Developmental Targeted Prevention of Conduct Disorder
Bibliographic record
Abstract
Abstract Traditionally, the term targeted prevention refers to interventions designed to prevent the development of adjustment problems in individuals by reducing risk factors or by implementing protective factors identified in studies of human development. Because risk and protective factors vary with development, a developmental perspective is necessary in order to identify which factors are most relevant at each period of life, based on well-defined and empirically supported etiological models. Moreover, because prevention strategies vary greatly depending on the factors that are targeted at different developmental periods and ages, a developmental perspective suggests that they need to be shaped accordingly. A further expansion of the concept of developmental targeted prevention includes the notion of “stepwise continuous prevention” for the extreme cases who do not revert to normative behavior during a given developmental period. This notion draws on the chronic-disease model of conduct problems and encompasses several developmental periods. The current debate around these issues is important as they apply to the prevention of conduct problems in youth by targeting risk factors during maternal pregnancy, early childhood, childhood, and adolescence. A consensual view of developmental targeted prevention is, however, necessary for prevention efforts to be coordinated and fruitful.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".