Maternal prenatal smoking, parental antisocial behavior, and early childhood physical aggression
Bibliographic record
Abstract
This study investigated joint effects of maternal prenatal smoking and parental history of antisocial behavior on physical aggression between ages 17 and 42 months in a population sample of children born in Québec (N = 1,745). An analysis of variance (ANOVA) showed significant main effects of maternal prenatal smoking and a significant interaction between maternal prenatal smoking and mother's history of antisocial behavior in the prediction of children's probability to display high and rising physical aggression. The interaction indicated that the effects of heavy smoking during pregnancy (> or =10 cigarettes/day) were greater when the mother also had a serious history of antisocial behavior. The effects remained significant after the introduction of control variables (e.g., hostile-reactive parenting, family functioning, parental separation/divorce, family income, and maternal education). Another significant interaction not accounted for by control variables was observed for maternal prenatal smoking and family income, indicating more serious effects of maternal prenatal smoking under relatively low-income, conditions. Both interactions indicate critical adversities that, in combination with maternal prenatal smoking, have supra-additive effects on (the development of) physical aggression during early childhood. These findings may have implications for the selection of intervention targets and strategies.
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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 | 0.000 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".