Psychosocial Interventions for Children and Adolescents with Conduct Problems
Bibliographic record
Abstract
Conduct problems (CP) encompass antisocial behaviors that violate others’ rights and/or societal norms. In this chapter, we describe evidence-based psychosocial interventions targeting CP during childhood and adolescence. Specifically, we outline the theoretical underpinnings and intervention components of family-based interventions and multicomponent interventions, including interventions specifically targeting high-risk personality traits for CP (e.g., callous-unemotional traits and psychopathy). We briefly synthesize efficacy and effectiveness research on intervention effects in relation to CP, gun violence, gang affiliation, and criminal legal system involvement. We also describe the current evidence testing logic models (including mediating and moderating models) and we discuss the effective components of psychosocial interventions. Finally, we provide recommendations for future research to address youth gun violence. Ultimately, evidence supports the utility of psychosocial interventions for improving CP, especially when they are tailored to the needs of individuals. However, the field currently lacks evidence supporting the efficacy of these psychosocial interventions to reduce gun violence among children and adolescents. This is a critical next frontier for the field.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.007 | 0.001 |
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".