The Structure of Psychopathology in Early Adolescence: Study of a Canadian Sample
Bibliographic record
Abstract
OBJECTIVE: The current study investigates the correlational structure of psychopathology in a large sample of Canadian adolescents and highlights the association between the psychopathological dimensions and gender. METHOD: Data came from 3826 Canadian adolescents aged 12.8 ± 0.4 y. Five alternative dimensional models were tested using confirmatory factor analysis, and the association between gender, language, and the mean level of psychopathological dimensions was examined using a multiple-indicators multiple-causes model. RESULTS: A bifactor model with 1 general psychopathology factor and 3 specific dimensions (internalizing, externalizing, thought disorder) provided the best fit to the data. Results indicated metric invariance of the bifactor structure with respect to language. Females reported higher mean levels of internalizing, and males reported higher mean levels of externalizing. No significant sex differences emerged in liability to thought disorder or general psychopathology. The presence of a general psychopathology factor increased the association between gender and specific dimensions. CONCLUSIONS: The current study is the first to highlight the bifactor structure including a specific thought disorder factor in a Canadian sample of adolescents. The findings further highlight the importance of transdiagnostic approaches to prevention and intervention among young adolescents.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".