Childhood Disruptive Behaviour Disorders: Review of Their Origin, Development, and Prevention
Bibliographic record
Abstract
OBJECTIVE: To review preventive studies of disruptive behaviour disorders (DBDs) in light of recent empirical knowledge on their development. METHOD: We draw on the results of longitudinal studies of children starting in infancy to examine the onset, development, and risk factors for DBD symptoms. We review randomized controlled trials of preventive interventions provided to families before the child is aged 3 years, with reported outcome measures of DBD symptoms at follow-up. RESULTS: Children who present high levels of DBD symptoms start to do so in the first 2 years of life and have risk factors that can be identified in the mother during pregnancy or even earlier, and shortly after the child's birth. Most preventive experiments have started relatively late after birth and have targeted parenting, with weak effects on children's DBDs. Preventive experiments that have provided intensive intervention to at-risk mothers starting during pregnancy have shown important effects in reducing key risk factors and some of the most severe consequences of DBDs. However, even those experiments have not succeeded in preventing childhood DBDs in the home and school contexts. CONCLUSIONS: We suggest adopting a sequential, multitarget, intergenerational, experimental approach both to increase our knowledge about causal mechanisms and to increase our effectiveness in curbing DBDs and their serious lifelong consequences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".