Development and Prediction of Hyperactive Symptoms From 2 to 7 Years in a Population-Based Sample
Bibliographic record
Abstract
OBJECTIVES: Children with hyperactive symptoms are often referred to mental health services. Given the frequency and persistent nature of hyperactivity, it is important to better understand its developmental course. This study identified the different developmental trajectories of hyperactive symptoms from 2 to 7 years and tested early predictors of high-level and persistent hyperactivity. These data may lead to earlier detection of at-risk children and to more effective interventions that take into account developmental considerations. PARTICIPANTS: Four data-collection cycles of a nationwide survey of Canadian children were used to track the early development of hyperactivity. Children were 0 to 23 months at the first cycle in 1994 and 6 to 7 years at the fourth cycle in 2000. OUTCOME MEASURES: Hyperactivity data were gathered from mothers on a biennial basis beginning when children were 24 months old. Information on potential prenatal and postnatal predictors was gathered from mothers at the first cycle. DESIGN: Group-based semiparametric mixture modeling was used to estimate developmental trajectories, and logistic-regression analysis identified predictors of hyperactivity. RESULTS: Four trajectories of hyperactive symptoms were identified: very low, low, moderate, and high. Statistically significant predictors for high and persistent hyperactivity, after controlling for all other factors, were maternal prenatal smoking, child male gender, maternal depression, and hostile parenting. CONCLUSIONS: For the majority of children, the frequency of hyperactive symptoms decreased or remained low from 2 to 7 years. However, 7 children in 100 were classified as having high initial levels of hyperactive symptoms that persisted over time. Several prenatal and early postnatal risk factors identified these children, although additional variables will need to be identified to accurately predict high and persistent hyperactivity. Findings suggest that preventive interventions could target high-risk families during pregnancy and early childhood.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".