Association of childhood externalizing, internalizing, comorbid problems with criminal convictions by early adulthood
Bibliographic record
Abstract
Childhood externalizing problems have been linked with adult criminality. However, little is known about criminal outcomes among children with comorbid externalizing and internalizing problems. We examined the associations between profiles of behavioral problems during childhood (i.e., externalizing, internalizing, and comorbid) and criminality by early adulthood. Participants were N = 3017 children from the population-based Quebec Longitudinal Study of Kindergarten Children followed up from age 6-25. Multitrajectory modeling of teacher-rated externalizing and internalizing problems from age 6-12 years identified four distinct profiles: no/low, externalizing, internalizing, and comorbid problems. Juvenile (age 13-17) and adult (age 18-25) criminal convictions were extracted from official records. Compared to children in the no/low profile, those in the externalizing and comorbid profiles were at higher risk of having a criminal conviction, while no association was found for children in the internalizing profile. Children with comorbid externalizing and internalizing problems were most at risk of having a criminal conviction by adulthood, with a significantly higher risk when compared to children with externalizing or internalizing problems only. Similar results were found when violent and non-violent crimes were investigated separately. Specific interventions targeting early comorbid behavioral problems could reduce long-term criminality.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".