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Record W2107236318 · doi:10.1521/pedi.2008.22.4.389

Structure of Personality Pathology in Normal and Clinical Samples: Spanish Validation of the DAPP-BQ

2008· article· en· W2107236318 on OpenAlexaff
Jose Alfonso Gutiérrez-Zotes, Fernando Gutiérrez, Joaquı́n Valero, E. De Frutos Gallego, Eva Baillés, Xavier Torres, Antonio Labad, W. John Livesley

Bibliographic record

VenueJournal of Personality Disorders · 2008
Typearticle
Languageen
FieldPsychology
TopicPersonality Disorders and Psychopathology
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPsychologyPersonality pathologyPersonalityPersonality disordersClinical psychologyPsychometricsPopulationDSM-5Emotional dysregulationConstruct validityDevelopmental psychologySocial psychologyMedicine

Abstract

fetched live from OpenAlex

Given that the DSM taxonomy of personality disorders is flawed by severe classificatory problems, the development of alternative classificatory systems, such as the Dimensional Assessment of Personality Pathology-Basic Questionnaire (DAPP-BQ), has now become a priority. This study examined the internal consistency, second-order factor structure, and criterion validity of a Spanish translation of the DAPP-BQ in two samples: subjects with personality disorder (n = 155) and subjects from the general population (n = 300). Alpha coefficients ranged satisfactorily from .75 to .93. Four second-order factors of Emotional Dysregulation, Dissocial Behavior, Inhibitedness, and Compulsivity were obtained, which were replicable between samples and identical to those reported in the literature. Finally, disordered subjects scored significantly higher than normal subjects on 17 of the 18 DAPP-BQ traits. Some pending issues in the construction of an alternative taxonomy of personality disorders are discussed.

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)0.005
metaresearch head score (Gemma)0.012
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 score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.043
GPT teacher head0.343
Teacher spread0.300 · 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

Citations44
Published2008
Admission routes1
Has abstractyes

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