Effectiveness of psychosocial intervention for children and adolescents with comorbid problems: a systematic review
Bibliographic record
Abstract
BACKGROUND: Comorbidity is common among child clinical samples. Reviews on effective intervention for comorbid problems are lacking. METHOD: Based on a literature search of three databases (PsycINFO, MEDLINE and ERIC), initial data analysis was carried out on 865 studies; of these,10 randomised trials fully met study inclusion criteria and were subject to final analysis, with quality assessments and effect sizes calculated. RESULTS: Overall, effect sizes for externalising (M = 1.12) and internalising (M = 1.09) outcomes were large. Effect sizes were large for family-based (M = 1.80) compared to individual (M = 0.78) and group-based (M = 0.54) interventions. Studies with homotypic comorbidity (M= 1.18) displayed larger treatment effect sizes than ones with heterotypic comorbidity (M = 0.54). CONCLUSIONS: While the overall quality ratings of the reviewed studies varied from mediocre to good, with a variety of measures used across studies to assess the same outcomes, findings suggest that current interventions are effective for reducing internalising and externalising problems in children with comorbidity. More substantive evidence for the beneficial effects of psychosocial interventions for children with comorbid problems may arise as more robust studies, which more explicitly address and describe comorbidity, become available.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".