Latent Profiles of Externalizing Psychopathology and Their Relation to Children's Aggression and Social Behavior
Bibliographic record
Abstract
OBJECTIVE: This study identified profiles of clinic-referred children with disruptive behavior and determined the association between identified profiles and children's aggression, peer problems, and prosocial skills. METHOD: Parents and teachers of 208 children (163 boys) aged 6 to 12 years (Mage = 8.80, SD = 1.75) completed measures to assess children's callous-unemotional (CU) traits, inattentive-impulsive-overactive (IO) and oppositional-defiant (OD) behavior, aggression, and social behaviors. Latent class analysis was used to identify the profiles, and the pseudoclass draw method to test the equality of means for each of the aggression and social behavioral outcomes across the latent classes. RESULTS: Five profiles were identified: (1) Low (35.6% of children), with relatively low levels of CU traits and IO and OD behavior; (2) Low-Moderate (30.8%), with low-moderate levels of CU traits, low IO and moderate OD behavior; (3) Moderate (21.6%), with moderate levels of CU traits and IO and moderate-high OD behavior; (4) Moderate-High (7.2%), with moderate-high levels of CU traits, high IO and moderate-high OD behavior; and (5) High (4.8%), with high levels of CU traits, IO and OD behavior. CONCLUSION: Children categorized into profiles showed important differences in level of aggression and social behavior. The overlap between CU traits, IO, and OD behavior add to understanding of child psychopathology that influences behavior and clinical outcomes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".