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Record W4205259387 · doi:10.1521/pedi_2012_35_541

Preliminary Steps Toward Extracting the Specific Alternative Model for Personality Disorders Diagnoses From Criteria A and B Self-Reports

2022· article· en· W4205259387 on OpenAlexaff
Dominick Gamache, Philippe Leclerc, Maude Payant, Kristel Mayrand, Marie-Chloé Nolin, Louis-Alexandre Marcoux, Stéphane Sabourin, Marc Tremblay, Claudia Savard

Bibliographic record

VenueJournal of Personality Disorders · 2022
Typearticle
Languageen
FieldPsychology
TopicPersonality Disorders and Psychopathology
Canadian institutionsCentre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-JeanUniversité LavalUniversité Sainte-AnneUniversité du Québec à MontréalCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsMedical diagnosisPersonality disordersPsychologyFacet (psychology)PersonalityClinical psychologyBig Five personality traitsSocial psychologyMedicinePathology

Abstract

fetched live from OpenAlex

The Alternative DSM-5 Model for Personality Disorders (AMPD) retains six specific personality disorders (PDs) that can be diagnosed based on Criterion A level of impairment and Criterion B maladaptive facets. Those specific diagnoses are still underresearched, despite the preference expressed by most PD scholars for a mixed/hybrid classification. This study explores the possibility of using Criterion A and B self-report questionnaires to extract the specific AMPD diagnoses. Plausible prevalence estimates were found in three samples (outpatient PD, private practice, community; N = 766) using the facet score ≥ 2 and t score > 65 methods for determining the presence of a Criterion B facet; diagnoses had meaningful correlations with external variables. This study provides evidence—albeit preliminary—that the extraction of the specific AMPD PDs from self-report questionnaires might be a viable avenue. Ultimately, it could promote the use and dissemination of those diagnoses for screening purposes in clinical and research settings.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.284
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.049
GPT teacher head0.343
Teacher spread0.294 · 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.

Study designQualitative
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

Citations9
Published2022
Admission routes1
Has abstractyes

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