disease · 2020
Typearticle
Languageen
FieldPsychology
TopicPersonality Disorders and Psychopathology
Canadian institutionsMcGill UniversityJewish General Hospital
Fundersnot available
KeywordsPersonalityPsychologyPersonality disordersClinical psychologyPsychopathologyBig Five personality traitsPersonality pathologyPersonality Assessment InventoryDevelopmental psychologySocial psychology

Abstract

fetched live from OpenAlex

The present study focused on demographic and personality differences in the use of 30 defense mechanisms in adolescents with personality psychopathology and explored the hierarchical organization of personality traits based on the adaptiveness of defensive functioning. A total of 102 self-referred adolescent outpatients were interviewed and assessed on defense mechanisms and personality traits using the Defense Mechanisms Rating Scales and the Shedler-Westen Assessment Procedure 200 for Adolescents, respectively. Age and gender differences were found throughout the hierarchy. Pearson's correlations revealed a hierarchical organization of emerging personality disorders (PDs) in adolescence. More adaptive defenses were clearly associated with healthier personality style, whereas more pathological personality styles such as those with borderline traits were characterized by more rigid and maladaptive defenses. Dissociation was also associated with maladaptive personality types. Identifying the defenses associated with emerging personality disorders may inform the unconscious function of defense mechanisms in specific PDs. The systematic assessment of defense mechanisms might also help therapists to monitor changes during treatment.

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 score0.121
Threshold uncertainty score0.414

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 head0.043
GPT teacher head0.307
Teacher spread0.263 · 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

Citations51
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of Nervous and Mental DiseaseSame topicPersonality Disorders and PsychopathologyFrench-language works237,207