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Record W3038764689 · doi:10.1002/jclp.23021

Personality correlates of eating pathology severity and subtypes in The National Comorbidity Survey Adolescent Supplement

2020· article· en· W3038764689 on OpenAlexafffund
Nicole K. Legg, Brianna J. Turner

Bibliographic record

VenueJournal of Clinical Psychology · 2020
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsUniversity of Victoria
FundersInstitute of Neurosciences, Mental Health and AddictionNational Institute of Mental HealthSubstance Abuse and Mental Health Services AdministrationNational Institute on Drug AbuseRobert Wood Johnson Foundation Center for Health PolicyJohn W. Alden Trust
KeywordsImpulsivityBulimia nervosaBinge eatingDisinhibitionPsychologyAnorexia nervosaSubclinical infectionComorbidityEating disordersClinical psychologyBinge-eating disorderPersonalityPersonality pathologyPsychiatryBig Five personality traitsPersonality disordersMedicineInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: We examined how personality traits are associated with eating pathology (EP) across a range of severities and symptom subtypes. METHOD: The National Comorbidity Survey Adolescent Supplement (N = 10,148) was used to compare personality across severities (i.e., clinical Eating Disorders [EDs], subclinical disordered eating, preclinical weight concerns, or no weight/EP [no W/EP]), subclinical subtypes (i.e., Binge Eating Only, Binge-Purge, Binge-Restrict, Restrict-Purge, or no W/EP), and clinical subtypes (i.e., Anorexia Nervosa [AN]), Bulimia Nervosa [BN], Binge Eating Disorder [BED], internalizing disorders, or no ED or internalizing disorder) of EP. RESULTS: More severe EP was associated with more extreme personality trait endorsements. Impulsivity-related traits did not consistently distinguish binge eating/purging from restricting subtypes, although behavioral disinhibition differentiated adolescents with BN or BED from AN. CONCLUSION: Personality traits related to affectivity and impulsivity were more consistently associated with severity rather than subtype of EP.

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)0.007
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.014
Threshold uncertainty score0.499

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.297
GPT teacher head0.508
Teacher spread0.210 · 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

Citations10
Published2020
Admission routes2
Has abstractyes

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