EFFECTIVENESS OF COGNITIVE–BEHAVIOURAL TREATMENT IN CHILDREN WITH CONDUCT DISORDERS WITH CO-MORBID ANXIETY AND DEPRESSION
Bibliographic record
Abstract
Objective To evaluate empirically the effectiveness of a cognitive–behavioural treatment programme for children with conduct disorder and concomitant anxiety or mood disorders. Method Participants included 46 children in 2005 (40 boys and six girls, M = 9.8 years; SD 1.7) and 36 children in 2006 (30 boys and six girls, M = 10.4 years; SD 1.4). The children were referred by school personnel to a community mental health treatment centre to participate in a cognitive–behaviour-based programme. The goals of the programme included: teaching children how to manage anger, control their impulsivity, think about the consequences of their behaviour and otherwise develop more socially appropriate behaviours. Outcome measures included: degree of externalised symptoms (aggressiveness, opposition, hyperactivity and attention), internalised symptoms (anxiety and depression) and adjustment problems (social and school performance) and clinical judgement of goal attainment leading to discharge from the agency (or referred to another service). Results In 2005, 26 children (56.5%) were found to have achieved programme goals. Programme attendance was significantly associated with a decrease in externalised symptoms (T = 3.2, p Conclusion Cognitive–behavioural family intervention has beneficial effects on reducing symptoms in children with conduct disorder.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".