Infant temperament prospectively predicts general psychopathology in childhood
Bibliographic record
Abstract
Abstract Recent models of psychopathology suggest the presence of a general factor capturing the shared variance among all symptoms along with specific psychopathology factors (e.g., internalizing and externalizing). However, few studies have examined predictors that may serve as transdiagnostic risk factors for general psychopathology from early development. In the current study we examine, for the first time, whether observed and parent-reported infant temperament dimensions prospectively predict general psychopathology as well as specific psychopathology dimensions (e.g., internalizing and externalizing) across childhood. In a longitudinal cohort (N= 291), temperament dimensions were assessed at 4 months of age. Psychopathology symptoms were assessed at 7, 9, and 12 years of age. A bifactor model was used to estimate general, internalizing, and externalizing psychopathology factors. Across behavioral observations and parent-reports, higher motor activity in infancy significantly predicted greater general psychopathology in mid to late childhood. Moreover, low positive affect was predictive of the internalizing-specific factor. Other temperament dimensions were not related with any of the psychopathology factors after accounting for the general psychopathology factor. The results of this study suggest that infant motor activity may act as an early indicator of transdiagnostic risk. Our findings inform the etiology of general psychopathology and have implications for the early identification for children at risk for psychopathology.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".