Effectiveness of Day Treatment for Disruptive Behaviour Disorders: What is the Long-term Clinical Outcome for Children?
Bibliographic record
Abstract
OBJECTIVE: The present study investigates the clinical long-term outcomes (2½ to 4 years post-discharge) of children aged 12 and under with a primary diagnosis of a Disruptive Behaviour Disorder (DBD) who attended a short-term day treatment program using best-practice treatment strategies. This study compared children's admission, discharge, and follow-up test scores on standardized measures of behaviour and functioning, as rated by parents. METHOD: Measures of clinical symptoms in the children and parent report of stress were used. To test for treatment effects across time, two repeated-measures ANOVAs were calculated. RESULTS: There was significant treatment change across time points on measures of social problems, externalizing symptoms, levels of aggression, intensity of problems, and symptoms of ADHD. CONCLUSIONS: Children with DBD who attended a short-term day treatment program using best-practice treatment strategies showed significant improvement in their behaviour at home. These improvements were relatively long lasting. The current study lends support to the effectiveness of day treatment and the idea that severe DBD can be treated using multi-modal, intensive, and evidence-based treatment techniques resulting in long-term change.
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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 | 0.002 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".