Multiple developmental pathways underlying conduct problems: A multitrajectory framework
Bibliographic record
Abstract
Abstract In the past decades, there has been an overemphasis of a descriptive/behavioral approach to study conduct disorder. In an equifinal perspective, we aimed to examine the developmental multitrajectory groups of psychological features (irritability, interpersonal callousness, hyperactivity/impulsivity, and depressive–anxiety symptoms) and their associations with conduct problems. In a population-based cohort (n = 1,309 participants followed from 5 months to 17 years old), latent-class growth analysis was performed for each psychological feature to identify a two-trajectory model (from ages 6 to 12 years). Based on parameter estimates of the two-trajectory models for each of the four psychological features, a parallel process growth mixture model identified eight significant developmental patterns that were subsequently compared with typically developing children. Furthermore, we observed that while interpersonal callousness conferred an increased risk for childhood and adolescence conduct problems, its co-occurrence with hyperactivity/impulsivity, irritability, and/or depressive–anxiety symptoms heightened the general risk, but also predicted distinct subtypes of conduct problems (i.e., aggressive and rule-breaking behaviors). Thus, by studying complex developmental combinations of psychological features, we observed qualitatively distinct pathways towards conduct problems. A multitrajectory framework of psychological features should be considered as a significant step towards unveiling the multiple etiological pathways leading to conduct disorder and its substantial clinical heterogeneity.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".