Hyperactive Behaviors Among 17‐Month‐Olds in a Population‐Based Cohort
Bibliographic record
Abstract
ABSTRACT During a child's early years, it may be difficult to distinguish between problematic versus normative heightened motor activity. This distinction is important because disorganized heightened motor activity (often labeled hyperactive behavior) can interfere with early childhood developmental tasks. The current study used latent class analyses to estimate the proportion of 17‐month‐olds in the general population who exhibit hyperactive behaviors on a frequent basis (testing for sex differences) and to examine the extent to which toddlers with frequent hyperactive behaviors also might be frequently exhibiting other problem behaviors. We used mother‐reported cross‐sectional data on 2,045 toddlers from a provincially representative study. Results indicated that it is possible to distinguish between 17‐month‐olds who exhibit hyperactive behaviors on a frequent basis and those who never or only occasionally exhibit such behaviors. Specifically, 28.1% of toddlers exhibited hyperactive behaviors on a frequent basis. There were no significant sex differences in the probability of belonging to a particular latent class, but boys in the high‐hyperactivity latent class had a greater propensity to exhibit hyperactive behaviors on a frequent basis. While hyperactivity was highly correlated with both physical aggression and opposition‐defiance, it appears to already represent a distinct behavior problem in young children.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".