Fifty years of preventing and treating childhood behaviour disorders: a systematic review to inform policy and practice
Bibliographic record
Abstract
QUESTION: Oppositional defiant and conduct disorders (ODD and CD) start early and persist, incurring high individual and collective costs. To inform policy and practice, we therefore asked: What is the best available research evidence on preventing and treating these disorders? STUDY SELECTION AND ANALYSIS: We sought randomised controlled trials (RCTs) evaluating interventions addressing the prevention or treatment of behaviour problems in individuals aged 18 years or younger. Our criteria were tailored to identify higher-quality RCTs that were also relevant to policy and practice. We searched the CINAHL, ERIC, MEDLINE, PsycINFO and Web of Science databases, updating our initial searches in May 2017. Thirty-seven RCTs met inclusion criteria-evaluating 15 prevention programmes, 8 psychosocial treatments and 5 medications. We then conducted narrative synthesis. FINDINGS: reduced behavioural diagnoses. Three other interventions reduced criminal activity. Psychosocial treatment benefits lasted from 1 to 8 years. While 4 medications reduced post-test symptoms, all caused important adverse events. CONCLUSIONS: Considerable RCT evidence favours prevention. CLINICAL IMPLICATIONS: Effective prevention programmes should therefore be made widely available. Effective psychosocial treatments should also be provided for all children with ODD/CD. But medications should be a last resort given associated adverse events and given only short-term evidence of benefits. Policymakers and practitioners can help children and populations by acting on these findings.
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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 | 0.037 | 0.123 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| 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".