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Record W2141344740 · doi:10.1542/peds.114.1.e43

Physical Aggression During Early Childhood: Trajectories and Predictors

2004· article· en· W2141344740 on OpenAlexafffund
Richard E. Tremblay, Daniel S. Nagin, Jean R. Séguin, Mark Zoccolillo, Philip David Zelazo, Michel Boivin, Daniel Pérusse, Christa Japel

Bibliographic record

VenuePEDIATRICS · 2004
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsUniversité LavalUniversity of TorontoUniversité de MontréalMcGill UniversityResearch Unit on Children's Psychosocial Maladjustment
FundersCanadian Institutes of Health Research
KeywordsAggressionPhysical abuseMedicinePoison controlPopulationInjury preventionPsychological interventionSuicide preventionClinical psychologyDomestic violencePsychiatryPsychologyMedical emergencyEnvironmental health

Abstract

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OBJECTIVES: Physical aggression in children is a major public health problem. Not only is childhood physical aggression a precursor of the physical and mental health problems that will be visited on victims, but also aggressive children themselves are at higher risk of alcohol and drug abuse, accidents, violent crimes, depression, suicide attempts, spouse abuse, and neglectful and abusive parenting. Furthermore, violence commonly results in serious injuries to the perpetrators themselves. Although it is unusual for young children to harm seriously the targets of their physical aggression, studies of physical aggression during infancy indicate that by 17 months of age, the large majority of children are physically aggressive toward siblings, peers, and adults. This study aimed, first, to identify the trajectories of physical aggression during early childhood and, second, to identify antecedents of high levels of physical aggression early in life. Such antecedents could help to understand better the developmental origins of violence later in life and to identify targets for preventive interventions. METHODS: A random population sample of 572 families with a 5-month-old newborn was recruited. Assessments of physical aggression frequency were obtained from mothers at 17, 30, and 42 months after birth. Using a semiparametric, mixture model, distinct clusters of physical aggression trajectories were identified. Multivariate logit regression analysis was then used to identify which family and child characteristics, before 5 months of age, predict individuals on a high-level physical aggression trajectory from 17 to 42 months after birth. RESULTS: Three trajectories of physical aggression were identified. The first was composed of children who displayed little or no physical aggression. These individuals were estimated to account for approximately 28% of the sample. The largest group, estimated at approximately 58% of the sample, followed a rising trajectory of modest aggression. Finally, a group, estimated to comprise approximately 14% of the sample, followed a rising trajectory of high physical aggression. Best predictors before or at birth of the high physical aggression trajectory group, controlling for the levels of the other risk factors, were having young siblings (odds ratio [OR]: 4.00; confidence interval [CI]: 2.2-7.4), mothers with high levels of antisocial behavior before the end of high school (OR: 3.1; CI: 1.1-8.6), mothers who started having children early (OR: 3.1; CI: 1.4-6.8), families with low income (OR: 2.6; CI: 1.3-5.2), and mothers who smoked during pregnancy (OR: 2.2; CI: 1.1-4.1). Best predictors at 5 months of age were mothers' coercive parenting behavior (OR: 2.3; CI: 1.1-4.7) and family dysfunction (OR: 2.2; CI: 1.2-4.1). The OR for a high-aggression trajectory was 10.9 for children whose mother reported both high levels of antisocial behavior and early childbearing. CONCLUSIONS: Most children have initiated the use of physical aggression during infancy, and most will learn to use alternatives in the following years before they enter primary school. Humans seem to learn to regulate the use of physical aggression during the preschool years. Those who do not, seem to be at highest risk of serious violent behavior during adolescence and adulthood. Results from the present study indicate that children who are at highest risk of not learning to regulate physical aggression in early childhood have mothers with a history of antisocial behavior 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. All of these variables are relatively easy to measure during pregnancy. Preventive interventions should target families with high-risk profiles on these variables. Experiments with such programs have shown long-term impacts on child abuse and child antisocial behavior. However, these impacts were not observed in families with physical violence. The problem may be that the prevention programs that were provided did not specifically target the parents' control over their physical aggression and their skills in teaching their infant not to be physically aggressive. Most intervention programs to prevent youth physical aggression have targeted school-age children. If children normally learn not to be physically aggressive during the preschool years, then one would expect that interventions that target infants who are at high risk of chronic physical aggression would have more of an impact than interventions 5 to 10 years later, when physical aggression has become a way of life.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.007
GPT teacher head0.247
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations1,100
Published2004
Admission routes2
Has abstractyes

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