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Record W198443639

The joint hierarchical structure of adolescent personality pathology: converging evidence from two approaches to measurement.

2013· article· en· W198443639 on OpenAlexaffabout
Shauna C. Kushner, Jennifer L. Tackett, Barbara De Clercq

Bibliographic record

VenuePubMed · 2013
Typearticle
Languageen
FieldPsychology
TopicPersonality Disorders and Psychopathology
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPersonality pathologyPsychologyWrightConceptualizationPersonalityPsychoticismPersonality disordersClinical psychologyEmotional dysregulationBig Five personality traitsCognitive psychologyDevelopmental psychologySocial psychologyArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: To examine the joint hierarchical structure of two measures of adolescent personality pathology within a community sample of Canadian adolescents. METHOD: Self-reported data on demographic information and pathological personality traits were obtained from 144 youth (M age = 16.08 years, SD = 1.30). Personality pathology was measured using the youth-version of the Schedule for Nonadaptive and Adaptive Personality (SNAP-Y; Linde, Stringer, Simms, & Clark, in press) and the Dimensional Personality Symptom Item Pool (DIPSI; De Clercq, De Fruyt, Van Leeuwen, & Mervielde, 2006). Lower-order scales were subjected to structural hierarchical analyses. RESULTS: Scales from the two measures were complementary in defining higher-order traits. Traits at the 4-factor level of the hierarchy (Need for Approval, Disagreeableness, Detachment, and Compulsivity) showed similarities and differences with previous results in adults. CONCLUSIONS: The current investigation integrated top-down and bottom-up measures for a comprehensive account of the higher-order hierarchy of adolescent personality pathology. Results are discussed in the context of convergence across approaches and in comparison with previous findings in adult samples.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.590
Threshold uncertainty score0.689

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.208
GPT teacher head0.287
Teacher spread0.079 · 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

Citations21
Published2013
Admission routes2
Has abstractyes

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