A thorough investigation of the bifactor model of psychopathology in a representative birth cohort: Testing internal and predictive validity to inform models of comorbidity.
Bibliographic record
Abstract
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).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".