Impact and clinical significance of a preventive intervention for disruptive boys
Bibliographic record
Abstract
BACKGROUND: Many intervention programmes have attempted to reduce disruptive behaviour problems during early childhood to prevent maladjustment during adolescence and adulthood. AIMS: To assess the long-term impact and clinical significance of a 2-year multicomponent preventive intervention on criminal behaviour and academic achievement, using intention-to-treat analyses. METHOD: Targeted disruptive-aggressive boys considered to be at risk of later criminality and low school achievement (n=250), identified from a community sample (n=895), were randomly allocated to an intervention or a control group. The rest of the sample (n=645) served as the low-risk group. The intervention was multimodal and aimed at boys, parents and teachers. Official data measured both outcomes. RESULTS: Significantly more boys in the intervention group (13%; P<0.05) completed high-school graduation and generally fewer (11%; P=0.06) had a criminal record compared with those allocated to the control group. CONCLUSIONS: The results suggest that early preventive intervention for those at high risk of antisocial behaviour is likely to benefit both the individuals concerned and society.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".