Parent personality and children's inattention/hyperactivity problems are related via early caregiving
Bibliographic record
Abstract
Abstract Diverse mechanisms account for the familial aggregation of certain personality traits and externalizing psychopathology. We explored the roles of positive and negative parenting as mediators of longitudinal associations between parents' maladaptive personality traits and their children's inattention/hyperactivity problems. We collected self, informant and observational measures of parent personality, parenting and child symptoms from a community sample of 409 children (208 girls) and their primary caregiver across three waves of data collection at child ages 3, 5 and 8 years. Primary caregiver's negative temperament, mistrust and aggression were positively associated with children's symptoms of hyperactivity/inattention. While parenting was unrelated to self‐reported traits, responsive parenting mediated negative associations between informant‐reported maladaptive parent traits and children's hyperactivity/inattention. In addition, informant‐reported trait aggression predicted boys' ADHD symptoms via hostile parenting. Findings implicate mechanisms that may underlie intergenerational transmission and continuity of inattention/hyperactivity and highlight the importance of multi‐informant, multi‐method approaches when studying relationships between parent traits and child outcomes. Highlights Parents' trait aggression predicts boys' ADHD symptoms, partially via caregiving. Mechanisms that link parents' traits to child ADHD symptoms are trait‐specific. Informant‐, but not self‐, reported parent personality was related to parenting.
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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 | 0.005 |
| 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".