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Record W1563715206 · doi:10.1177/070674370905400406

Dimensions of Personality: Clinicians' Perspectives

2009· article· en· W1563715206 on OpenAlexvenueno aff
Stephanie N. Mullins‐Sweatt, Vera Smit, Roel Verheul, John M. Oldham, Thomas A. Widiger

Bibliographic record

VenueThe Canadian Journal of Psychiatry · 2009
Typearticle
Languageen
FieldPsychology
TopicPersonality Disorders and Psychopathology
Canadian institutionsnot available
Fundersnot available
KeywordsPersonalityPsychologyPersonality disordersClinical psychologyPersonality Assessment InventoryRating scaleBig Five personality traitsPersonality pathologyInclusion (mineral)PsychometricsSocial psychologyDevelopmental psychology

Abstract

fetched live from OpenAlex

OBJECTIVE: To obtain the opinions and preferences of practising clinicians about the clinical utility of personality scales included within 8 alternative dimensional models of personality disorder for inclusion within an official diagnostic nomenclature. METHOD: Psychiatrists (n = 226) and psychologists (n = 164) from 2 continents provided clinical utility ratings on personality scales organized from 8 alternative dimensional models of personality disorder. RESULTS: The psychiatrists and the psychologists supported the inclusion of most of the scales from all 8 of the models that were compared. Normal personality traits were endorsed, although abnormal personality traits generally received higher levels of endorsement. The list of endorsed traits was reduced further by organizing the scales into groups based on redundancy within each of 5 broad domains and then selecting within each group the scale that received the highest rating. CONCLUSIONS: This list appears to represent each domain in a manner that is comprehensive both in its coverage of the respective domain, as well as in representing particular strengths of each of the alternative dimensional models, at least for the stated preferences of psychiatrists and psychologists.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.546
Threshold uncertainty score1.000

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 science0.0000.000
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 head0.026
GPT teacher head0.324
Teacher spread0.297 · 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 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

Citations13
Published2009
Admission routes1
Has abstractyes

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