Physical Aggression and Language Ability from 17 to 72 Months: Cross-Lagged Effects in a Population Sample
Bibliographic record
Abstract
BACKGROUND: Does poor language ability in early childhood increase the likelihood of physical aggression or is language ability delayed by frequent physical aggression? This study examined the longitudinal associations between physical aggression and language ability from toddlerhood to early childhood in a population sample while controlling for parenting behaviours, non-verbal intellectual functioning, and children's sex. METHODS: Children enrolled in the Quebec Longitudinal Study of Child Development (QLSCD) (N = 2, 057) were assessed longitudinally from 17 to 72 months via parent reports and standardized assessments. RESULTS: The cross-lagged models revealed modest reciprocal associations between physical aggression and language performance from 17 to 41 months but not thereafter. CONCLUSIONS: Significant associations between physical aggression and poor language ability are minimal and limited to the period when physical aggression and language performance are both substantially increasing. During that period parenting behaviours may play an important role in supporting language ability while reducing the frequency of physical aggression. Further studies are needed that utilize multiple assessments of physical aggression, assess multiple domains of language abilities, and that examine the potential mediating role of parenting behaviours between 12 and 48 months.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".