The impact of high neuroticism in parents on children's psychosocial functioning in a population at high risk for major affective disorder: A family–environmental pathway of intergenerational risk
Bibliographic record
Abstract
Behavioral genetic studies indicate that nongenetic factors play a role in the development of bipolar and major depressive disorders. The trait of neuroticism is common among individuals with major affective disorders. We hypothesized that high neuroticism among parents affects the family environment and parenting practices and thereby increases the risk of psychosocial problems among offspring. This hypothesis is tested in a sample of participants at high and low risk for major affective disorders, which contained parents with bipolar disorder (55), major depression (21), or no mental disorder (148) and their 146 children between 4 and 14 years of age. Parents with high neuroticism scores were characterized by low psychosocial functioning, poor parenting, more dependent stressful life events, and the use of more emotion-focused and less task-oriented coping skills. High neuroticism in parents was associated with internalizing and externalizing problems among the children, as assessed by parent and teacher ratings on the Child Behavior Checklist and clinician ratings. The results suggest that high neuroticism in parents with major affective disorders is associated with inadequate parenting practices and the creation of a stressful family environment, which are subsequently related to psychosocial problems among the offspring.This work was supported by a grant to the Research Team for the Study of the Development of Affective Disorders (Drs. S. Hodgins, A. Schwartzman, L. Serbin, O. Bernazzani, C. Laroche, W. R. Beardslee, G. A. Carlson, and R. Rende) from the Fonds de la Recherche en Santé du Québec and by grants from the combined program of the Conseil Québecois de la Recherche Sociale and Fonds de la Recherche en Santé du Québec (1994–1998) as awarded to S. Hodgins. M. A. Ellenbogen is supported by a postdoctoral fellowship from the Canadian Institutes of Health Research. The authors would like to thank members of the Research Team for the Study of the Development of Affective Disorders and anonymous reviewers for their helpful comments on these findings.
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 | 0.000 | 0.002 |
| 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".