Physical aggression during early childhood: trajectories and predictors.
Bibliographic record
Abstract
INTRODUCTION: This study aimed to identify the trajectories of physical aggression during early childhood and antecedents of high levels of physical aggression early in life. METHODS: 572 families with a 5-month-old newborn were recruited. Assessments of physical aggression frequency were obtained from mothers at 17, 30, and 42 months after birth. Using a semiparametric mixture model and multivariate logit regression analyses, distinct clusters of physical aggression trajectories were identified, as well as family and child characteristics that predict high level aggression trajectories. RESULTS: THREE TRAJECTORIES OF PHYSICAL AGGRESSION WERE IDENTIFIED: 1. children (28% of sample) who displayed little or no physical aggression, 2. approximately 58% followed a rising trajectory of modest aggression, and 3. a rising trajectory of high physical aggression (14%). CONCLUSIONS: Children who are at highest risk of not learning to regulate physical aggression in early childhood have mothers with a history of antisocial behaviour during their school years, mothers who start childbearing early and who smoke during pregnancy, and parents who have low income and have serious problems living together. Preventive interventions should target families with high-risk profiles on these variables.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".