Developmental association of prosocial behaviour with aggression, anxiety and depression from infancy to preadolescence
Bibliographic record
Abstract
BACKGROUND: Research on associations between children's prosocial behaviour and mental health has provided mixed evidence. The present study sought to describe and predict the joint development of prosocial behaviour with externalizing and internalizing problems (physical aggression, anxiety and depression) from 2 to 11 years of age. METHOD: Data were drawn from the National Longitudinal Survey of Children and Youth (NLSCY). Biennial prosocial behaviour, physical aggression, anxiety and depression maternal ratings were sought for 10,700 children aged 0 to 9 years at the first assessment point. RESULTS: While a negative association was observed between prosociality and physical aggression, more complex associations emerged with internalizing problems. Being a boy decreased the likelihood of membership in the high prosocial trajectory. Maternal depression increased the likelihood of moderate aggression, but also of joint high prosociality/low aggression. Low family income predicted the joint development of high prosociality with high physical aggression and high depression. CONCLUSIONS: Individual differences exist in the association of prosocial behaviour with mental health. While high prosociality tends to co-occur with low levels of mental health problems, high prosociality and internalizing/externalizing problems can co-occur in subgroups of children. Child, mother and family characteristics are predictive of individual differences in prosocial behaviour and mental health development. Mechanisms underlying these associations warrant future investigations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".