Parental affective personality and children's self‐reported internalising and externalising behaviour
Bibliographic record
Abstract
Abstract Past research has demonstrated associations between parental personality traits and children's behaviour. However, fathers have been largely excluded from this research, and mothers often rate both their own personality and their child's behaviour, contributing to shared method variance. This study contributes to the literature by examining associations between parental biologically based affective personality traits, analysed separately for mothers and fathers, and seven‐ and eight‐year‐old children's self‐reported internalising and externalising behaviours. Data were analysed for 272 mother‐child dyads and 208 father‐child dyads. A series of multiple linear regressions was utilised to test associations between mothers’ and fathers’ traits of ANGER, SADNESS, FEAR, PLAY, SEEKING and CARE, assessed using the Affective Neuroscience Personality Scales (ANPS), and children's self‐reported internalising and externalising behaviours, assessed using the Berkeley Puppet Interview. Results revealed that higher ANPS ANGER scores amongst mothers were associated with more externalising behaviours in boys. Mothers with higher scores on SEEKING had sons with lower scores on externalising behaviours, while mothers with higher PLAY scores had sons with lower scores on internalising behaviours. Fathers with higher ANPS SADNESS scores had children with greater internalising behaviours, while fathers with greater FEAR traits had children with lower internalising behaviours. Indirect associations through harsh or positive parenting were not significant. Findings demonstrated that ANPS traits of ANGER, PLAY and SEEKING for mothers and FEAR and SADNESS for fathers are associated with children's self‐reported externalising and internalising behaviours. This study adds to the literature on biologically based parental affective personality and children's internalising and externalising behaviours.
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 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 | 0.000 | 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) | 0.002 | 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".