Sex Differences in the Classification of Conduct Problems: Implications for Treatment
Bibliographic record
Abstract
Abstract Purpose Conduct problem behaviors are highly heterogeneous symptom clusters, creating many challenges in investigating etiology and planning treatment. The aim of this study was to first identify distinct subgroups of males and females with conduct problems using a data driven approach and, secondly, to investigate whether these subgroups differed in treatment outcome after an evidence-based crime prevention program. Methods We used a latent class analysis (LCA) in Mplus` to classify 517 males and 354 females (age 6–11) into classes based on the presence of conduct disorder or oppositional defiance disorder items from the Child Behavior Checklist. All children were then enlisted into the 13-week group core component (children and parent groups) of the program Stop Now And Plan (SNAP®), a cognitive-behavioral, trauma-informed, and gender-specific program that teaches children (and their caregivers) emotion-regulation, self-control, and problem-solving skills. Results The LCA revealed four classes for males, which separated into (1) “rule-breaking,” (2) “aggressive,” (3) “mild,” and (4) “severe” conduct problems. While all four groups showed a significant improvement following the SNAP program, they differed in the type and magnitude of their improvements. For females, we observed two classes of conduct problems that were largely distinguishable based on severity of conduct problems. Participants in both female groups significantly improved with treatment, but did not differ in the type or magnitude of improvement. Conclusion This study presents novel findings of sex differences in clustering of conduct problems and adds to the discussion of how to target treatment for individuals presenting with a variety of different problem behaviors.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".