Early risk factors associated with preschool developmental patterns of single and co-occurrent disruptive behaviors in a population sample.
Bibliographic record
Abstract
= 2,057; 50.7% boys). Six high-trajectory classes obtained by latent growth modeling were used as longitudinal indicators of single-DB and co-occurrent DBs. Children following low or moderate trajectories for all DBs served as the reference class. Results showed low commonality of risk factors among single-DB trajectory classes, suggesting that "pure" forms of DBs have specific etiologies. In contrast, the trajectory classes with a high DB in common shared 20.0% to 46.7% of their risk factors. Overall, 40.0% of significant risk factors across trajectory classes were common to between two and four classes, whereas 60.0% of the significant risk factors were specific to one class or another. However, risk factors common among classes accounted for the greater part (63.2%) of the associations, especially in co-occurrent DBs trajectory classes. These risk factors included male sex, a higher number of siblings, maternal symptoms of depression and conduct problems, young motherhood, lack of positive parenting, family dysfunction, and lower socioeconomic status. Children thus develop early distinct patterns of DBs associated with both common and specific prenatal and early postnatal risk factors. Longitudinal assessments of early manifestations of DB, including a range of behaviors and a variety of potential risk factors to reflect the distinctiveness of children and their families, could help guide etiological research, tailor early interventions, and prevent a cascade of deleterious influences and outcomes. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".