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Record W4321452868 · doi:10.1037/abn0000816

A thorough investigation of the bifactor model of psychopathology in a representative birth cohort: Testing internal and predictive validity to inform models of comorbidity.

2023· article· en· W4321452868 on OpenAlexafffund
Nina Pocuca, Marie‐Claude Geoffroy, Stéphane Paquin, Kim Archambault, Jean R. Séguin, Sophie Parent, Michel Boivin, Richard E. Tremblay, Sylvana Côté, Natalie Castellanos‐Ryan

Bibliographic record

VenueJournal of Psychopathology and Clinical Science · 2023
Typearticle
Languageen
FieldPsychology
TopicMental Health Research Topics
Canadian institutionsUniversité LavalMcGill UniversityDouglas Mental Health University InstituteUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
FundersCanadian Centre on Substance Use and AddictionFonds de Recherche du Québec - SantéSocial Sciences and Humanities Research Council of CanadaInstitut de la statistique du QuébecMinistère de la Santé et des Services sociauxCanadian Institutes of Health ResearchFondation Lucie et Andre ChagnonFonds de Recherche du Québec-Société et Culture
KeywordsPsychopathologyComorbidityPsychologyMental healthClinical psychologyStructural equation modelingCohortPsychiatryMedicine

Abstract

fetched live from OpenAlex

This study used symptom dimensions reflecting DSM-V internalizing, externalizing, eating disorders, and substance use (SU) and related problems to thoroughly investigate the structure of psychopathology in mid-adolescence (15 and 17 years, N = 1,515, 52% female). Compared to other hierarchical configurations (unidimensional, correlated factors, or higher-order model), a bifactor model of psychopathology wherein all first-order symptom dimensions loaded onto a second-order general psychopathology factor (P factor) and one of three, second-order specific internalizing, externalizing, or SU factors, best captured the structure of the psychopathology in mid-adolescence. This bifactor model was then used to predict several distinct mental health disorders and alcohol use disorder (AUD) at 20 years, via a structural equation model (SEM). The P factor (bifactor model) was associated with all but one outcome (suicidal ideation without an attempt), at 20 years. Controlling for the P factor, there were no additional, positive, temporal cross-associations (i.e., between mental health (mid-adolescence) and AUD at 20 years, or between SU (mid-adolescence) and mental health problems at 20 years). These results are bolstered by findings from a well-fitting correlated factors model. Namely, when mid-adolescent psychopathology was modeled using an adjusted correlated factors model, associations with outcomes at 20 years were largely masked, with no significant partial, temporal cross-associations. Thus, collectively, findings indicate that comorbidity between SU and mental health in youth may be largely attributable to an underlying liability to experience both problems (i.e., P factor). Ultimately, results support targeting the common liability to psychopathology in the prevention of later mental health problems and AUD. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

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)0.021
metaresearch head score (Gemma)0.051
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 score0.070
Threshold uncertainty score0.140

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.051
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.003
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.002
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 head0.340
GPT teacher head0.518
Teacher spread0.178 · 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

Citations3
Published2023
Admission routes2
Has abstractyes

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