Moderating effect of trait emotional intelligence on the relationship between parental nurturance and prosocial behaviour
Bibliographic record
Abstract
INTRODUCTION: Research on the moderating role of trait emotional intelligence (EI) has typically examined this construct in light of other risk factors and their detrimental effects on adolescents' outcomes. This study aims to expand this line of research by focusing on the enhancing effect of trait EI and its moderating effects on the relationship between parental nurturance and adolescents' prosocial behaviour. According to such view, higher trait EI was expected to enhance the positive effect of parental nurturance on adolescents' prosocial behaviour. METHOD: A nationally representative sample of 1850 Canadian adolescents completed self-reported ratings of trait EI and parenting at 10-12 years of age which were related to prosocial behaviour at 14-16 years. RESULTS: Findings indicated that, after controlling for previous ratings of prosocial behaviour at age 10-12, child's age and sex, adolescents with high trait EI who were exposed to positive rearing experiences in early adolescence reported higher levels of prosocial behaviour two years later compared to respondents with either low or average scores on trait EI. CONCLUSION: In line with our prediction, these results confirm the enhancing the effects of trait EI on the relationship between parental nurturance and future development of prosocial behaviour. These findings have important implications in regard to the important role of trait EI in predicting variation of adolescents' prosocial behaviour in response to positive parental influences.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".