A dimensional latent variable model approach to connecting psychopathology and neurocognition hierarchies.
Bibliographic record
Abstract
= 715 female) with current mental health concerns participated in this online research study and completed questionnaires of dimensional psychopathology and comprehensive neuropsychological testing using measures with previously established latent hierarchical structures. A series of confirmatory and exploratory higher-order, bifactor, and correlated factors models were tested. Hierarchical regressions and structural models were used to test associations between psychopathology and neurocognition dimensions. An exploratory six-factor bifactor model (general psychopathology, harmful substance use, anxiety, detachment, depression, posttraumatic stress) and a confirmatory five-factor model of psychopathology (general psychopathology, internalizing, externalizing, thought, detachment plus method factor) emerged. An exploratory three-factor bifactor model of neurocognition (general neurocognition, executive function, and social cognition) was retained. Hierarchical regressions revealed a significant negative association of general psychopathology with general neurocognition. Detachment was associated with a further decrement in general neurocognition and social cognition. A positive association was found between anxiety and social cognition. Within a structural model between the five-factor bifactor model of psychopathology and three-factor bifactor model of neurocognition, only the association between detachment and general neurocognition remained significant. Higher levels of detachment are most consistently associated with decrements in general neurocognition across different models. (PsycInfo Database Record (c) 2026 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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".