MétaCan
Menu
Back to cohort
Record W2902482571 · doi:10.6092/2282-1619/2018.6.1848

Measuring Grandiose and Vulnerable Narcissism in Adolescents

2018· article· en· W2902482571 on OpenAlexaff
Simon Chrétien, Karin Ensink, Jean Descôteaux, Lina Normandin

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2018
Typearticle
Languageen
FieldPsychology
TopicPersonality Traits and Psychology
Canadian institutionsUniversité de SherbrookeUniversité Laval
Fundersnot available
KeywordsNarcissismPsychologyPathologicalPsychopathologyClinical psychologyDevelopmental psychologySocial psychologyMedicineInternal medicine

Abstract

fetched live from OpenAlex

The Pathological Narcissism Inventory (PNI) has been widely used with adults. Its vulnerable and grandiose dimensions have been differentially associated with psychopathology and interpersonal difficulties. While the PNI has been used with adolescents, its structure and correlates remain to be investigated. The aim of this study was to examine the psychometric properties of the French PNI for adolescents and its association with indices of dysfunction. A total of 570 adolescents completed the PNI, the Youth Self Reportto assess internalizing and externalizing difficulties, and the Self-Perception Profile for Adolescents to assess self-esteem. Results showed that the first and second-order factor structure of the PNI for adolescents is identical to the one found in adults. Temporal stability at one month was good. Between gender differences, as well as correlations between PNI dimensions, internalizing and externalizing difficulties, and self-esteem further add to the conclusion that the French PNI-A has good psychometric properties. Key words: adolescence, narcissism, measure, french, pathological narcissism inventory

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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.417
GPT teacher head0.588
Teacher spread0.171 · 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

Citations6
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueDOAJ (DOAJ: Directory of Open Access Journals)Same topicPersonality Traits and PsychologyFrench-language works237,207